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Background Purpose Methods Results Conclusions Radiolabeling is critical in complex chemical reactions involving positron emission tomography (PET) radiotracer production. The process is now automated within a synthesis module to enhance efficiency and reduce radiation exposure. The key to this automation is the use of radiation detectors to monitor radioactivity transfer and ensure the progression of reactions. However, the high cost of these detectors has motivated the need for a more affordable alternative.This study aimed to develop a compact and cost‐efficient detector using scintillating fibers and silicon photomultipliers (SiPMs) to track radioactivity throughout PET radiotracer production.Monte Carlo simulations were performed with the Geant4‐based M‐TAG software for four detector geometries (single fiber, single fiber with bolus, 16‐fiber bundle, and spiral) to optimize the detector construction for better detection efficiency. The simulations scored the energy deposited into the scintillating fibers per simulated particle, which was used to estimate the expected voltage pulse height from the SiPM considering the total light collection efficiency. Based on the simulation results, two detector configurations (16‐fiber bundle and spiral fiber) were constructed using plastic scintillating fibers, optical fibers, a 6 mm ×$\times$ 6 mm SiPM, and commonly available electronic components. The detectors were calibrated using a Fluorine‐18 (18F$^{18}{\rm F}$) source with typical activity levels used in radiotracer production. Detector performances were subsequently evaluated through linearity tests and measurement uncertainty assessments. Errors up to ±5%$\pm 5\%$ were considered acceptable for troubleshooting purposes.The calibration curves showed a linear response with changing activity for both detectors. The calibrated detectors offered real‐time activity measurements ranging from 0.10 to 49.41 GBq, with a 3‐s refresh rate. In the activity range above 0.145 GBq, the uncertainties were less than 5%$5\%$ for both the 16‐fiber and spiral configurations. The spiral detector recorded a signal with a half‐life of 105.88±0.40$105.88 \pm 0.40$ min, closely aligning with the reference half‐life of 18F$^{18}{\rm F}$.Cost‐efficient plastic scintillation fiber detectors were developed to facilitate the troubleshooting of automated synthesis of PET radiotracers. [ABSTRACT FROM AUTHOR]

Background Purpose Methods Results Conclusions Modern PET reconstruction algorithms incorporate point‐spread‐function (PSF) correction to mitigate partial volume effect. However, PSF correction can introduce edge artifacts that lead to quantification errors. Consequently, current international guidelines advise against using PSF correction in brain PET reconstruction.We aimed to investigate PSF‐induced quantification errors in recent digital PET systems and identify conditions that mitigate them. This study utilized brain PET imaging with alginate‐based realistic phantoms, simulating lesion‐to‐background activity ratios of 10:1 and 2:1, with eleven reconstruction parameter sets.Phantoms were prepared using a commercial anthropomorphic head phantom and two homemade inserts. Each insert contained a homogeneous 18F‐FDG alginate background with hot spheres of varying diameter (3, 4, 6, 8, 10, 12, and 15 mm). PET imaging was conducted on a digital PET‐CT system Biograph Vision 600 (Siemens), with a 10 min scan duration. Imaging was performed with and without PSF correction, with 2, 4, 6, 12, 18, or 24 iterations in reconstruction, and with or without additional Gaussian postfiltering. We assessed the recovery coefficient (RC), contrast recovery coefficient (CRC), variability, and CRC‐to‐variability ratios for each sphere size and reconstruction parameter set.PSF‐corrected images of the 10:1 spheres exhibited a nonmonotonic CRC‐to‐sphere diameter relationship due to edge artifacts overshoot in the 10 mm‐diameter sphere. In contrast, PSF images of the 2:1 spheres showed a monotonically increasing relationship. Non‐PSF images of both phantoms showed an expected monotonically increasing CRC‐to‐sphere diameter relationship but with lower CRC values compared to PSF images. The nonmonotonic relationship observed with 10:1 spheres was mitigated by applying a 3‐mm FWHM Gaussian postfiltering. For both phantoms, reconstructions with 6 iterations, PSF correction, and additional 3‐mm FWHM Gaussian postfiltering demonstrated the highest CRC‐to‐variability ratios.Our findings indicate that Gaussian postfiltering suppresses PSF artifacts. This parameter set corrected the nonmonotonic CRC‐to‐sphere diameter relationship and improved the CRC‐to‐variability ratio compared to non‐PSF reconstructions. Therefore, to enhance lesion detectability without compromising quantification accuracy, PSF correction coupled with Gaussian postfiltering should be used in brain PET. [ABSTRACT FROM AUTHOR]

Background: Magnetic Resonance Imaging (MRI) and Positron Emission Tomography (PET) stand as pivotal diagnostic tools for brain disorders, offering the potential for mutually enriching disease diagnostic perspectives. However, the costs associated with PET scans and the inherent radioactivity have limited the widespread application of PET. Furthermore, it is noteworthy to highlight the promising potential of high‐field and ultra‐high‐field neuroimaging in cognitive neuroscience research and clinical practice. With the enhancement of MRI resolution, a related question arises: can high‐resolution MRI improve the quality of PET images? Purpose: This study aims to enhance the quality of synthesized PET images by leveraging the superior resolution capabilities provided by high‐field and ultra‐high‐field MRI. Methods: From a statistical perspective, the joint probability distribution is considered the most direct and fundamental approach for representing the correlation between PET and MRI. In this study, we proposed a novel model, the joint diffusion attention model, namely, the joint diffusion attention model (JDAM), which primarily focuses on learning information about the joint probability distribution. JDAM consists of two primary processes: the diffusion process and the sampling process. During the diffusion process, PET gradually transforms into a Gaussian noise distribution by adding Gaussian noise, while MRI remains fixed. The central objective of the diffusion process is to learn the gradient of the logarithm of the joint probability distribution between MRI and noise PET. The sampling process operates as a predictor‐corrector. The predictor initiates a reverse diffusion process, and the corrector applies Langevin dynamics. Results: Experimental results from the publicly available Alzheimer's Disease Neuroimaging Initiative dataset highlight the effectiveness of the proposed model compared to state‐of‐the‐art (SOTA) models such as Pix2pix and CycleGAN. Significantly, synthetic PET images guided by ultra‐high‐field MRI exhibit marked improvements in signal‐to‐noise characteristics when contrasted with those generated from high‐field MRI data. These results have been endorsed by medical experts, who consider the PET images synthesized through JDAM to possess scientific merit. This endorsement is based on their symmetrical features and precise representation of regions displaying hypometabolism, a hallmark of Alzheimer's disease. Conclusions: This study establishes the feasibility of generating PET images from MRI. Synthesis of PET by JDAM significantly enhances image quality compared to SOTA models. [ABSTRACT FROM AUTHOR]

Background: Radiomics has been used in the diagnosis of tumor lymph node metastasis (LNM). However, to date, most studies have been based on intratumoral radiomics. Few studies have focused on the use of 18F‐fluorodeoxyglucose positron emission computed tomography (18F‐FDG PET/CT) peritumoral radiomics for the diagnosis of LNM in colorectal cancer (CRC). Purpose: Determining the value of radiomics features extracted from 18F‐FDG PET/CT images of the peritumoral region in predicting LNM in patients with CRC. Methods: The clinical data and preoperative 18F‐FDG PET/CT images of 244 CRC patients were retrospectively analyzed. Intratumoral and peritumoral radiomics features were screened using the mutual information method, and least absolute shrinkage and selection operator regression. Based on the selected radiomics features, a radiomics score (Rad‐score) was calculated, and independent risk factors obtained from univariate and multivariate logistic regression analyses were used to construct clinical and combined (Radiomics + Clinical) models. The performance of these models was evaluated using the DeLong test, while their clinical utility was assessed by decision curve analysis. Finally, a nomogram was constructed to visualize the predictive model. Results: The most optimal set of features retained by the feature filtering process were all peritumoral radiomic features. Carcinoembryonic antigen levels, PET/CT‐reported lymph node status and Rad‐score were found to be independent risk factors for LNM. All three LNM risk assessment models exhibited good predictive performance, with the combined model showing the best classification results, with areas under the curve of 0.85 and 0.76 in the training and validation groups, respectively. The DeLong test revealed that the performance of the combined model was superior to that of the clinical and radiomics models in both the training and validation groups, although this difference was only statistically significant in the training group. DCA indicated that the combined model displayed better clinical utility. Conclusions: 18F‐FDG PET/CT peritumoral radiomics is uniquely suited to predict the presence of LNM in patients with CRC. In particular, the predictive efficacy of LNM for precision therapy and individualized patient management can be improved by using a combination of clinical risk factors. [ABSTRACT FROM AUTHOR]

Purpose: Positron Emission Tomography (PET) has been a commonly used imaging modality in broad clinical applications. One of the most important tradeoffs in PET imaging is between image quality and radiation dose: high image quality comes with high radiation exposure. Improving image quality is desirable for all clinical applications while minimizing radiation exposure is needed to reduce risk to patients. Methods: We introduce PET Consistency Model (PET‐CM), an efficient diffusion‐based method for generating high‐quality full‐dose PET images from low‐dose PET images. It employs a two‐step process, adding Gaussian noise to full‐dose PET images in the forward diffusion, and then denoising them using a PET Shifted‐window Vision Transformer (PET‐VIT) network in the reverse diffusion. The PET‐VIT network learns a consistency function that enables direct denoising of Gaussian noise into clean full‐dose PET images. PET‐CM achieves state‐of‐the‐art image quality while requiring significantly less computation time than other methods. Evaluation with normalized mean absolute error (NMAE), peak signal‐to‐noise ratio (PSNR), multi‐scale structure similarity index (SSIM), normalized cross‐correlation (NCC), and clinical evaluation including Human Ranking Score (HRS) and Standardized Uptake Value (SUV) Error analysis shows its superiority in synthesizing full‐dose PET images from low‐dose inputs. Results: In experiments comparing eighth‐dose to full‐dose images, PET‐CM demonstrated impressive performance with NMAE of 1.278 ± 0.122%, PSNR of 33.783 ± 0.824 dB, SSIM of 0.964 ± 0.009, NCC of 0.968 ± 0.011, HRS of 4.543, and SUV Error of 0.255 ± 0.318%, with an average generation time of 62 s per patient. This is a significant improvement compared to the state‐of‐the‐art diffusion‐based model with PET‐CM reaching this result 12× faster. Similarly, in the quarter‐dose to full‐dose image experiments, PET‐CM delivered competitive outcomes, achieving an NMAE of 0.973 ± 0.066%, PSNR of 36.172 ± 0.801 dB, SSIM of 0.984 ± 0.004, NCC of 0.990 ± 0.005, HRS of 4.428, and SUV Error of 0.151 ± 0.192% using the same generation process, which underlining its high quantitative and clinical precision in both denoising scenario. Conclusions: We propose PET‐CM, the first efficient diffusion‐model‐based method, for estimating full‐dose PET images from low‐dose images. PET‐CM provides comparable quality to the state‐of‐the‐art diffusion model with higher efficiency. By utilizing this approach, it becomes possible to maintain high‐quality PET images suitable for clinical use while mitigating the risks associated with radiation. The code is availble at https://github.com/shaoyanpan/Full‐dose‐Whole‐body‐PET‐Synthesis‐from‐Low‐dose‐PET‐Using‐Consistency‐Model. [ABSTRACT FROM AUTHOR]

Background: Biology‐guided radiotherapy (BgRT) is a novel technology that uses positron emission tomography (PET) data to direct radiotherapy delivery in real‐time. BgRT enables the precise delivery of radiation doses based on the PET signals emanating from PET‐avid tumors on the fly. In this way, BgRT uniquely utilizes radiotracer uptake as a biological beacon for controlling and adjusting dose delivery in real‐time to account for target motion. Purpose: To demonstrate using real‐time PET for BgRT delivery on the RefleXion X1 radiotherapy machine. The X1 radiotherapy machine is a rotating ring‐gantry radiotherapy system that generates a nominal 6MV photon beam, PET, and computed tomography (CT) components. The system utilizes emitted photons from PET‐avid targets to deliver effective radiation beamlets or pulses to the tumor in real‐time. Methods: This study demonstrated a real‐time PET BgRT delivery experiment under three scenarios. These scenarios included BgRT delivering to (S1) a static target in a homogeneous and heterogeneous environment, (S2) a static target with a hot avoidance structure and partial PET‐avid target, and (S3) a moving target. The first step was to create stereotactic body radiotherapy (SBRT) and BgRT plans (offline PET data supported) using RefleXion's custom‐built treatment planning system (TPS). Additionally, to create a BgRT plan using PET‐guided delivery, the targets were filled with 18F‐Fluorodeoxyglucose (FDG), which represents a tumor/target, that is, PET‐avid. The background materials were created in the insert with homogeneous water medium (for S1) and heterogeneous water with styrofoam mesh medium. A heterogeneous background medium simulated soft tissue surrounding the tumor. The treatment plan was then delivered to the experimental setups using a pre‐commercial version of the X1 machine. As a final step, the dosimetric accuracy for S1 and S2 was assessed using the ArcCheck analysis tool—the gamma criteria of 3%/3 mm. For S3, the delivery dose was quantified using EBT‐XD radiochromic film. The accuracy criteria were based on coverage, where 100% of the clinical target volume (CTV) receives at least 97% of the prescription dose, and the maximum dose in the CTV was ≤130% of the maximum planned dose (97 % ≤ CTV ≤ 130%). Results: For the S1, both SBRT and BgRT deliveries had gamma pass rates greater than 95% (SBRT range: 96.9%–100%, BgRT range: 95.2%–98.9%), while in S2, the gamma pass rate was 98% for SBRT and between 95.2% and 98.9% for BgRT plan delivering. For S3, both SBRT and BgRT motion deliveries met CTV dose coverage requirements, with BgRT plans delivering a very high dose to the target. The CTV dose ranges were (a) SBRT:100.4%–120.4%, and (b) BgRT: 121.3%–139.9%. Conclusions: This phantom‐based study demonstrated that PET signals from PET‐avid tumors can be utilized to direct real‐time dose delivery to the tumor accurately, which is comparable to the dosimetric accuracy of SBRT. Furthermore, BgRT delivered a PET‐signal controlled dose to the moving target, equivalent to the dose distribution to the static target. A future study will compare the performance of BgRT with conventional image‐guided radiotherapy. [ABSTRACT FROM AUTHOR]

Background Purpose Methods Results Conclusion Deep learning (DL) techniques have been extensively applied in medical image classification. The unique characteristics of medical imaging data present challenges, including small labeled datasets, severely imbalanced class distribution, and significant variations in imaging quality. Recently, generative adversarial network (GAN)‐based classification methods have gained attention for their ability to enhance classification accuracy by incorporating realistic GAN‐generated images as data augmentation. However, the performance of these GAN‐based methods often relies on high‐quality generated images, while large amounts of training data are required to train GAN models to achieve optimal performance.In this study, we propose an adversarial learning‐based classification framework to achieve better classification performance. Innovatively, GAN models are employed as supplementary regularization terms to support classification, aiming to address the challenges described above.The proposed classification framework, GAN‐DL, consists of a feature extraction network (F‐Net), a classifier, and two adversarial networks, specifically a reconstruction network (R‐Net) and a discriminator network (D‐Net). The F‐Net extracts features from input images, and the classifier uses these features for classification tasks. R‐Net and D‐Net have been designed following the GAN architecture. R‐Net employs the extracted feature to reconstruct the original images, while D‐Net is tasked with the discrimination between the reconstructed image and the original images. An iterative adversarial learning strategy is designed to guide model training by incorporating multiple network‐specific loss functions. These loss functions, serving as supplementary regularization, are automatically derived during the reconstruction process and require no additional data annotation.To verify the model's effectiveness, we performed experiments on two datasets, including a COVID‐19 dataset with 13 958 chest x‐ray images and an oropharyngeal squamous cell carcinoma (OPSCC) dataset with 3255 positron emission tomography images. Thirteen classic DL‐based classification methods were implemented on the same datasets for comparison. Performance metrics included precision, sensitivity, specificity, and F1$F_1$‐score. In addition, we conducted ablation studies to assess the effects of various factors on model performance, including the network depth of F‐Net, training image size, training dataset size, and loss function design. Our method achieved superior performance than all comparative methods. On the COVID‐19 dataset, our method achieved 95.4%±0.6%$95.4\%\pm 0.6\%$, 95.3%±0.9%$95.3\%\pm 0.9\%$, 97.7%±0.4%$97.7\%\pm 0.4\%$, and 95.3%±0.9%$95.3\%\pm 0.9\%$ in terms of precision, sensitivity, specificity, and F1$F_1$‐score, respectively. It achieved 96.2%±0.7%$96.2\%\pm 0.7\%$ across all these metrics on the OPSCC dataset. The study to investigate the effects of two adversarial networks highlights the crucial role of D‐Net in improving model performance. Ablation studies further provide an in‐depth understanding of our methodology.Our adversarial‐based classification framework leverages GAN‐based adversarial networks and an iterative adversarial learning strategy to harness supplementary regularization during training. This design significantly enhances classification accuracy and mitigates overfitting issues in medical image datasets. Moreover, its modular design not only demonstrates flexibility but also indicates its potential applicability to various clinical contexts and medical imaging applications. [ABSTRACT FROM AUTHOR]

Background Purpose Methods Results Conclusions Positron emission tomography (PET) has been investigated for its ability to reconstruct proton‐induced positron activity distributions in proton therapy. This technique holds potential for range verification in clinical practice. Recently, deep learning‐based dose estimation from positron activity distributions shows promise for in vivo proton dose monitoring and guided proton therapy.This study evaluates the effectiveness of three classical neural network models, recurrent neural network (RNN), U‐Net, and Transformer, for proton dose estimating. It also investigates the characteristics of these models, providing valuable insights for selecting the appropriate model in clinical practice.Proton dose calculations for spot beams were simulated using Geant4. Computed tomography (CT) images from four head cases were utilized, with three for training neural networks and the remaining one for testing. The neural networks were trained with one‐dimensional (1D) positron activity distributions as inputs and generated 1D dose distributions as outputs. The impact of the number of training samples on the networks was examined, and their dose prediction performance in both homogeneous brain and heterogeneous nasopharynx sites was evaluated. Additionally, the effect of positron activity distribution uncertainty on dose prediction performance was investigated. To quantitatively evaluate the models, mean relative error (MRE) and absolute range error (ARE) were used as evaluation metrics.The U‐Net exhibited a notable advantage in range verification with a smaller number of training samples, achieving approximately 75% of AREs below 0.5 mm using only 500 training samples. The networks performed better in the homogeneous brain site compared to the heterogeneous nasopharyngeal site. In the homogeneous brain site, all networks exhibited small AREs, with approximately 90% of the AREs below 0.5 mm. The Transformer exhibited the best overall dose distribution prediction, with approximately 92% of MREs below 3%. In the heterogeneous nasopharyngeal site, all networks demonstrated acceptable AREs, with approximately 88% of AREs below 3 mm. The Transformer maintained the best overall dose distribution prediction, with approximately 85% of MREs below 5%. The performance of all three networks in dose prediction declined as the uncertainty of positron activity distribution increased, and the Transformer consistently outperformed the other networks in all cases.Both the U‐Net and the Transformer have certain advantages in the proton dose estimation task. The U‐Net proves well suited for range verification with a small training sample size, while the Transformer outperforms others at dose‐guided proton therapy. [ABSTRACT FROM AUTHOR]

Background: Data‐driven gated (DDG) PET has gained clinical acceptance and has been shown to match or outperform external‐device gated (EDG) PET. However, in most clinical applications, DDG PET is matched with helical CT acquired in free breathing (FB) at a random respiratory phase, leaving registration, and optimal attenuation correction (AC) to chance. Furthermore, DDG PET requires additional scan time to reduce image noise as it only preserves 35%–50% of the PET data at or near the end‐expiratory phase of the breathing cycle. Purpose: A new full‐counts, phase‐matched (FCPM) DDG PET/CT was developed based on a low‐dose cine CT to improve registration between DDG PET and DDG CT, to reduce image noise, and to avoid increasing acquisition times in DDG PET. Methods: A new DDG CT was developed for three respiratory phases of CT images from a low dose cine CT acquisition of 1.35 mSv for a coverage of about 15.4 cm: end‐inspiration (EI), average (AVG), and end‐expiration (EE) to match with the three corresponding phases of DDG PET data: −10% to 15%; 15% to 30%, and 80% to 90%; and 30% to 80%, respectively. The EI and EE phases of DDG CT were selected based on the physiological changes in lung density and body outlines reflected in the dynamic cine CT images. The AVG phase was derived from averaging of all phases of the cine CT images. The cine CT was acquired over the lower lungs and/or upper abdomen for correction of misregistration between PET and FB CT as well as DDG PET and FB CT. The three phases of DDG CT were used for AC of the corresponding phases of PET. After phase‐matched AC of each PET dataset, the EI and AVG PET data were registered to the EE PET data with deformable image registration. The final result was FCPM DDG PET/CT which accounts for all PET data registered at the EE phase. We applied this approach to 14 18F‐FDG lung cancer patient studies acquired at 2 min/bed position on the GE Discovery MI (25‐cm axial FOV) and evaluated its efficacy in improved quantification and noise reduction. Results: Relative to static PET/CT, the SUVmax increases for the EI, AVG, EE, and FCPM DDG PET/CT were 1.67 ± 0.40, 1.50 ± 0.28, 1.64 ± 0.36, and 1.49 ± 0.28, respectively. There were 10.8% and 9.1% average decreases in SUVmax from EI and EE to FCPM DDG PET/CT, respectively. EI, AVG, and EE DDG PET/CT all maintained increased image noise relative to static PET/CT. However, the noise levels of FCPM and static PET were statistically equivalent, suggesting the inclusion of all counts was able to decrease the image noise relative to EI and EE DDG PET/CT. Conclusions: A new FCPM DDG PET/CT has been developed to account for 100% of collected PET data in DDG PET applications. Image noise in FCPM is comparable to static PET, while small decreases in SUVmax were also observed in FCPM when compared to either EI or EE DDG PET/CT. [ABSTRACT FROM AUTHOR]

Background: Gamma camera imaging, including single photon emission computed tomography (SPECT), is crucial for research, diagnostics, and radionuclide therapy. Gamma cameras are predominantly based on arrays of photon multipliers tubes (PMTs) that read out NaI(Tl) scintillation crystals. In this way, standard gamma cameras can localize ɣ‐rays with energies typically ranging from 30 to 360 keV. In the last decade, there has been an increasing interest towards gamma imaging outside this conventional clinical energy range, for example, for theragnostic applications and preclinical multi‐isotope positron emission tomography (PET) and PET‐SPECT. However, standard gamma cameras are typically equipped with 9.5 mm thick NaI(Tl) crystals which can result in limited sensitivity for these higher energies. Purpose: Here we investigate to what extent thicker scintillators can improve the photopeak sensitivity for higher energy isotopes while attempting to maintain spatial resolution. Methods: Using Monte Carlo simulations, we analyzed multiple PMT‐based configurations of gamma detectors with monolithic NaI (Tl) crystals of 20 and 40 mm thickness. Optimized light guide thickness together with 2‐inch round, 3‐inch round, 60 × 60 mm2 square, and 76 × 76 mm2 square PMTs were tested. For each setup, we assessed photopeak sensitivity, energy resolution, spatial, and depth‐of‐interaction (DoI) resolution for conventional (140 keV) and high (511 keV) energy ɣ using a maximum‐likelihood algorithm. These metrics were compared to those of a "standard" 9.5 mm‐thick crystal detector with 3‐inch round PMTs. Results: Estimated photopeak sensitivities for 511 keV were 27% and 53% for 20 and 40 mm thick scintillators, which is respectively, 2.2 and 4.4 times higher than for 9.5 mm thickness. In most cases, energy resolution benefits from using square PMTs instead of round ones, regardless of their size. Lateral and DoI spatial resolution are best for smaller PMTs (2‐inch round and 60 × 60 mm2 square) which outperform the more cost‐effective larger PMT setups (3‐inch round and 76 × 76 mm2 square), while PMT layout and shape have negligible (< 10%) effect on resolution. Best spatial resolution was obtained with 60 × 60 mm2 PMTs; for 140 keV, lateral resolution was 3.5 mm irrespective of scintillator thickness, improving to 2.8 and 2.9 mm for 511 keV with 20 and 40 mm thick crystals, respectively. Using the 3‐inch round PMTs, lateral resolutions of 4.5 and 3.9 mm for 140 keV and of 3.5 and 3.7 mm for 511 keV were obtained with 20 and 40 mm thick crystals respectively, indicating a moderate performance degradation compared to the 3.5 and 2.9 mm resolution obtained by the standard detector for 140 and 511 keV. Additionally, DoI resolution for 511 keV was 7.0 and 5.6 mm with 20 and 40 mm crystals using 60 × 60 mm2 square PMTs, while with 3‐inch round PMTs 12.1 and 5.9 mm were obtained. Conclusion: Depending on PMT size and shape, the use of thicker scintillator crystals can substantially improve detector sensitivity at high gamma energies, while spatial resolution is slightly improved or mildly degraded compared to standard crystals. [ABSTRACT FROM AUTHOR]

Background Purpose Methods Results Conclusions The distance traveled by the positron before annihilation with an electron, the so‐called positron range, negatively effects the positron emission tomography (PET) image quality for radionuclides emitting high‐energy positrons such as Gallium‐68 (68Ga).In this study, the effect of a tissue‐independent positron range correction for Gallium‐68 (68Ga‐PRC) was investigated based on phantom measurements. The effect of the 68Ga‐PRC was also explored in four patients.The positron range distribution profile of 68Ga in water was generated via Monte Carlo simulation. That profile was mapped to a spatially invariant 3D convolution kernel which was incorporated in the OSEM and Q.Clear reconstruction algorithms to perform the 68Ga‐PRC. In addition, each reconstruction method included point spread function (PSF) modeling and time‐of‐flight information. For both Fluorine‐18 (18F) and 68Ga, the NEMA IQ phantom was filled with a sphere‐to‐background ratio of 10:1 and scanned with the GE Discovery MI 5R PET/CT system. Standard non‐positron range correction (PRC) reconstructions were performed for both radionuclides, while also PRC reconstructions were performed for 68Ga. Reconstructions parameters (OSEM: number of updates, Q.Clear: beta value) were adapted to achieve similar noise levels between the corresponding reconstructions. The effect of 68Ga‐PRC was assessed for both OSEM and Q.Clear reconstructions and compared to non‐PRC reconstructions for 68Ga and 18F in terms of image contrast, noise, recovery coefficient (RC), and spatial resolution. For the clinical validation, 68Ga‐labeled prostate‐specific membrane antigen (68Ga‐PSMA) and 68Ga‐DOTATOC PET scans were included of two patients each. For each PET scan, patients were injected with 1.5 MBq/kg of 68Ga‐PSMA or 68Ga‐DOTATOC and the contrast‐to‐noise ratio (CNR) was calculated and compared to the non‐PRC reconstructions.For OSEM reconstructions, including the 68Ga‐PRC improved the RC by 9.4% (3.7%–19.3%) and spatial resolution by 21.7% (4.6 mm vs. 3.6 mm) for similar noise levels. For Q.Clear reconstructions, 68Ga‐PRC modeling improved the RC by 6.7% (2.8%–10.5%) and spatial resolution by 15.3% (5.9 mm vs. 5.0 mm) while obtaining similar noise levels. In the patient data, the use of 68Ga‐PRC enhanced the CNR by 13.2%.Including 68Ga‐PRC in the PET reconstruction enhanced the image quality of 68Ga PET data compared to the standard non‐PRC reconstructions for similar noise levels. Limited patient results also supported this improvement. [ABSTRACT FROM AUTHOR]

Background: Biology‐guided radiotherapy (BgRT) is a novel radiotherapy delivery technique that utilizes the tumor itself to guide dynamic delivery of treatment dose to the tumor. The RefleXion X1 system is the first radiotherapy system developed to deliver SCINTIX® BgRT. The X1 is characterized by its split arc design, employing two 90‐degree positron emission tomography (PET) arcs to guide therapeutic radiation beams in real time, currently cleared by FDA to treat bone and lung tumors. Purpose: This study aims to comprehensively evaluate the capabilities of the SCINTIX radiotherapy delivery system by evaluating its sensitivity to changes in PET contrast, its adaptability in the context of patient motion, and its performance across a spectrum of prescription doses. Methods: A series of experimental scenarios, both static and dynamic, were designed to assess the SCINTIX BgRT system's performance, including an end‐to‐end test. These experiments involved a range of factors, including changes in PET contrast, motion, and prescription doses. Measurements were performed using a custom‐made ArcCHECK insert which included a 2.2 cm spherical target and a c‐shape structure that can be filled with a PET tracer with varying concentrations. Sinusoidal and cosine4 motion patterns, simulating patient breathing, was used to test the SCINTIX system's ability to deliver BgRT during motion‐induced challenges. Each experiment was evaluated against specific metrics, including Activity Concentration (AC), Normalized Target Signal (NTS), and Biology Tracking Zone (BTZ) bounded dose‐volume histogram (bDVH) pass rates. The accuracy of the delivered BgRT doses on ArcCHECK and EBT‐XD film were evaluated using gamma 3%/2 mm and 3%/3 mm analysis. Results: In static scenarios, the X1 system consistently demonstrated precision and robustness in SCINTIX dose delivery. The end‐to‐end delivery to the spherical target yielded good results, with AC and NTS values surpassing the critical thresholds of 5 kBq/mL and 2, respectively. Furthermore, bDVH analysis consistently confirmed 100% pass rates. These results were reaffirmed in scenarios involving changes in PET contrast, emphasizing the system's ability to adapt to varying PET avidities. Gamma analysis with 3%/2 mm (10% dose threshold) criteria consistently achieved pass rates > 91.5% for the static tests. In dynamic SCINTIX delivery scenarios, the X1 system exhibited adaptability under conditions of motion. Sinusoidal and cosine4 motion patterns resulted in 3%/3 mm gamma pass rates > 87%. Moreover, the comparison with gated stereotactic body radiotherapy (SBRT) delivery on a conventional c‐arm Linac resulted in 93.9% gamma pass rates and used as comparison to evaluate the interplay effect. The 1 cm step shift tests showed low overall gamma pass rates of 60.3% in ArcCHECK measurements, while the doses in the PTV agreed with the plan with 99.9% for 3%/3 mm measured with film. Conclusions: The comprehensive evaluation of the X1 radiotherapy delivery system for SCINTIX BgRT demonstrated good agreement for the static tests. The system consistently achieved critical metrics and delivered the BgRT doses per plan. The motion tests demonstrated its ability to co‐localize the dose where the PET signal is and deliver acceptable BgRT dose distributions. [ABSTRACT FROM AUTHOR]

Background: Preclinical low‐count positron emission tomography (LC‐PET) imaging offers numerous advantages such as facilitating imaging logistics, enabling longitudinal studies of long‐ and short‐lived isotopes as well as increasing scanner throughput. However, LC‐PET is characterized by reduced photon‐count levels resulting in low signal‐to‐noise ratio (SNR), segmentation difficulties, and quantification uncertainties. Purpose: We developed and evaluated a novel deep‐learning (DL) architecture—Attention based Residual‐Dilated Net (ARD‐Net)—to generate standard‐count PET (SC‐PET) images from LC‐PET images. The performance of the ARD‐Net framework was evaluated for numerous low count realizations using fidelity‐based qualitative metrics, task‐based segmentation, and quantitative metrics. Method: Patient Derived tumor Xenograft (PDX) with tumors implanted in the mammary fat‐pad were subjected to preclinical [18F]‐Fluorodeoxyglucose (FDG)‐PET/CT imaging. SC‐PET images were derived from a 10 min static FDG‐PET acquisition, 50 min post administration of FDG, and were resampled to generate four distinct LC‐PET realizations corresponding to 10%, 5%, 1.6%, and 0.8% of SC‐PET count‐level. ARD‐Net was trained and optimized using 48 preclinical FDG‐PET datasets, while 16 datasets were utilized to assess performance. Further, the performance of ARD‐Net was benchmarked against two leading DL‐based methods (Residual UNet, RU‐Net; and Dilated Network, D‐Net) and non‐DL methods (Non‐Local Means, NLM; and Block Matching 3D Filtering, BM3D). The performance of the framework was evaluated using traditional fidelity‐based image quality metrics such as Structural Similarity Index Metric (SSIM) and Normalized Root Mean Square Error (NRMSE), as well as human observer‐based tumor segmentation performance (Dice Score and volume bias) and quantitative analysis of Standardized Uptake Value (SUV) measurements. Additionally, radiomics‐derived features were utilized as a measure of quality assurance (QA) in comparison to true SC‐PET. Finally, a performance ensemble score (EPS) was developed by integrating fidelity‐based and task‐based metrics. Concordance Correlation Coefficient (CCC) was utilized to determine concordance between measures. The non‐parametric Friedman Test with Bonferroni correction was used to compare the performance of ARD‐Net against benchmarked methods with significance at adjusted p‐value ≤0.01. Results: ARD‐Net‐generated SC‐PET images exhibited significantly better (p ≤ 0.01 post Bonferroni correction) overall image fidelity scores in terms of SSIM and NRMSE at majority of photon‐count levels compared to benchmarked DL and non‐DL methods. In terms of task‐based quantitative accuracy evaluated by SUVMean and SUVPeak, ARD‐Net exhibited less than 5% median absolute bias for SUVMean compared to true SC‐PET and lower degree of variability compared to benchmarked DL and non‐DL based methods in generating SC‐PET. Additionally, ARD‐Net‐generated SC‐PET images displayed higher degree of concordance to SC‐PET images in terms of radiomics features compared to non‐DL and other DL approaches. Finally, the ensemble score suggested that ARD‐Net exhibited significantly superior performance compared to benchmarked algorithms (p ≤ 0.01 post Bonferroni correction). Conclusion: ARD‐Net provides a robust framework to generate SC‐PET from LC‐PET images. ARD‐Net generated SC‐PET images exhibited superior performance compared other DL and non‐DL approaches in terms of image‐fidelity based metrics, task‐based segmentation metrics, and minimal bias in terms of task‐based quantification performance for preclinical PET imaging. [ABSTRACT FROM AUTHOR]

Background: High‐resolution brain positron emission tomography (PET) scanner is emerging as a significant and transformative non‐invasive neuroimaging tool to advance neuroscience research as well as improve diagnosis and treatment in neurology and psychiatry. Time‐of‐flight (TOF) and depth‐of‐interaction (DOI) information provide markedly higher PET imaging performance by increasing image signal‐to‐noise ratio and mitigating spatial resolution degradation due to parallax error, respectively. PET detector modules that utilize light sharing can inherently carry DOI information from the multiple timestamps that are generated per gamma event. The difference between two timestamps that are triggered by scintillation photons traveling in opposite directions signifies the event's depth‐dependent optical photon TOF (oTOF). However, light leak at the crystal‐readout interface substantially degrades the resolution of this oTOF‐based depth encoding. Purpose: We demonstrate the feasibility of oTOF‐based depth encoding by mitigating light leak in single‐ended‐readout Prism‐PET detector modules using tapered crystals. Minimizing light leak also improved both energy‐based DOI and coincidence timing resolutions. Methods: The tapered Prism‐PET module consists of a 16 ×$\times$ 16 array of 1.5 ×$\times$ 1.5 ×$\times$ 20 mm3${\rm {mm}}^3$ lutetium yttrium oxyorthosillicate (LYSO) crystals, which are tapered down to 1.2 ×$\times$ 1.2 mm2${\rm {mm}}^2$ at the crystal‐readout interface. The LYSO array couples 4‐to‐1 to an 8 ×$\times$ 8 array of 3 ×$\times$ 3 mm2${\rm {mm}}^2$ silicon photomultiplier (SiPM) pixels on the tapered end and to a segmented prismatoid light guide array on the opposite end. Performance of tapered and non‐tapered Prism‐PET detectors was experimentally characterized and evaluated by measuring flood histogram, energy resolution, energy‐, and oTOF‐based DOI resolutions, and coincidence timing resolution. Sensitivities of scanners using different Prism‐PET detector designs were simulated using Geant4 application for tomographic emission (GATE). Results: For the tapered (non‐tapered) Prism‐PET module, the measured full width at half maximum (FWHM) energy, timing, energy‐based DOI, and oTOF‐based DOI resolutions were 8.88 (11.18)%, 243 (286) ps, 2.35 (3.18) mm, and 5.42 (13.87) mm, respectively. The scanner sensitivities using non‐tapered and tapered crystals, and 10 rings of detector modules, were simulated to be 30.9 and 29.5 kcps/MBq, respectively. Conclusions: The tapered Prism‐PET module with minimized light leak enabled the first experimental report of oTOF‐based depth encoding at the detector module level. It also enabled the utilization of thinner (i.e., 0.1 mm) inter‐crystal spacing with barium sulfate as the reflector while also improving energy‐based DOI and timing resolutions. [ABSTRACT FROM AUTHOR]

Purpose: Assessing renal perfusion in‐vivo is challenging and quantitative information regarding renal hemodynamics is hardly incorporated in medical decision‐making while abnormal renal hemodynamics might play a crucial role in the onset and progression of renal disease. Combining physiological stimuli with rubidium‐82 positron emission tomography/computed tomography (82Rb PET/CT) offers opportunities to test the kidney perfusion under various conditions. The aim of this study is: (1) to investigate the application of a one‐tissue compartment model for measuring renal hemodynamics with dynamic 82Rb PET/CT imaging, and (2) to evaluate whether dynamic PET/CT is sensitive to detect differences in renal hemodynamics in stress conditions compared to resting state. Methods: A one‐tissue compartment model for the kidney was applied to cardiac 82Rb PET/CT scans that were obtained for ischemia detection as part of clinical care. Retrospective data, collected from 17 patients undergoing dynamic myocardial 82Rb PET/CT imaging in rest, were used to evaluate various CT‐based volumes of interest (VOIs) of the kidney. Subsequently, retrospective data, collected from 10 patients (five impaired kidney functions and five controls) undergoing dynamic myocardial 82Rb PET/CT imaging, were used to evaluate image‐derived input functions (IDIFs), PET‐based VOIs of the kidney, extraction fractions, and whether dynamic 82Rb PET/CT can measure renal hemodynamics differences using the renal blood flow (RBF) values in rest and after exposure to adenosine pharmacological stress. Results: The delivery rate (K1) values showed no significant (p = 0.14) difference between the mean standard deviation (SD) K1 values using one CT‐based VOI and the use of two, three, and four CT‐based VOIs, respectively 2.01(0.32), 1.90(0.40), 1.93(0.39), and 1.94(0.40) mL/min/mL. The ratio between RBF in rest and RBF in pharmacological stress for the controls were overall significantly lower compared to the impaired kidney function group for both PET‐based delineation methods (region growing and iso‐contouring), with the smallest median interquartile range (IQR) of 0.40(0.28–0.66) and 0.96(0.62–1.15), respectively (p < 0.05). The K1 of the impaired kidney function group were close to 1.0 mL/min/mL. Conclusions: This study demonstrated that obtaining renal K1 and RBF values using 82Rb PET/CT was feasible using a one‐tissue compartment model. Applying iso‐contouring as the PET‐based VOI of the kidney and using AA as an IDIF is suggested for consideration in further studies. Dynamic 82Rb PET/CT imaging showed significant differences in renal hemodynamics in rest compared to when exposed to adenosine. This indicates that dynamic 82Rb PET/CT has potential to detect differences in renal hemodynamics in stress conditions compared to the resting state, and might be useful as a novel diagnostic tool for assessing renal perfusion. [ABSTRACT FROM AUTHOR]

Background: Yttrium‐90 (90Y$^{90}{\rm {Y}}$) represents the primary radioisotope used in radioembolization procedures, while holmium‐166 (166Ho$^{166}{\rm {Ho}}$) is hypothesized to serve as a viable substitute for 90Y$^{90}{\rm {Y}}$ due to its comparable therapeutic potential and improved quantitative imaging. Voxel‐based dosimetry for these radioisotopes relies on activity images obtained through PET or SPECT and dosimetry methods, including the voxel S‐value (VSV) and the local deposition method (LDM). However, the evaluation of the accuracy of absorbed dose calculations has been limited by the use of non‐ideal reference standards and investigations restricted to the liver. The objective of this study was to expand upon these dosimetry characterizations by investigating the impact of image resolutions, voxel sizes, target volumes, and tissue materials on the accuracy of 90Y$^{90}{\rm {Y}}$ and 166Ho$^{166}{\rm {Ho}}$ dosimetry techniques. Methods: A specialized radiopharmaceutical dosimetry software called reDoseMC was developed using the Geant4 Monte Carlo toolkit and validated by benchmarking the generated 90Y$^{90}{\rm {Y}}$ kernels with published data. The decay spectra of both 90Y$^{90}{\rm {Y}}$ and 166Ho$^{166}{\rm {Ho}}$ were also compared. Multiple VSV kernels were generated for the liver, lungs, soft tissue, and bone for isotropic voxel sizes of 1 mm, 2 mm, and 4 mm. Three theoretical phantom setups were created with 20 or 40 mm activity and mass density inserts for the same three voxel sizes. To replicate the limited spatial resolutions present in PET and SPECT images, image resolutions were modeled using a 3D Gaussian kernel with a Full Width at Half Maximum (FWHM) ranging from 0 to 16 mm and with no added noise. The VSV and LDM dosimetry methods were evaluated by characterizing their respective kernels and analyzing their absorbed dose estimates calculated on theoretical phantoms. The ground truth for these estimations was calculated using reDoseMC. Results: The decay spectra obtained through reDoseMC showed less than a 1% difference when compared to previously published experimental data for energies below 1.9 MeV in the case of 90Y$^{90}{\rm {Y}}$ and less than 1% for energies below 1.5 MeV for 166Ho$^{166}{\rm {Ho}}$. Additionally, the validation kernels for 90Y$^{90}{\rm {Y}}$ VSV exhibited results similar to those found in published Monte Carlo codes, with source dose depositions having less than a 3% error margin. Resolution thresholds (FWHMthreshs${\rm {FWHM}}_\mathrm{thresh}{\rm {s}}$), defined as resolutions that resulted in similar dose estimates between the LDM and VSV methods, were observed for 90Y$^{90}{\rm {Y}}$. They were 1.5 mm for bone, 2.5 mm for soft tissue and liver, and 8.5 mm for lungs. For 166Ho$^{166}{\rm {Ho}}$, the accuracy of absorbed dose deposition was found to be dependent on the contributions of absorbed dose from photons. Volume errors due to variations in voxel size impacted the final dose estimates. Larger target volumes yielded more accurate mean doses than smaller volumes. For both radioisotopes, the radial dose profiles for the VSV and LDM approximated but never matched the reference standard. Conclusions: reDoseMC was developed and validated for radiopharmaceutical dosimetry. The accuracy of voxel‐based dosimetry was found to vary widely with changes in image resolutions, voxel sizes, chosen target volumes, and tissue material; hence, the standardization of dosimetry protocols was found to be of great importance for comparable dosimetry analysis. [ABSTRACT FROM AUTHOR]

Background: Cardiovascular disease is the most common cause of death worldwide, including infection and inflammation related conditions. Multiple studies have demonstrated potential advantages of hybrid positron emission tomography combined with computed tomography (PET/CT) as an adjunct to current clinical inflammatory and infectious biochemical markers. To quantitatively analyze vascular diseases at PET/CT, robust segmentation of the aorta is necessary. However, manual segmentation is extremely time‐consuming and labor‐intensive. Purpose: To investigate the feasibility and accuracy of an automated tool to segment and quantify multiple parts of the diseased aorta on unenhanced low‐dose computed tomography (LDCT) as an anatomical reference for PET‐assessed vascular disease. Methods: A software pipeline was developed including automated segmentation using a 3D U‐Net, calcium scoring, PET uptake quantification, background measurement, radiomics feature extraction, and 2D surface visualization of vessel wall calcium and tracer uptake distribution. To train the 3D U‐Net, 352 non‐contrast LDCTs from (2‐[18F]FDG and Na[18F]F) PET/CTs performed in patients with various vascular pathologies with manual segmentation of the ascending aorta, aortic arch, descending aorta, and abdominal aorta were used. The last 22 consecutive scans were used as a hold‐out internal test set. The remaining dataset was randomly split into training (n = 264; 80%) and validation (n = 66; 20%) sets. Further evaluation was performed on an external test set of 49 PET/CTs. The dice similarity coefficient (DSC) and Hausdorff distance (HD) were used to assess segmentation performance. Automatically obtained calcium scores and uptake values were compared with manual scoring obtained using clinical softwares (syngo.via and Affinity Viewer) in six patient images. intraclass correlation coefficients (ICC) were calculated to validate calcium and uptake values. Results: Fully automated segmentation of the aorta using a 3D U‐Net was feasible in LDCT obtained from PET/CT scans. The external test set yielded a DSC of 0.867 ± 0.030 and HD of 1.0 [0.6–1.4] mm, similar to an open‐source model with a DSC of 0.864 ± 0.023 and HD of 1.4 [1.0–1.8] mm. Quantification of calcium and uptake values were in excellent agreement with clinical software (ICC: 1.00 [1.00–1.00] and 0.99 [0.93–1.00] for calcium and uptake values, respectively). Conclusions: We present an automated pipeline to segment the ascending aorta, aortic arch, descending aorta, and abdominal aorta on LDCT from PET/CT and to accurately provide uptake values, calcium scores, background measurement, radiomics features, and a 2D visualization. We call this algorithm SEQUOIA (SEgmentation, QUantification, and visualizatiOn of the dIseased Aorta) and is available at https://github.com/UMCG‐CVI/SEQUOIA. This model could augment the utility of aortic evaluation at PET/CT studies tremendously, irrespective of the tracer, and potentially provide fast and reliable quantification of cardiovascular diseases in clinical practice, both for primary diagnosis and disease monitoring. [ABSTRACT FROM AUTHOR]

Background: Phantoms are commonly used to evaluate and compare the performance of imaging systems given the known ground truth. Positron emission tomography (PET) scanners are routinely validated using the NEMA image quality phantom, in which lesions are modeled using 10 to 37 mm fillable spheres. The NEMA phantom neglects, however, to model focal (3–10‐mm), high‐uptake lesions that are increasingly observed in prostate‐specific membrane antigen (PSMA) PET images. PSMA‐targeting radiopharmaceuticals allow for enhanced detection of metastatic prostate cancers. As such, there is significant need to develop an updated phantom which considers both the quantitative and lesion detectability of this new paradigm in oncological PET imaging. Purpose: In this work, we present the Quantitative PET Prostate Phantom (Q3P); a portable and modular phantom that can be used to improve and harmonize imaging protocols for 18F‐PSMA PET scans. Methods: A one‐piece cylindrical phantom was designed effectively in two halves, which we call modules. Module 1 was designed to mimic lesions in the presence of background, and Module 2 mimicked very high contrast conditions (i.e., very low background) that can be observed in 18F‐PSMA PET scans. Shell‐less radioactive spheres (3–16‐mm) were cast using epoxy resin mixed with sodium‐22 (22Na), a long half‐life positron emitter with positron range similar to 18F. To establish realistic lesion contrast, the 22Na spheres were mounted in a cylindrical chamber that can be filled with an 18F background (module 1). Thirteen exchangeable spherical cavity inserts (3–37‐mm) were machined in two parts and solvent welded together, and filled with 18F (50 kBq/mL) to model lesions with very high contrast (module 2). Five 2.5‐min PET scans were acquired on a 5‐ring GE Discovery MI PET/CT scanner (General Electric, USA). Lesions were segmented using 41% of SUVmax fixed thresholding (41% FT) and recovery coefficients (RCs) were computed from 5 noise realizations. Results: The manufactured phantom is portable (5.7 kg) and scan preparation takes less than 40 min. The total 22Na activity is 250 kBq, allowing it to be shipped as an exempt package under International Atomic Energy Agency (IAEA) regulations. Recovery coefficients, computed using PSF modeling and no post‐reconstruction smoothing, were 130.3% (16 mm), 147.1% (10 mm), 87.2% (6 mm), and 7.0% (3 mm) for RCmax, which decreased to 91.1% (16 mm), 90.6% (10 mm), 53.2% (6 mm), and 3.6% (3 mm) for RCmean in the 22Na spheres. Comparatively, 18F sphere recovery was 110.7% (17 mm), 123.6% (10 mm), 106.5% (7 mm), and 23.3% (3 mm) for RCmax, which was reduced to 76.7% (17 mm), 77.7% (10 mm), 66.8% (7 mm), and 13.5% (3 mm), for RCmean. Conclusions: A standardized imaging phantom was developed for lesion quantification assessment in 18F‐PSMA PET images. The phantom is configurable, providing users with the opportunity to modify background activity levels or sphere sizes according to clinical demands. Distributed to the community, the Q3P phantom has the potential to enable better assessment of lesion quantification and harmonization of 18F‐PSMA PET imaging, which may lead to more robust predictive metrics and better outcome prediction in metastatic prostate cancer. [ABSTRACT FROM AUTHOR]

Background: This study addresses the technical gap between clinical radiation therapy (RT) and preclinical small‐animal RT, hindering the comprehensive validation of innovative clinical RT approaches in small‐animal models of cancer and the translation of preclinical RT studies into clinical practices. Purpose: The main aim was to explore the feasibility of biologically guided RT implemented within a small‐animal radiation therapy (SART) platform, with integrated quad‐modal on‐board positron emission tomography (PET), single‐photon emission computed tomography, photon‐counting spectral CT, and cone‐beam CT (CBCT) imaging, in a Monte Carlo model as a proof‐of‐concept. Methods: We developed a SART workflow employing quad‐modal imaging guidance, integrating multimodal image‐guided RT and emission‐guided RT (EGRT). The EGRT algorithm was outlined using positron signals from a PET radiotracer, enabling near real‐time adjustments to radiation treatment beams for precise targeting in the presence of a 2‐mm setup error. Molecular image‐guided RT, incorporating a dose escalation/de‐escalation scheme, was demonstrated using a simulated phantom with a dose painting plan. The plan involved delivering a low dose to the CBCT‐delineated planning target volume (PTV) and a high dose boosted to the highly active biological target volume (hBTV) identified by the 18F‐PET image. Additionally, the Bayesian eigentissue decomposition method illustrated the quantitative decomposition of radiotherapy‐related parameters, specifically iodine uptake fraction and virtual noncontrast (VNC) electron density, using a simulated phantom with Kidney1 and Liver2 inserts mixed with an iodine contrast agent at electron fractions of 0.01–0.02. Results: EGRT simulations generated over 4,000 beamlet responses in dose slice deliveries and illustrated superior dose coverage and distribution with significantly lower doses delivered to normal tissues, even with a 2‐mm setup error introduced, demonstrating the robustness of the novel EGRT scheme compared to conventional image‐guided RT. In the dose‐painting plan, doubling the dose to the hBTV while maintaining a low dose for the PTV resulted in an organ‐at‐risk (OAR) dose comparable to the low‐dose treatment for the PTV alone. Furthermore, the decomposition of radiotherapy‐related parameters in Kidney1 and Liver2 inserts, including iodine uptake fractions and VNC electron densities, exhibited average relative errors of less than 1.0% and 2.5%, respectively. Conclusions: The results demonstrated the successful implementation of biologically guided RT within the proposed quad‐model image‐guided SART platform, with potential applications in preclinical RT and adaptive RT studies. [ABSTRACT FROM AUTHOR]

Background: Preclinical research and organ‐dedicated applications use and require high (spatial‐)resolution positron emission tomography (PET) detectors to visualize small structures (early) and understand biological processes at a finer level of detail. Researchers seeking to improve detector and image spatial resolution have explored various detector designs. Current commercial high‐resolution systems often employ finely pixelated or monolithic scintillators, each with its limitations. Purpose: We present a semi‐monolithic detector, tailored for high‐resolution PET applications with a spatial resolution in the range of 1 mm or better, merging concepts of monolithic and pixelated crystals. The detector features LYSO slabs measuring (24 × 10 × 1) mm3, coupled to a 12 × 12 readout channel photosensor with 4 mm pitch. The slabs are grouped in two arrays of 44 slabs each to achieve a higher optical photon density despite the fine segmentation. Methods: We employ a fan beam collimator for fast calibration to train machine‐learning‐based positioning models for all three dimensions, including slab identification and depth‐of‐interaction (DOI), utilizing gradient tree boosting (GTB). The data for all dimensions was acquired in less than 2 h. Energy calculation was based on a position‐dependent energy calibration. Using an analytical timing calibration, time skews were corrected for coincidence timing resolution (CTR) estimation. Results: Leveraging machine‐learning‐based calibration in all three dimensions, we achieved high detector spatial resolution: down to 1.18 mm full width at half maximum (FWHM) detector spatial resolution and 0.75 mm mean absolute error (MAE) in the planar‐monolithic direction, and 2.14 mm FWHM and 1.03 mm MAE for DOI at an energy window of (435–585) keV. Correct slab interaction identification in planar‐segmented direction exceeded 80%, alongside an energy resolution of 12.7% and a CTR of 450 ps FWHM. Conclusions: The introduced finely segmented, high‐resolution slab detector demonstrates appealing performance characteristics suitable for high‐resolution PET applications. The current benchtop‐based detector calibration routine allows these detectors to be used in PET systems. [ABSTRACT FROM AUTHOR]

Background: In spite of the tremendous potential of game‐changing biological image‐ and/or biologically guided radiation therapy (RT) and adaptive radiation therapy for cancer treatment, existing limited strategies for integrating molecular imaging and/or biological information with RT have impeded the translation of preclinical research findings to clinical applications. Additionally, there is an urgent need for a highly integrated small‐animal radiation therapy (SART) platform that can seamlessly combine therapeutic and diagnostic capabilities to comprehensively enhance RT for cancer treatment. Purpose: We investigated a highly integrated quad‐modal on‐board imaging configuration combining positron emission tomography (PET), single‐photon emission computed tomography (SPECT), photon‐counting spectral CT, and cone‐beam computed tomography (CBCT) in a SART platform using a Monte Carlo model as a proof‐of‐concept. Methods: The quad‐modal on‐board imaging configuration of the SART platform was designed and evaluated by using the GATE Monte Carlo code. A partial‐ring on‐board PET imaging subsystem, utilizing advanced semiconductor thallium bromide detector technology, was designed to achieve high sensitivity and spatial resolution. On‐board SPECT, photon‐counting spectral‐CT, and CBCT imaging were performed using a single cadmium zinc telluride flat detector panel. The absolute peak sensitivity and scatter fraction of the PET subsystem were estimated by using simulated phantoms described in the NEMA NU‐4 standard. The spatial resolution of the PET image of the platform was evaluated by imaging a simulated micro‐Derenzo hot‐rod phantom. To evaluate the quantitative imaging capability of the system's spectral CT, the Bayesian eigentissue decomposition (ETD) method was utilized to quantitatively decompose the virtual noncontrast (VNC) electron densities and iodine contrast agent fractions in the Kidney1 inserts mixed with the iodine contrast agent within the simulated phantoms. The performance of the proposed quad‐model imaging in the platform was validated by imaging a simulated phantom with multiple imaging probes, including an iodine contrast agent and radioisotopes of 18F and 99mTc. Results: The PET subsystem demonstrated an absolute peak sensitivity of 18.5% at the scanner center, with an energy window of 175–560 KeV, and a scatter fraction of only 3.5% for the mouse phantom, with a default energy window of 480–540 KeV. The spatial resolution of PET on‐board imaging exceeded 1.2 mm. All imaging probes were identified clearly within the phantom. The PET and SPECT images agreed well with the actual spatial distributions of the tracers within the phantom. Average relative errors on electron density and iodine contrast agent fraction in the Kidney1 inserts were less than 3%. High‐quality PET images, SPECT images, spectral‐CT images (including iodine contrast agent fraction images and VNC electron density images), and CBCT images of the simulated phantom demonstrated the comprehensive multimodal imaging capability of the system. Conclusions: The results demonstrated the feasibility of the proposed quad‐modal imaging configuration in a SART platform. The design incorporates anatomical, molecular, and functional information about tumors, thereby facilitating successful translation of preclinical studies into clinical practices. [ABSTRACT FROM AUTHOR]

Background: A compact PET/SPECT/CT system Inliview‐3000B has been developed to provide multi‐modality information on small animals for biomedical research. Its PET subsystem employed a dual‐layer‐offset detector design for depth‐of‐interaction capability and higher detection efficiency, but the irregular design caused some difficulties in calculating the normalization factors and the sensitivity map. Besides, the relatively larger (2 mm) crystal cross‐section size also posed a challenge to high‐resolution image reconstruction. Purpose: We present an efficient image reconstruction method to achieve high imaging performance for the PET subsystem of Inliview‐3000B. Methods: List mode reconstruction with efficient system modeling was used for the PET imaging. We adopt an on‐the‐fly multi‐ray tracing method with random crystal sampling to model the solid angle, crystal penetration and object attenuation effect, and modify the system response model during each iteration to improve the reconstruction performance and computational efficiency. We estimate crystal efficiency with a novel iterative approach that combines measured cylinder phantom data with simulated line‐of‐response (LOR)‐based factors for normalization correction before reconstruction. Since it is necessary to calculate normalization factors and the sensitivity map, we stack the two crystal layers together and extend the conventional data organization method here to index all useful LORs. Simulations and experiments were performed to demonstrate the feasibility and advantage of the proposed method. Results: Simulation results showed that the iterative algorithm for crystal efficiency estimation could achieve good accuracy. NEMA image quality phantom studies have demonstrated the superiority of random sampling, which is able to achieve good imaging performance with much less computation than traditional uniform sampling. In the spatial resolution evaluation based on the mini‐Derenzo phantom, 1.1 mm hot rods could be identified with the proposed reconstruction method. Reconstruction of double mice and a rat showed good spatial resolution and a high signal‐to‐noise ratio, and organs with higher uptake could be recognized well. Conclusion: The results validated the superiority of introducing randomness into reconstruction, and demonstrated its reliability for high‐performance imaging. The Inliview‐3000B PET subsystem with the proposed image reconstruction can provide rich and detailed information on small animals for preclinical research. [ABSTRACT FROM AUTHOR]

Background: Misregistration between CT and PET data can result in mis‐localization and inaccurate quantification of functional uptake in whole body PET/CT imaging. This problem is exacerbated when an abnormal inspiration occurs during the free‐breathing helical CT (FB CT) used for attenuation correction of PET data. In data‐driven gated (DDG) PET, the data selected for reconstruction is typically derived from the end‐expiration (EE) phase of the breathing cycle, making this potential issue worse. Purpose: The objective of this study is to develop a deformable image registration (DIR)‐based respiratory motion model to improve the registration and quantification between misregistered FB CT and PET. Methods: Twenty‐two whole‐body 18F‐FDG PET/CT scans encompassing 48 lesions in misregistered regions were analyzed in this study. End‐inspiration (EI) and EE PET data were derived from −10% to 15% and 30% to 80% of the breathing cycle, respectively. DIR was used to estimate a motion model from the EE to EI phase of the PET data. The model was then used to generate PET images at any phase of up to four times the amplitude of motion between EE and EI for correlation with the misregistered FB CT. Once a matched phase of the FB CT was determined, FB CT was deformed to a pseudo CT at the EE phase (DIR CT). DIR CT was compared with the ground truth DDG CT for AC and localization of the DDG PET. Results: Between DDG PET/FB CT and DDG PET/DIR CT, a significant increase in ∆%SUV was observed (p < 0.01), with median values elevating from 26.7% to 42.4%. This new method was most effective for lesions ≤3 cm proximal to the diaphragm (p < 0.001) but showed decreasing efficacy as the distance increased. When FB CT was severely misregistered with DDG PET (>3 cm), DDG PET/DIR CT outperformed DDG PET/FB CT alone (p < 0.05). Even when patients showed varied breathing patterns during the PET/CT scan, DDG PET/DIR CT still surpassed the efficiency of DDG PET/FB CT (p < 0.01). Though DDG PET/DIR CT couldn't match the performance of the DDG PET/CT ground truth (42.4% vs. 53.6%, p < 0.01), it reached 84% of its quantification, demonstrating good agreement and a strong overall correlation (regression coefficient of 0.94, p < 0.0001). In some cases, anatomical distortion and blurring, and misregistration error were observed in DIR CT, rendering it still unable to correct inaccurate localization near the boundaries of two organs. Conclusions: Based on the motion model derived from gated PET data, DIR CT can significantly improve the quantification and localization of DDG PET. This approach can achieve a performance level of about 84% of the ground truth established by DDG PET/CT. These results show that self‐gated PET and DIR CT may offer an alternative clinical solution to DDG PET and FB CT for quantification without the need for additional cine‐CT imaging. DIR CT was at times inferior to DDG CT due to some distortion and blurring of anatomy and misregistration error. [ABSTRACT FROM AUTHOR]

Background: Radiotherapy (RT) combined with cetuximab is the standard treatment for patients with inoperable head and neck cancers. Segmentation of head and neck (H&N) tumors is a prerequisite for radiotherapy planning but a time‐consuming process. In recent years, deep convolutional neural networks (DCNN) have become the de facto standard for automated image segmentation. However, due to the expensive computational cost associated with enlarging the field of view in DCNNs, their ability to model long‐range dependency is still limited, and this can result in sub‐optimal segmentation performance for objects with background context spanning over long distances. On the other hand, Transformer models have demonstrated excellent capabilities in capturing such long‐range information in several semantic segmentation tasks performed on medical images. Purpose: Despite the impressive representation capacity of vision transformer models, current vision transformer‐based segmentation models still suffer from inconsistent and incorrect dense predictions when fed with multi‐modal input data. We suspect that the power of their self‐attention mechanism may be limited in extracting the complementary information that exists in multi‐modal data. To this end, we propose a novel segmentation model, debuted, Cross‐modal Swin Transformer (SwinCross), with cross‐modal attention (CMA) module to incorporate cross‐modal feature extraction at multiple resolutions. Methods: We propose a novel architecture for cross‐modal 3D semantic segmentation with two main components: (1) a cross‐modal 3D Swin Transformer for integrating information from multiple modalities (PET and CT), and (2) a cross‐modal shifted window attention block for learning complementary information from the modalities. To evaluate the efficacy of our approach, we conducted experiments and ablation studies on the HECKTOR 2021 challenge dataset. We compared our method against nnU‐Net (the backbone of the top‐5 methods in HECKTOR 2021) and other state‐of‐the‐art transformer‐based models, including UNETR and Swin UNETR. The experiments employed a five‐fold cross‐validation setup using PET and CT images. Results: Empirical evidence demonstrates that our proposed method consistently outperforms the comparative techniques. This success can be attributed to the CMA module's capacity to enhance inter‐modality feature representations between PET and CT during head‐and‐neck tumor segmentation. Notably, SwinCross consistently surpasses Swin UNETR across all five folds, showcasing its proficiency in learning multi‐modal feature representations at varying resolutions through the cross‐modal attention modules. Conclusions: We introduced a cross‐modal Swin Transformer for automating the delineation of head and neck tumors in PET and CT images. Our model incorporates a cross‐modality attention module, enabling the exchange of features between modalities at multiple resolutions. The experimental results establish the superiority of our method in capturing improved inter‐modality correlations between PET and CT for head‐and‐neck tumor segmentation. Furthermore, the proposed methodology holds applicability to other semantic segmentation tasks involving different imaging modalities like SPECT/CT or PET/MRI. Code:https://github.com/yli192/SwinCross_CrossModalSwinTransformer_for_Medical_Image_Segmentation [ABSTRACT FROM AUTHOR]

Background: Simultaneous positron emission tomography (PET)/magnetic resonance imaging (MRI) scanners and inserts are valuable tools for accurate diagnosis, treatment planning, and monitoring due to their complementary information. However, the integration of a PET system into an MRI scanner presents technical challenges for a distortion‐free operation. Purpose: We aim to develop a PET insert dedicated to breast imaging in combination with the 3T PET/MRI scanner Biograph mMR (Siemens Healthineers) as well as a brain PET insert for the 7T MRI scanner MAGNETOM Terra (Siemens Healthineers). For this development, we selected as a basis the C13500 series PET modules (Hamamatsu Photonics K.K.) as they offer an all‐in‐one solution with a scalable, modular design for compact integration with state‐of‐the‐art performance. The original PET modules were not designed to be operated with an MRI scanner, therefore we implemented several modifications such as signal transmission via plastic optical fiber, radio frequency (RF) shielding of the front‐end electronics, and filter for the power supply lines. In this work, we evaluated the mutual MRI compatibility between the modified PET modules and the 3T and 7T MRI scanner. Methods: We used a proof‐of‐concept setup with two detectors to comprehensively evaluate a potential distortion of the performance of the modified PET modules whilst exposing them to a variety of MR sequences up to the peak operation conditions of the Biograph mMR. A method using the periodicity of the sequences to identify distortions of the PET events in the phase of RF pulse transmission was introduced. Vice versa, the potential distortion of the Biograph mMR was evaluated by vendor proprietary MRI compatibility test sequences. Afterwards, these studies were extended to the MAGNETOM Terra. Results: No distortions were introduced by gradient field switching (field strength up to 20 mT/m at a slew rate of 66.0 T/ms−1). However, RF pulse transmission induced a reduction of the single event rate from 33.0 kcounts/s to 32.0 kcounts/s and a degradation of the coincidence resolution time from 251 to 299 ps. Further, the proposed method revealed artifacts in the energy and timing histograms. Finally, by using the front‐end filters it was possible to prevent any RF pulse induced distortion of event rate, energy, or time stamps even for a 700° flip angle (45.5 μT) sequence. The evaluations to assess potential distortions of the MRI scanner showed that carefully designed RF shielding boxes for the PET modules were required to prevent distortion of the RF spectra. The increase in B0 field inhomogeneity of 0.254 ppm and local changes of the B1 field of 12.5% introduced by the PET modules did not qualitatively affect the MR imaging with a spin echo and MPRAGE sequence for the Biograph mMR and the MAGNETOM Terra, respectively. Conclusion: Our study demonstrates the feasibility of using a modified version of the PET modules in combination with 3T and 7T MRI scanners. Building upon the encouraging MRI compatibility results from our proof‐of‐concept detectors, we will proceed to develop PET inserts for breast and brain imaging using these modules. [ABSTRACT FROM AUTHOR]

Background: Attenuation and scatter correction is crucial for quantitative positron emission tomography (PET) imaging. Direct attenuation correction (AC) in the image domain using deep learning approaches has been recently proposed for combined PET/MR and standalone PET modalities lacking transmission scanning devices or anatomical imaging. Purpose: In this study, different input settings were considered in the model training to investigate deep learning‐based AC in the image space. Methods: Three different deep learning methods were developed for direct AC in the image space: (i) use of non‐attenuation‐corrected PET images as input (NonAC‐PET), (ii) use of attenuation‐corrected PET images with a simple two‐class AC map (composed of soft‐tissue and background air) obtained from NonAC‐PET images (PET segmentation‐based AC [SegAC‐PET]), and (iii) use of both NonAC‐PET and SegAC‐PET images in a Double‐Channel fashion to predict ground truth attenuation corrected PET images with Computed Tomography images (CTAC‐PET). Since a simple two‐class AC map (generated from NonAC‐PET images) can easily be generated, this work assessed the added value of incorporating SegAC‐PET images into direct AC in the image space. A 4‐fold cross‐validation scheme was adopted to train and evaluate the different models based using 80 brain 18F‐Fluorodeoxyglucose PET/CT images. The voxel‐wise and region‐wise accuracy of the models were examined via measuring the standardized uptake value (SUV) quantification bias in different regions of the brain. Results: The overall root mean square error (RMSE) for the Double‐Channel setting was 0.157 ± 0.08 SUV in the whole brain region, while RMSEs of 0.214 ± 0.07 and 0.189 ± 0.14 SUV were observed in NonAC‐PET and SegAC‐PET models, respectively. A mean SUV bias of 0.01 ± 0.26% was achieved by the Double‐Channel model regarding the activity concentration in cerebellum region, as opposed to 0.08 ± 0.28% and 0.05 ± 0.28% SUV biases for the network that uniquely used NonAC‐PET or SegAC‐PET as input, respectively. SegAC‐PET images with an SUV bias of ‐1.15 ± 0.54%, served as a benchmark for clinically accepted errors. In general, the Double‐Channel network, relying on both SegAC‐PET and NonAC‐PET images, outperformed the other AC models. Conclusion: Since the generation of two‐class AC maps from non‐AC PET images is straightforward, the current study investigated the potential added value of incorporating SegAC‐PET images into a deep learning‐based direct AC approach. Altogether, compared with models that use only NonAC‐PET and SegAC‐PET images, the Double‐Channel deep learning network exhibited superior attenuation correction accuracy. [ABSTRACT FROM AUTHOR]

Background: Computed tomography (CT)‐based positron emission tomography (PET) attenuation correction (AC) is a commonly used method in PET AC. However, the CT truncation caused by the subject's limbs outside the CT field‐of‐view (FOV) leads to errors in PET AC. Purpose: In order to enhance the quantitative accuracy of PET imaging in the all‐digital DigitMI 930 PET/CT system, we assessed the impact of FOV truncation on its image quality and investigated the effectiveness of geometric shape‐based FOV extension algorithms in this system. Methods: We implemented two geometric shape‐based FOV extension algorithms. By setting the data from different numbers of detector channels on either side of the sinogram to zero, we simulated various levels of truncation. Specific regions of interest (ROI) were selected, and the mean values of these ROIs were calculated to visually compare the differences between truncated CT, CT extended using the FOV extension algorithms, and the original CT. Furthermore, we conducted statistical analyses on the mean and standard deviation of residual maps between truncated/extended CT and the original CT at different levels of truncation. Subsequently, similar data processing was applied to PET images corrected using original CT and those corrected using simulated truncated and extended CT images. This allowed us to evaluate the influence of FOV truncation on the images produced by the DigitMI 930 PET/CT system and assess the effectiveness of the FOV extension algorithms. Results: Truncation caused bright artifacts at the CT FOV edge and a slight increase in pixel values within the FOV. When using truncated CT data for PET AC, the PET activity outside the CT FOV decreased, while the extension algorithm effectively reduced these effects. Patient data showed that the activity within the CT FOV decreased by 60% in the truncated image compared to the base image, but this number could be reduced to at least 17.3% after extension. Conclusion: The two geometric shape‐based algorithms effectively eliminate CT truncation artifacts and restore the true distribution of CT shape and PET emission data outside the FOV in the all‐digital DigitMI 930 PET/CT system. These two algorithms can be used as basic solutions for CT FOV extension in all‐digital PET/CT systems. [ABSTRACT FROM AUTHOR]

Background: A CZT (cadmium zinc telluride) PET (positron emission tomography) system is being developed at Stanford University. CZT has the promise of outperforming scintillator‐based systems in energy and spatial resolution but has relatively poor coincidence timing resolution. Purpose: To supplement GATE (GEANT 4 Application for Emission Tomography) simulations with charge transport and electronics modeling for a high‐resolution CZT PET system. Methods: A conventional GATE simulation was supplemented with electron‐hole transport modeling and experimentally measured single detector energy resolution to improve the system‐level understanding of a CZT high‐resolution PET system in development at Stanford University. The modeling used GATE hits data and applied charge transport in the crystal and RC‐CR processing of the simulated signals to model the electronics, including leading‐edge discriminators and peak pick‐off. Depth correction was also performed on the simulation data. Experimentally acquired data were used to determine energy resolution parameters and were compared to simulation data. Results: The distributions of the coincidence timing, anode energy, and cathode energy are consistent with experimental data. Numerically, the simulation achieved 153 ns FWHM coincidence time resolution (CTR), which is of the same order of magnitude as the raw 210 ns CTR previously found experimentally. Further, the anode energy resolution was found to be 5.9% FWHM (full width at half maximum) at 511 keV in the simulation, which is between the experimental value found for a single crystal of 3% and the value found for the dual‐panel setup of 8.02%, after depth correction. Conclusions: Developing this advanced simulation improves upon the limitations of GATE for modeling semiconductor PET systems and provides a means for deeper analysis of the coincidence timing resolution and other complementary electron‐hole dependent system parameters. [ABSTRACT FROM AUTHOR]

Background: Prostate‐specific membrane antigen (PSMA) PET imaging represents a valuable source of information reflecting disease stage, response rate, and treatment optimization options, particularly with PSMA radioligand therapy. Quantification of radiopharmaceutical uptake in healthy organs from PSMA images has the potential to minimize toxicity by extrapolation of the radiation dose delivery towards personalization of therapy. However, segmentation and quantification of uptake in organs requires labor‐intensive organ delineations that are often not feasible in the clinic nor scalable for large clinical trials. Purpose: In this work we develop and test the PSMA Healthy organ segmentation network (PSMA‐Hornet), a fully‐automated deep neural net for simultaneous segmentation of 14 healthy organs representing the normal biodistribution of [18F]DCFPyL on PET/CT images. We also propose a modified U‐net architecture, a self‐supervised pre‐training method for PET/CT images, a multi‐target Dice loss, and multi‐target batch balancing to effectively train PSMA‐Hornet and similar networks. Methods: The study used manually‐segmented [18F]DCFPyL PET/CT images from 100 subjects, and 526 similar images without segmentations. The unsegmented images were used for self‐supervised model pretraining. For supervised training, Monte‐Carlo cross‐validation was used to evaluate the network performance, with 85 subjects in each trial reserved for model training, 5 for validation, and 10 for testing. Image segmentation and quantification metrics were evaluated on the test folds with respect to manual segmentations by a nuclear medicine physician, and compared to inter‐rater agreement. The model's segmentation performance was also evaluated on a separate set of 19 images with high tumor load. Results: With our best model, the lowest mean Dice coefficient on the test set was 0.826 for the sublingual gland, and the highest was 0.964 for liver. The highest mean error in tracer uptake quantification was 13.9% in the sublingual gland. Self‐supervised pretraining improved training convergence, train‐to‐test generalization, and segmentation quality. In addition, we found that a multi‐target network produced significantly higher segmentation accuracy than single‐organ networks. Conclusions: The developed network can be used to automatically obtain high‐quality organ segmentations for PSMA image analysis tasks. It can be used to reproducibly extract imaging data, and holds promise for clinical applications such as personalized radiation dosimetry and improved radioligand therapy. [ABSTRACT FROM AUTHOR]

Background: The use of computed tomography (CT) for attenuation correction (AC) in whole‐body PET/CT can result in a significant contribution to radiation exposure. This can become a limiting factor for reducing considerably the overall radiation exposure of the patient when using the new long axial field of view (LAFOV) PET scanners. However, recent CT technology have introduced features such as the tin (Sn) filter, which can substantially reduce the CT radiation dose. Purpose: The purpose of this study was to investigate the ultra‐low dose CT for attenuation correction using the Sn filter together with other dose reduction options such as tube current (mAs) reduction. We explore the impact of dose reduction in the context of AC‐CT and how it affects PET image quality. Methods: The study evaluated a range of ultra‐low dose CT protocols using five physical phantoms that represented a broad collection of tissue electron densities. A long axial field of view (LAFOV) PET/CT scanner was used to scan all phantoms, applying various CT dose reduction parameters such as reducing tube current (mAs), increasing the pitch value, and applying the Sn filter. The effective dose resulting from the CT scans was determined using the CTDIVol reported by the scanner. Several voxel‐based and volumes of interest (VOI)‐based comparisons were performed to compare the ultra‐low dose CT images, the generated attenuation maps, and corresponding PET images against those images acquired with the standard low dose CT protocol. Finally, two patient datasets were acquired using one of the suggested ultra‐low dose CT settings. Results: By incorporating the Sn filter and adjusting mAs to the lowest available value, the radiation dose in CT images of PBU‐60 phantom was significantly reduced; resulting in an effective dose of nearly 2% compared to the routine low dose CT protocols currently in clinical use. The assessment of PET images using VOI and voxel‐based comparisons indicated relative differences (RD%) of under 6% for mean activity concentration (AC) in the torso phantom and patient dataset and under 8% for a source point in the CIRS phantom. The maximum RD% value of AC was 14% for the point source in the CIRS phantom. Increasing the tube current from 6 mAs to 30 mAs in patients with high BMI, or with arms down, can suppress the photon starvation artifact, whilst still preserving a dose reduction of 90%. Conclusions: Introducing a Sn filter in CT imaging lowers radiation dose by more than 90%. This reduction has minimal effect on PET image quantification at least for patients without Body Mass Index (BMI) higher than 30. Notably, this study results need validation using a larger clinical PET/CT dataset in the future, including patients with higher BMI. [ABSTRACT FROM AUTHOR]

Background: The prostate‐specific membrane antigen (PSMA) targeted positron‐emitting tomography (PET) tracers are increasingly used in clinical practice, with novel tracers constantly being developed. Recently, 18F‐PSMA‐11 has been gaining growing interest for several merits; however, direct in vivo visualization of its kinetic features in humans remains lacking. Purpose: To visualize the kinetic features of 18F‐PSMA‐11 in healthy subjects and patients with prostate cancer derived from the total‐body dynamic PET scans. Methods: A total of 8 healthy volunteers (7 males; 1 female) and 3 patients with prostate cancer underwent total‐body PET/CT imaging at 1 and 2 h post injection (p.i.) of 18F‐PSMA‐11, of which 7 healthy subjects and 3 patients underwent total‐body dynamic PET scans lasting 30 min. Reversible two‐tissue compartments (2TC) and Patlak models were fitted based on the voxel‐based time activity curves (TACs), with the parametric images generated subsequently. Additionally, semi‐automated segmentation of multiple organs was performed in the dynamic images to measure the SUVmean at different time points and in the parametric images to estimate the mean value of the kinetic parameters of these organs. Results: 18F‐PSMA‐11 showed quick accumulation within prostate cancer, as early as 45 s after tracer injection. It was rapidly cleared from blood circulation and predominantly excreted through the urinary system. High and rapid radiotracer accumulation was observed in the liver, spleen, lacrimal glands, and salivary glands, whereas gradual accumulation was observed in the skeleton. Prostate cancer tissue is visualized in all parametric images, and best seen in DV and Patlak Ki images. Patlak Ki showed a good correlation with 2TC Ki values (r = 0.858, p < 0.05) but less noise than 2TC images. A scanning time point of 30–35 min p.i. was then suggested for satisfactory tumor to background ratio. Conclusion: Prostate cancer tissue is visible in most parametric images, and is better shown by Patlak Ki and 2TC DV images. Patlak Ki is consistent with, and thus is preferred over, 2TC Ki images for substantially quicker calculation. Based on the dynamic imaging analysis, a shorter uptake time (30–35 min) might be preferred for a better balance of tumor to background ratio. [ABSTRACT FROM AUTHOR]

Background: Attenuation correction (AC) is an important methodical step in positron emission tomography/magnetic resonance imaging (PET/MRI) to correct for attenuated and scattered PET photons. Purpose: The overall quality of magnetic resonance (MR)‐based AC in whole‐body PET/MRI was evaluated in direct comparison to computed tomography (CT)‐based AC serving as reference. The quantitative impact of isolated tissue classes in the MR‐AC was systematically investigated to identify potential optimization needs and strategies. Methods: Data of n = 60 whole‐body PET/CT patients with normal lung tissue and without metal implants/prostheses were used to generate six different AC‐models based on the CT data for each patient, simulating variations of MR‐AC. The original continuous CT‐AC (CT‐org) is referred to as reference. A pseudo MR‐AC (CT‐mrac), generated from CT data, with four tissue classes and a bone atlas represents the MR‐AC. Relative difference in linear attenuation coefficients (LAC) and standardized uptake values were calculated. From the results two improvements regarding soft tissue AC and lung AC were proposed and evaluated. Results: The overall performance of MR‐AC is in good agreement compared to CT‐AC. Lungs, heart, and bone tissue were identified as the regions with most deviation to the CT‐AC (myocardium −15%, bone tissue −14%, and lungs ±20%). Using single‐valued LACs for AC in the lung only provides limited accuracy. For improved soft tissue AC, splitting the combined soft tissue class into muscles and organs each with adapted LAC could reduce the deviations to the CT‐AC to < ±1%. For improved lung AC, applying a gradient LAC in the lungs could remarkably reduce over‐ or undercorrections in PET signal compared to CT‐AC (±5%). Conclusions: The AC is important to ensure best PET image quality and accurate PET quantification for diagnostics and radiotherapy planning. The optimized segment‐based AC proposed in this study, which was evaluated on PET/CT data, inherently reduces quantification bias in normal lung tissue and soft tissue compared to the CT‐AC reference. [ABSTRACT FROM AUTHOR]

Background: Low‐dose positron emission tomography (LD‐PET) imaging is commonly employed in preclinical research to minimize radiation exposure to animal subjects. However, LD‐PET images often exhibit poor quality and high noise levels due to the low signal‐to‐noise ratio. Deep learning (DL) techniques such as generative adversarial networks (GANs) and convolutional neural network (CNN) have the capability to enhance the quality of images derived from noisy or low‐quality PET data, which encodes critical information about radioactivity distribution in the body. Purpose: Our objective was to optimize the image quality and reduce noise in preclinical PET images by utilizing the sinogram domain as input for DL models, resulting in improved image quality as compared to LD‐PET images. Methods: A GAN and CNN model were utilized to predict high‐dose (HD) preclinical PET sinograms from the corresponding LD preclinical PET sinograms. In order to generate the datasets, experiments were conducted on micro‐phantoms, animal subjects (rats), and virtual simulations. The quality of DL‐generated images was weighted by performing the following quantitative measures: structural similarity index measure (SSIM), root mean squared error (RMSE), peak signal‐to‐noise ratio (PSNR), signal‐to‐noise ratio (SNR), and contrast‐to‐noise ratio (CNR). Additionally, DL input and output were both subjected to a spatial resolution calculation of full width half maximum (FWHM) and full width tenth maximum (FWTM). DL outcomes were then compared with the conventional denoising algorithms such as non‐local means (NLM), block‐matching, and 3D filtering (BM3D). Results: The DL models effectively learned image features and produced high‐quality images, as reflected in the quantitative metrics. Notably, the FWHM and FWTM values of DL PET images exhibited significantly improved accuracy compared to LD, NLM, and BM3D PET images, and just as precise as HD PET images. The MSE loss underscored the excellent performance of the models, indicating that the models performed well. To further improve the training, the generator loss (G loss) was increased to a value higher than the discriminator loss (D loss), thereby achieving convergence in the GAN model. Conclusions: The sinograms generated by the GAN network closely resembled real HD preclinical PET sinograms and were more realistic than LD. There was a noticeable improvement in image quality and noise factor in the predicted HD images. Importantly, DL networks did not fully compromise the spatial resolution of the images. [ABSTRACT FROM AUTHOR]

Background: Scatter correction (SC) is essential in PET for accurate quantitative imaging. The state‐of‐the‐art SC method is single‐scatter simulation (SSS). Although this method is usually robust and accurate, it can fail in some situations, for example when there is motion between the CT and PET scans in PET/CT. Therefore, it is of interest to consider other SC methods. Purpose: In this work, an energy‐based scatter estimation (EBS) method is described in detail, tested in phantoms and patients, and compared to SSS. Methods: This version of EBS was developed for list‐mode data from Biograph Vision‐600 PET/CT scanner. EBS is based on digitized 2D energy histograms in each bin of a coarsely sampled PET sinogram, either with or without time of flight (TOF). The histograms are modeled as a noisy realization of a linear combination of nine basis functions whose parameters were derived from a measurement of the 511‐keV photopeak spectrum as well as Monte‐Carlo simulations of the scattering process. EBS uses an iterative expectation maximization approach to determine the coefficients in the linear combination, and from this estimates the scatter. The investigation was restricted to 18F‐based PET data in which the acquired number of counts was similar to the levels seen in oncological whole‐body PET/CT scans. To evaluate the performance, phantom scans were used that involved the NEMA NU2‐2018 protocol, a slab phantom, an NU 2‐1994 phantom, a cardiac phantom in an anthropomorphic chest phantom, and a uniformly‐filled torso phantom with a bladder phantom slightly outside the axial field of view. Contrast recovery (CR) and other parameters were evaluated in images reconstructed with SSS and EBS. Furthermore, FDG PET scans of seven lung cancer patients were used in the evaluation. Standardized uptake values (SUV) based on SSS and EBS were compared in 27 lesions. Results: EBS and SSS images were visually similar in all cases except the torso + bladder phantom, where the EBS was much closer to the expected uniform image. The NU2‐2018 analysis indicated a 2% scatter residual in EBS images compared to 3% with SSS, and 10% higher background variability, which is a surrogate for image noise. The cardiac phantom scan showed that CR was 98.2% with EBS and 99.6% with SSS, and that the SSS sinogram had values greater than the net‐true emission sinogram, indicating a slight overcorrection in the case of SSS. In the lesion SUV comparison in patient scans, EBS correlated strongly (R2 = 0.9973) with SSS, and SUV based on EBS were systematically 0.1 SUV lower. In the case of the torso + bladder phantom portion, the SSS image of the torso + bladder phantom was 299% times hotter than expected in one area, due to scatter estimation error, compared to 16% colder with EBS. Conclusions: In evaluating clinically relevant parameters such as SUV in focal lesions, EBS and SSS give almost the same results. In phantoms, some scatter figures of merit were slightly improved by use of EBS, though an image variability figure of merit was slightly degraded. In typical oncological whole‐body PET/CT, EBS may be a suitable replacement for SSS, especially when SSS fails due to technical problems during the scan. [ABSTRACT FROM AUTHOR]

Background: Radiopharmaceutical therapy (RPT) is an increasingly adopted modality for treating cancer. There is evidence that the optimization of the treatment based on dosimetry can improve outcomes. However, standardization of the clinical dosimetry workflow still represents a major effort. Among the many sources of variability, the impact of using different Dose Voxel Kernels (DVKs) to generate absorbed dose (AD) maps by convolution with the time‐integrated activity (TIA) distribution has not been systematically investigated. Purpose: This study aims to compare DVKs and assess the differences in the ADs when convolving the same TIA map with different DVKs. Methods: DVKs of 3 × 3 × 3 mm3 sampling—nine for 177Lu, nine for 90Y—were selected from those most used in commercial/free software or presented in prior publications. For each voxel within a 11 × 11 × 11 matrix, the coefficient of variation (CoV) and the percentage difference between maximum and minimum values (% maximum difference) were calculated. The total absorbed dose per decay (SUM), calculated as the sum of all the voxel values in each kernel, was also compared. Publicly available quantitative SPECT images for two patients treated with 177Lu‐DOTATATE and PET images for two patients treated with 90Y‐microspheres were used, including organs at risk (177Lu: kidneys; 90Y: liver and healthy liver) and tumors' segmentations. For each patient, the mean AD to the volumes of interest (VOIs) was calculated using the different DVKs, the same TIA map and the same software tool for dose convolution, thereby focusing on the DVK impact. For each VOI, the % maximum difference of the mean AD between maximum and minimum values was computed. Results: The CoV (% maximum difference) in voxels of normalized coordinates [0,0,0], [0,1,0], and [0,1,1] were 5%(21%), 9%(35%), and 10%(46%) for the 177Lu DVKs. For the case of 90Y, these values were 2%(9%), 4%(14%), and 4%(16%). The CoV (% maximum difference) for SUM was 9%(33%) for 177Lu, and 4%(15%) for 90Y. The variability of the mean tumor and organ AD was up to 19% and 15% in 177Lu‐DOTATATE and 90Y‐microspheres patients, respectively. Conclusions: This study showed a considerable AD variability due exclusively to the use of different DVKs. A concerted effort by the scientific community would contribute to decrease these discrepancies, strengthening the consistency of AD calculation in RPT. [ABSTRACT FROM AUTHOR]

Background: PET/CT images combining anatomic and metabolic data provide complementary information that can improve clinical task performance. PET image segmentation algorithms exploiting the multi‐modal information available are still lacking. Purpose: Our study aimed to assess the performance of PET and CT image fusion for gross tumor volume (GTV) segmentations of head and neck cancers (HNCs) utilizing conventional, deep learning (DL), and output‐level voting‐based fusions. Methods: The current study is based on a total of 328 histologically confirmed HNCs from six different centers. The images were automatically cropped to a 200 × 200 head and neck region box, and CT and PET images were normalized for further processing. Eighteen conventional image‐level fusions were implemented. In addition, a modified U2‐Net architecture as DL fusion model baseline was used. Three different input, layer, and decision‐level information fusions were used. Simultaneous truth and performance level estimation (STAPLE) and majority voting to merge different segmentation outputs (from PET and image‐level and network‐level fusions), that is, output‐level information fusion (voting‐based fusions) were employed. Different networks were trained in a 2D manner with a batch size of 64. Twenty percent of the dataset with stratification concerning the centers (20% in each center) were used for final result reporting. Different standard segmentation metrics and conventional PET metrics, such as SUV, were calculated. Results: In single modalities, PET had a reasonable performance with a Dice score of 0.77 ± 0.09, while CT did not perform acceptably and reached a Dice score of only 0.38 ± 0.22. Conventional fusion algorithms obtained a Dice score range of [0.76–0.81] with guided‐filter‐based context enhancement (GFCE) at the low‐end, and anisotropic diffusion and Karhunen–Loeve transform fusion (ADF), multi‐resolution singular value decomposition (MSVD), and multi‐level image decomposition based on latent low‐rank representation (MDLatLRR) at the high‐end. All DL fusion models achieved Dice scores of 0.80. Output‐level voting‐based models outperformed all other models, achieving superior results with a Dice score of 0.84 for Majority_ImgFus, Majority_All, and Majority_Fast. A mean error of almost zero was achieved for all fusions using SUVpeak, SUVmean and SUVmedian. Conclusion: PET/CT information fusion adds significant value to segmentation tasks, considerably outperforming PET‐only and CT‐only methods. In addition, both conventional image‐level and DL fusions achieve competitive results. Meanwhile, output‐level voting‐based fusion using majority voting of several algorithms results in statistically significant improvements in the segmentation of HNC. [ABSTRACT FROM AUTHOR]

Background: Cardiac radioablation (CR) is an innovative treatment to ablate cardiac arrythmia sources by radiation therapy. CR target delineation is a challenging task requiring the exploitation of highly different imaging modalities, including cardiac electro‐anatomical mapping (EAM). Purpose: In this work, a data integration process is proposed to alleviate the tediousness of CR target delineation by generating a fused representation of the heart, including all the information of interest resulting from the analysis and registration of electro‐anatomical data, PET scan and planning computed tomography (CT) scan. The proposed process was evaluated by cardiologists during delineation trials. Methods: The data processing pipeline was composed of the following steps. The cardiac structures of interest were segmented from cardiac CT scans using a deep learning method. The EAM data was registered to the cardiac CT scan using a point cloud based registration method. The PET scan was registered using rigid image registration. The EAM and PET information, as well as the myocardium thickness, were projected on the surface of the 3D mesh of the left ventricle. The target was identified by delineating a path on this surface that was further projected to the thickness of the myocardium to create the target volume. This process was evaluated by comparison with a standard slice‐by‐slice delineation with mental EAM registration. Four cardiologists delineated targets for three patients using both methods. The variability of target volumes, and the ease of use of the proposed method, were evaluated. Results: All cardiologists reported being more confident and efficient using the proposed method. The inter‐clinician variability in delineated target volume was systematically lower with the proposed method (average dice score of 0.62 vs. 0.32 with a classical method). Delineation times were also improved. Conclusions: A data integration process was proposed and evaluated to fuse images of interest for CR target delineation. It effectively reduces the tediousness of CR target delineation, while improving inter‐clinician agreement on target volumes. This study is still to be confirmed by including more clinicians and patient data to the experiments. [ABSTRACT FROM AUTHOR]

Background: Deep learning (DL) has been widely used for diagnosis and prognosis prediction of numerous frequently occurring diseases. Generally, DL models require large datasets to perform accurate and reliable prognosis prediction and avoid overlearning. However, prognosis prediction of rare diseases is still limited owing to the small number of cases, resulting in small datasets. Purpose: This paper proposes a multimodal DL method to predict the prognosis of patients with malignant pleural mesothelioma (MPM) with a small number of 3D positron emission tomography–computed tomography (PET/CT) images and clinical data. Methods: A 3D convolutional conditional variational autoencoder (3D‐CCVAE), which adds a 3D‐convolutional layer and conditional VAE to process 3D images, was used for dimensionality reduction of PET images. We developed a two‐step model that performs dimensionality reduction using the 3D‐CCVAE, which is resistant to overlearning. In the first step, clinical data were input to condition the model and perform dimensionality reduction of PET images, resulting in more efficient dimension reduction. In the second step, a subset of the dimensionally reduced features and clinical data were combined to predict 1‐year survival of patients using the random forest classifier. To demonstrate the usefulness of the 3D‐CCVAE, we created a model without the conditional mechanism (3D‐CVAE), one without the variational mechanism (3D‐CCAE), and one without an autoencoder (without AE), and compared their prediction results. We used PET images and clinical data of 520 patients with histologically proven MPM. The data were randomly split in a 2:1 ratio (train : test) and three‐fold cross‐validation was performed. The models were trained on the training set and evaluated based on the test set results. The area under the receiver operating characteristic curve (AUC) for all models was calculated using their 1‐year survival predictions, and the results were compared. Results: We obtained AUC values of 0.76 (95% confidence interval [CI], 0.72–0.80) for the 3D‐CCVAE model, 0.72 (95% CI, 0.68–0.77) for the 3D‐CVAE model, 0.70 (95% CI, 0.66–0.75) for the 3D‐CCAE model, and 0.69 (95% CI 0.65–0.74) for the without AE model. The 3D‐CCVAE model performed better than the other models (3D‐CVAE, p = 0.039; 3D‐CCAE, p = 0.0032; and without AE, p = 0.0011). Conclusions: This study demonstrates the usefulness of the 3D‐CCVAE in multimodal DL models learned using a small number of datasets. Additionally, it shows that dimensionality reduction via AE can be used to learn a DL model without increasing the overlearning risk. Moreover, the VAE mechanism can overcome the uncertainty of the model parameters that commonly occurs for small datasets, thereby eliminating the risk of overlearning. Additionally, more efficient dimensionality reduction of PET images can be performed by providing clinical data as conditions and ignoring clinical data‐related features. [ABSTRACT FROM AUTHOR]

Background: Small bowel carcinoid tumor is a rare neoplasm and increasing in incidence. Patients with small bowel carcinoid tumors often experience long delays in diagnosis due to the vague symptoms, slow growth of tumors, and lack of clinician awareness. Computed tomography (CT) is the most common imaging study for diagnosis of small bowel carcinoid tumor. It is often used with positron emission tomography (PET) to capture anatomical and functional aspects of carcinoid tumors and thus to increase the sensitivity. Purpose: We compared three different kinds of methods for the automatic detection of small bowel carcinoid tumors on CT scans, which is the first to the best of our knowledge. Methods: Thirty‐three preoperative CT scans of 33 unique patients with surgically‐proven carcinoid tumors within the small bowel were collected. Ground‐truth segmentation of tumors was drawn on CT scans by referring to available 18F‐DOPA PET scans and the corresponding radiology report. These scans were split into the trainval set (n = 24) and the test positive set (n= 9). Additionally, 22 CT scans of 22 unique patients who had no evidence of the tumor were collected to comprise the test negative set. We compared three different kinds of detection methods, which are detection network, patch‐based classification, and segmentation‐based methods. We also investigated the usefulness of small bowel segmentation for reduction of false positives (FPs) for each method. Free‐response receiver operating characteristic (FROC) curves and receiver operating characteristic (ROC) curves were used for lesion‐ and patient‐level evaluations, respectively. Statistical analyses comparing the FROC and ROC curves were also performed. Results: The detection network method performed the best among the compared methods. For lesion‐level detection, the detection network method, without the small bowel segmentation‐based filtering, achieved sensitivity values of (60.8%, 81.1%, 82.4%, 86.5%) at per‐scan FP rates of (1, 2, 4 ,8), respectively. The use of the small bowel segmentation did not improve the performance (p=0.742$p=0.742$). For patient‐level detection, again the detection network method, but with the small bowel segmentation‐based filtering, achieved the highest AUC of 0.86 with a sensitivity of 78% and specificity of 82% at the Youden point. Conclusions: The carcinoid tumors in this patient population were very small and potentially difficult to diagnose. The presented method showed reasonable sensitivity at small numbers of FPs for lesion‐level detection. It also achieved a promising AUC for patient‐level detection. The method may have clinical application in patients with this rare and difficult to detect disease. [ABSTRACT FROM AUTHOR]

Background: Standardized patient‐specific pretreatment dosimetry planning is mandatory in the modern era of nuclear molecular radiotherapy, which may eventually lead to improvements in the final therapeutic outcome. Only a comprehensive definition of a dosage therapeutic window encompassing the range of absorbed doses, that is, helpful without being detrimental can lead to therapy individualization and improved outcomes. As a result, setting absorbed dose safety limits for organs at risk (OARs) requires knowledge of the absorbed dose–effect relationship. Data sets of consistent and reliable inter‐center dosimetry findings are required to characterize this relationship. Purpose: We developed and standardized a new pretreatment planning model consisting of a predictive dosimetry procedure for OARs in patients with neuroendocrine tumors (NETs) treated with 177Lu‐DOTATATE (Lutathera). In the retrospective study described herein, we used machine learning (ML) regression algorithms to predict absorbed doses in OARs by exploiting a combination of radiomic and dosiomic features extracted from patients' imaging data. Methods: Pretreatment and posttreatment data for 20 patients with NETs treated with 177Lu‐DOTATATE were collected from two clinical centers. A total of 3412 radiomic and dosiomic features were extracted from the patients' computed tomography (CT) scans and dose maps, respectively. All dose maps were generated using Monte Carlo simulations. An ML regression model was designed based on ML algorithms for predicting the absorbed dose in every OAR (liver, left kidney, right kidney, and spleen) before and after the therapy and between each therapy session, thus predicting any possible radiotoxic effects. Results: We evaluated nine ML regression algorithms. Our predictive model achieved a mean absolute dose error (MAE, in Gy) of 0.61 for the liver, 1.58 for the spleen, 1.30 for the left kidney, and 1.35 for the right kidney between pretherapy 68Ga‐DOTATOC positron emission tomography (PET)/CT and posttherapy 177Lu‐DOTATATE single photon emission (SPECT)/CT scans. Τhe best predictive performance observed was based on the gradient boost for the liver, the left kidney and the right kidney, and on the extra tree regressor for the spleen. Evaluation of the model's performance according to its ability to predict the absorbed dose in each OAR in every possible combination of pretherapy 68Ga‐DOTATOC PET/CT and any posttherapy 177Lu‐DOTATATE treatment cycle SPECT/CT scans as well as any 177Lu‐DOTATATE SPECT/CT treatment cycle and the consequent 177Lu‐DOTATATE SPECT/CT treatment cycle revealed mean absorbed dose differences ranges from −0.55 to 0.68 Gy. Incorporating radiodosiomics features from the 68Ga‐DOTATOC PET/CT and first 177Lu‐DOTATATE SPECT/CT treatment cycle scans further improved the precision and minimized the standard deviation of the predictions in nine out of 12 instances. An average improvement of 57.34% was observed (range: 17.53%–96.12%). However, it's important to note that in three instances (i.e., Ga,C.1 → C3 in spleen and left kidney, and Ga,C.1 → C2 in right kidney) we did not observe an improvement (absolute differences of 0.17, 0.08, and 0.05 Gy, respectively). Wavelet‐based features proved to have high correlated predictive value, whereas non‐linear‐based ML regression algorithms proved to be more capable than the linear‐based of producing precise prediction in our case. Conclusions: The combination of radiomics and dosiomics has potential utility for personalized molecular radiotherapy (PMR) response evaluation and OAR dose prediction. These radiodosiomic features can potentially provide information on any possible disease recurrence and may be highly useful in clinical decision‐making, especially regarding dose escalation issues. [ABSTRACT FROM AUTHOR]

Background: The limited axial field‐of‐view (FOV) of conventional clinical positron emission tomography (PET) scanners (∼15 to 26 cm) allows detecting only 1% of all coincidence photons, hence limiting significantly their sensitivity. To overcome this limitation, the EXPLORER consortium developed the world's first total‐body PET/CT scanner that significantly increased the sensitivity, thus enabling to decrease the scan duration or injected dose. Purpose: The purpose of this study is to perform and validate Monte Carlo simulations of the uEXPLORER PET scanner, which can be used to devise novel conceptual designs and geometrical configurations through obtaining features that are difficult to obtain experimentally. Methods: The total‐body uEXPLORER PET scanner was modeled using GATE Monte Carlo (MC) platform. The model was validated through comparison with experimental measurements of various performance parameters, including spatial resolution, sensitivity, count rate performance, and image quality, according to NEMA‐NU2 2018 standards. Furthermore, the effects of the time coincidence window and maximum ring difference on the count rate and noise equivalent count rate (NECR) were evaluated. Results: Overall, the validation study showed that there was a good agreement between the simulation and experimental results. The differences between the simulated and experimental total sensitivity for the NEMA and extended phantoms at the center of the FOV were 2.3% and 0.0%, respectively. The difference in peak NECR was 9.9% for the NEMA phantom and 1.0% for the extended phantom. The average bias between the simulated and experimental results of the full‐width‐at‐half maximum (FWHM) for six different positions and three directions was 0.12 mm. The simulations showed that using a variable coincidence time window based on the maximum ring difference can reduce the effect of random coincidences and improve the NECR compared to a constant time coincidence window. The NECR corresponding to 252‐ring difference was 2.11 Mcps, which is larger than the NECR corresponding to 336‐ring difference (2.04 Mcps). Conclusion: The developed MC model of the uEXPLORER PET scanner was validated against experimental measurements and can be used for further assessment and design optimization of the scanner. [ABSTRACT FROM AUTHOR]

Background: Physiological motion, such as respiratory motion, has become a limiting factor in the spatial resolution of positron emission tomography (PET) imaging as the resolution of PET detectors continue to improve. Motion‐induced misregistration between PET and CT images can also cause attenuation correction artifacts. Respiratory gating can be used to freeze the motion and to reduce motion induced artifacts. Purpose: In this study, we propose a robust data‐driven approach using an unsupervised deep clustering network that employs an autoencoder (AE) to extract latent features for respiratory gating. Methods: We first divide list‐mode PET data into short‐time frames. The short‐time frame images are reconstructed without attenuation, scatter, or randoms correction to avoid attenuation mismatch artifacts and to reduce image reconstruction time. The deep AE is then trained using reconstructed short‐time frame images to extract latent features for respiratory gating. No additional data are required for the AE training. K‐means clustering is subsequently used to perform respiratory gating based on the latent features extracted by the deep AE. The effectiveness of our proposed Deep Clustering method was evaluated using physical phantom and real patient datasets. The performance was compared against phase gating based on an external signal (External) and image based principal component analysis (PCA) with K‐means clustering (Image PCA). Results: The proposed method produced gated images with higher contrast and sharper myocardium boundaries than those obtained using the External gating method and Image PCA. Quantitatively, the gated images generated by the proposed Deep Clustering method showed larger center of mass (COM) displacement and higher lesion contrast than those obtained using the other two methods. Conclusions: The effectiveness of our proposed method was validated using physical phantom and real patient data. The results showed our proposed framework could provide superior gating than the conventional External method and Image PCA. [ABSTRACT FROM AUTHOR]

Background: Whole‐body Metabolic Tumor Volume (MTVwb) is an independent prognostic factor for overall survival in lung cancer patients. Automatic segmentation methods have been proposed for MTV calculation. Nevertheless, most of existing methods for patients with lung cancer only segment tumors in the thoracic region. Purpose: In this paper, we present a Two‐Stage cascaded neural network integrated with Camouflaged Object Detection mEchanisms (TS‐Code‐Net) for automatic segmenting tumors from whole‐body PET/CT images. Methods: Firstly, tumors are detected from the Maximum Intensity Projection (MIP) images of PET/CT scans, and tumors' approximate localizations along z‐axis are identified. Secondly, the segmentations are performed on PET/CT slices that contain tumors identified by the first step. Camouflaged object detection mechanisms are utilized to distinguish the tumors from their surrounding regions that have similar Standard Uptake Values (SUV) and texture appearance. Finally, the TS‐Code‐Net is trained by minimizing the total loss that incorporates the segmentation accuracy loss and the class imbalance loss. Results: The performance of the TS‐Code‐Net is tested on a whole‐body PET/CT image data‐set including 480 Non‐Small Cell Lung Cancer (NSCLC) patients with five‐fold cross‐validation using image segmentation metrics. Our method achieves 0.70, 0.76, and 0.70, for Dice, Sensitivity and Precision, respectively, which demonstrates the superiority of the TS‐Code‐Net over several existing methods related to metastatic lung cancer segmentation from whole‐body PET/CT images. Conclusions: The proposed TS‐Code‐Net is effective for whole‐body tumor segmentation of PET/CT images. Codes for TS‐Code‐Net are available at: https://github.com/zyj19/TS‐Code‐Net. [ABSTRACT FROM AUTHOR]

PurposeThe developments of PET/CT and PET/MR scanners provide opportunities for improving PET image quality by using anatomical information. In this paper, we propose a novel co-learning three-dimensional (3D) convolutional neural network (CNN) to extract modality-specific features from PET/CT image pairs and integrate complementary features into an iterative reconstruction framework to improve PET image reconstruction.MethodsWe used a pretrained deep neural network to represent PET images. The network was trained using low-count PET and CT image pairs as inputs and high-count PET images as labels. This network was then incorporated into a constrained maximum likelihood framework to regularize PET image reconstruction. Two different network structures were investigated for the integration of anatomical information from CT images. One was a multichannel CNN, which treated PET and CT volumes as separate channels of the input. The other one was multibranch CNN, which implemented separate encoders for PET and CT images to extract latent features and fed the combined latent features into a decoder. Using computer-based Monte Carlo simulations and two real patient datasets, the proposed method has been compared with existing methods, including the maximum likelihood expectation maximization (MLEM) reconstruction, a kernel-based reconstruction and a CNN-based deep penalty method with and without anatomical guidance.ResultsReconstructed images showed that the proposed constrained ML reconstruction approach produced higher quality images than the competing methods. The tumors in the lung region have higher contrast in the proposed constrained ML reconstruction than in the CNN-based deep penalty reconstruction. The image quality was further improved by incorporating the anatomical information. Moreover, the liver standard deviation was lower in the proposed approach than all the competing methods at a matched lesion contrast.ConclusionsThe supervised co-learning strategy can improve the performance of constrained maximum likelihood reconstruction. Compared with existing techniques, the proposed method produced a better lesion contrast versus background standard deviation trade-off curve, which can potentially improve lesion detection.

Background: Given the large number of readout pixels in clinical positron emission tomography (PET) scanners, signal multiplexing is an indispensable feature to reduce scanner complexity, power consumption, heat output, and cost. Purpose: In this paper, we introduce interleaved multiplexing (iMux) scheme that utilizes the characteristic light‐sharing pattern of depth‐encoding Prism‐PET detector modules with single‐ended readout. Methods: In the iMux readout, four anodes from every other silicon photomultiplier (SiPM) pixels across rows and columns, which overlap with four distinct light guides, are connected to the same application‐specific integrated circuit (ASIC) channel. The 4‐to‐1 coupled Prism‐PET detector module was used which consisted of a 16 × 16 array of 1.5 × 1.5 × 20 mm3 lutetium yttrium oxyorthosilicate (LYSO) scintillator crystals coupled to an 8 × 8 array with 3 × 3 mm2 SiPM pixels. A deep learning‐based demultiplexing model was investigated to recover the encoded energy signals. Two different experiments were performed with non‐multiplexed and multiplexed readouts to evaluate the spatial, depth of interaction (DOI), and timing resolutions of our proposed iMux scheme. Results: The measured flood histograms, using the decoded energy signals from our deep learning‐based demultiplexing architecture, achieved perfect crystal identification of events with negligible decoding error. The average energy, DOI, and timing resolutions were 9.6 ± 1.5%, 2.9 ± 0.9 mm, and 266 ± 19 ps for non‐multiplexed readout and 10.3 ± 1.6%, 2.8 ± 0.8 mm, and 311 ± 28 ps for multiplexed readout, respectively. Conclusions: Our proposed iMux scheme improves on the already cost‐effective and high‐resolution Prism‐PET detector module and provides 16‐to‐1 crystal‐to‐readout multiplexing without appreciable performance degradation. Also, only four SiPM pixels are shorted together in the 8 × 8 array to achieve 4‐to‐1 pixel‐to‐readout multiplexing, resulting in lower capacitance per multiplexed channel. [ABSTRACT FROM AUTHOR]

Background: Small‐animal positron emission tomography (PET) systems are widely used in molecular imaging research and drug development. There is also growing interest in organ‐dedicated clinical PET systems. In these small‐diameter PET systems, the measurement of the depth‐of‐interaction (DOI) of annihilation photons in scintillation crystals allows for the correction of parallax error in PET system, leading to an improvement on the spatial resolution uniformity. The DOI information is also useful for improving the timing resolution of PET system as it enables the correction of DOI‐dependent time walk in the arrival time difference measurement of annihilation photon pairs. The dual‐ended readout scheme is one of the most widely investigated DOI measurement methods, which collects visible photons using a pair of photosensors located at both ends of the scintillation crystal. Although the dual‐ended readout allows for simple and accurate DOI estimation, it requires twice the number of photosensors compared to the single‐ended readout scheme. Purpose: To effectively reduce the number of photosensors in a dual‐ended readout scheme, we propose a novel PET detector configuration that employs 45° tilted and sparsely arranged silicon photomultipliers (SiPMs). In this configuration, the angle between the scintillation crystal and SiPM is 45°. Therefore, and thus, the diagonal of the scintillation crystal matches one of the lateral sides of the SiPM. Accordingly, it allows for the use of SiPM device larger than the scintillation crystal, thereby improving light collection efficiency with a higher fill factor and reducing SiPM quantity. In addition, all scintillation crystals can achieve more uniform performance than other dual‐ended readout methods with a sparse SiPM arrangement because 50% of the scintillation crystal cross section is commonly in contact with the SiPM. Methods: To demonstrate the feasibility of our proposed concept, we implemented a PET detector that employs a 4×${\rm{\;}} \times \;$4 LSO block with a single crystal dimension of 3.03 × 3.03 × 20 mm3 and a 45° tilted SiPM array. The 45° tilted SiPM array consists of 2 × 3 SiPM elements at the top ("Top SiPM") and 3 × 2 SiPM elements at the bottom ("Bottom SiPM"). Each crystal element of the 4 × 4 LSO block is optically coupled with each quarter section of the Top SiPM and Bottom SiPM pair. To characterize the performance of the PET detector, the energy, DOI, and timing resolution were measured for all 16 crystals. The energy data was obtained by summing all the charges from the Top SiPMs and Bottom SiPMs, and the DOI resolution was measured by irradiating the side of the crystal block at five different depths (2, 6, 10, 14, and 18 mm). The timing was estimated by averaging the arrival time of the annihilation photons measured at the Top SiPMs and Bottom SiPMs (Method 1). The DOI‐dependent time‐walk effect was further corrected by using DOI information and statistical variations in the trigger times at the Top SiPMs and Bottom SiPMs (Method 2). Results: The average DOI resolution of the proposed PET detector was 2.5 mm, thereby resolving the DOI at five different depths, and the average energy resolution was 16% full width at half maximum (FWHM). When Methods 1 and 2 were applied, the coincidence timing resolutions were 448 and 411 ps FWHM, respectively. Conclusions: We expect that our novel low‐cost PET detector design with 45° tilted SiPMs and a dual‐ended readout scheme would be a suitable solution for constructing a high‐resolution PET system with DOI encoding capability. [ABSTRACT FROM AUTHOR]

Background: The RefleXion X1 is a novel radiotherapy delivery system on a ring gantry equipped with fan‐beam kV‐CT and PET imaging subsystems. The day‐to‐day scanning variability of radiomics features must be evaluated before any attempt to utilize radiomics features. Purpose: This study aims to characterize the repeatability and reproducibility of radiomic features produced by the RefleXion X1 kV‐CT. Materials and Methods: The Credence Cartridge Radiomics (CCR) phantom includes six cartridges of varied materials. It was scanned 10 times on the RefleXion X1 kVCT imaging subsystem over a 3‐month period using the two most frequently used scanning protocols (BMS and BMF). Fifty‐five radiomic features were extracted for each ROI on each CT scan and analyzed using LifeX software. The coefficient of variation (COV) was computed to evaluate the repeatability. Intraclass correlation coefficient (ICC) and concordance correlation coefficient (CCC) were used to evaluate the repeatability and reproducibility of the scanned images using 0.9 as the threshold. This process is repeated on a GE PET‐CT scanner using several built‐in protocols as a comparison. Results: On average, 87% of the features on both scan protocols on the RefleXion X1 kVCT imaging subsystem can be considered repeatable as they met COV < 10% criteria. On GE PET‐CT, this number is similar at 86%. When we tighten the criteria to COV <5%, the RefleXion X1 kVCT imaging subsystem showed much better repeatability with 81% of features on average whereas GE PET‐CT showed only 73.5% on average. About 91% and 89% of the features with ICC > 0.9 respectively for BMS and BMF protocols on RefleXion X1. On the other hand, the percentage of features with ICC > 0.9 on GE PET‐CT ranges from 67% to 82%. The RefleXion X1 kVCT imaging subsystem showed excellent intra‐scanner reproducibility between the scanning protocols much better than the GE PET CT scanner. For the inter‐scanner reproducibility, the percentage of features with CCC > 0.9 ranged from 49% to 80%. between X1 and GE PET‐CT scanning protocols. Conclusions: Clinically useful CT radiomic features produced by the RefleXion X1 kVCT imaging subsystem are reproducible and stable over time, demonstrating its utility as a quantitative imaging platform. [ABSTRACT FROM AUTHOR]

Background: Simultaneous positron emission tomography/magnetic resonance imaging (PET/MRI) has shown promise in acquiring complementary multiparametric information of disease. However, designing these hybrid imaging systems is challenging due to the propensity for mutual interference between the PET and MRI subsystems. Currently, there are integrated PET/MRI systems for clinical applications. For neurologic imaging, a brain‐dedicated PET insert provides superior spatial resolution and sensitivity compared to body PET scanners. Purpose: Our first‐generation prototype brain PET insert ("PETcoil") demonstrated RF‐penetrability and MR‐compatibility. In the second‐generation PETcoil system, all analog silicon photomultiplier (SiPM) signal digitization is moved inside the detectors, which results in substantially better PET detector performance, but presents a greater technical challenge for achieving MR‐compatibility. In this paper, we report results from MR‐compatibility studies of two fully assembled second‐generation PET insert detector modules. Methods: We studied the effect of the presence of the two second‐generation TOF‐PET insert detectors on parameters that affect MR image quality and evaluated TOF‐PET detector performance under different MRI pulse sequence conditions. Results: With the presence of operating PET detectors, no RF noise peaks were induced in the MR images, but the relative average noise level was increased by 15%, which led to a 3.1 to 4.2‐dB degradation in MR image signal‐to‐noise ratio (SNR). The relative homogeneity of MR images degraded by less than 1.5% with the two operating TOF‐PET detectors present. The reported results also indicated that ghosting artifacts (percent signal ghosting (PSG) ⩽ 1%) and MR susceptibility artifacts (0.044 ppm) were insignificant. The PET detector data showed a relative change of less than 5% in detector module performance between running outside and within the MR bore under different MRI pulse sequences except for energy resolution in EPI sequence (13% relative difference). Conclusions: The PET detector operation did not cause any significant artifacts in MR images and the performance and time‐of‐flight (TOF) capability of the former were preserved under different tested MR conditions. [ABSTRACT FROM AUTHOR]