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Objective. Penalty parameters in penalized likelihood positron emission tomography (PET) reconstruction are typically determined empirically. The cross-validation log-likelihood (CVLL) method has been introduced to optimize these parameters by maximizing a CVLL function, which assesses the likelihood of reconstructed images using one subset of a list-mode dataset based on another subset. This study aims to validate the efficacy of the CVLL method in whole-body imaging for cancer patients using a conventional clinical PET scanner. Approach. Fifteen lung cancer patients were injected with 243.7 ± 23.8 MBq of [18F]FDG and underwent a 22 min PET scan on a Biograph mCT PET/CT scanner, starting at 60 ± 5 min post-injection. The PET list-mode data were partitioned by subsampling without replacement, with 20 minutes of data for image reconstruction using an in-house ordered subset expectation maximization algorithm and the remaining 2 minutes of data for cross-validation. Two penalty parameters, penalty strength β and Fair penalty function parameter δ, were subjected to optimization. Whole-body images were reconstructed, and CVLL values were computed across various penalty parameter combinations. The optimal image corresponding to the maximum CVLL value was selected by a grid search for each patient. Main results. The δ value required to maximize the CVLL value was notably small (⩽10−6 in this study). The influences of voxel size and scan duration on image optimization were investigated. A correlation analysis revealed a significant inverse relationship between optimal β and scan count level, with a correlation coefficient of −0.68 (p -value = 3.5 × 10−5). The optimal images selected by the CVLL method were compared with those chosen by two radiologists based on their diagnostic preferences. Differences were observed in the selection of optimal images. Significance. This study demonstrates the feasibility of incorporating the CVLL method into routine imaging protocols, potentially allowing for a wide range of combinations of injected radioactivity amounts and scan durations in modern PET imaging. [ABSTRACT FROM AUTHOR]

Objective. This work proposes, for the first time, an image-based end-to-end self-normalization framework for positron emission tomography (PET) using conditional generative adversarial networks (cGANs). Approach. We evaluated different approaches by exploring each of the following three methodologies. First, we used images that were either unnormalized or corrected for geometric factors, which encompass all time-invariant factors, as input data types. Second, we set the input tensor shape as either a single axial slice (2D) or three contiguous axial slices (2.5D). Third, we chose either Pix2Pix or polarized self-attention (PSA) Pix2Pix, which we developed for this work, as a deep learning network. The targets for all approaches were the axial slices of images normalized using the direct normalization method. We performed Monte Carlo simulations of ten voxelized phantoms with the SimSET simulation tool and produced 26,000 pairs of axial image slices for training and testing. Main results. The results showed that 2.5D PSA Pix2Pix trained with geometric-factors-corrected input images achieved the best performance among all the methods we tested. All approaches improved general image quality figures of merit peak signal to noise ratio (PSNR) and structural similarity index (SSIM) from ∼15 % to ∼55 %, and 2.5D PSA Pix2Pix showed the highest PSNR (28.074) and SSIM (0.921). Lesion detectability, measured with region of interest (ROI) PSNR, SSIM, normalized contrast recovery coefficient, and contrast-to-noise ratio, was generally improved for all approaches, and 2.5D PSA Pix2Pix trained with geometric-factors-corrected input images achieved the highest ROI PSNR (28.920) and SSIM (0.973). Significance. This study demonstrates the potential of an image-based end-to-end self-normalization framework using cGANs for improving PET image quality and lesion detectability without the need for separate normalization scans. [ABSTRACT FROM AUTHOR]

Objective. In quantitative dynamic positron emission tomography (PET), time series of images, reflecting the tissue response to the arterial tracer supply, are reconstructed. This response is described by kinetic parameters, which are commonly determined on basis of the tracer concentration in tissue and the arterial input function. In clinical routine the latter is estimated by arterial blood sampling and analysis, which is a challenging process and thus, attempted to be derived directly from reconstructed PET images. However, a mathematical analysis about the necessity of measurements of the common arterial whole blood activity concentration, and the concentration of free non-metabolized tracer in the arterial plasma, for a successful kinetic parameter identification does not exist. Here we aim to address this problem mathematically. Approach. We consider the identification problem in simultaneous pharmacokinetic modeling of multiple regions of interests of dynamic PET data using the irreversible two-tissue compartment model analytically. In addition to this consideration, the situation of noisy measurements is addressed using Tikhonov regularization. Furthermore, numerical simulations with a regularization approach are carried out to illustrate the analytical results in a synthetic application example. Main results. We provide mathematical proofs showing that, under reasonable assumptions, all metabolic tissue parameters can be uniquely identified without requiring additional blood samples to measure the arterial input function. A connection to noisy measurement data is made via a consistency result, showing that exact reconstruction of the ground-truth tissue parameters is stably maintained in the vanishing noise limit. Furthermore, our numerical experiments suggest that an approximate reconstruction of kinetic parameters according to our analytic results is also possible in practice for moderate noise levels. Significance. The analytical result, which holds in the idealized, noiseless scenario, suggests that for irreversible tracers, fully quantitative dynamic PET imaging is in principle possible without costly arterial blood sampling and metabolite analysis. [ABSTRACT FROM AUTHOR]

Objective. The partial-volume effect (PVE) is an important factor impairing tumour quantification in molecular imaging. The commonly used contour-volume-of-interest (contour-VOI) approach to correct for this effect employs phantom-based recovery coefficients. Applying oversize-VOIs could offer superior quantification accuracy in small lesions. The oversize-VOI approach uses a large oversize volume to determine the total tumour activity after applying a background correction. Aims of this study were to provide a procedure for the application of the oversize-VOI approach and to compare its performance to the contour-VOI approach in PET imaging. Approach. A sphere tumour model was simulated to determine the oversize diameter that contained 90%, 95%, and 98% of the total activity as a function of the tumour size. Experimental investigations involving phantom and clinical data were conducted on a digital PET/CT scanner. In the phantom investigation, 12 spherical tumour inserts (diameters ranging from 3.7 to 37.4 mm) containing 18F-solution were used. The accuracy of the contour- and oversize-VOI approach was evaluated for different signal-to-background ratios (20–3). Clinically, both approaches were applied on PET/CT images acquired with 18F-labelled prostate-specific membrane antigen in prostate cancer patients. Main results. From the tumour model, we deduced that an oversize-VOI of two PET spatial resolutions larger than the physical lesion diameter contains at least 98% of the total activity for lesions with diameters down to one PET spatial resolution, while minimizing the background contribution. Both approaches were robust against varying phantom and clinical imaging conditions. Performance of the oversize-VOI approach was favorable for lesions below 10 mm in diameter, whereas the contour-VOI approach was slightly more accurate for sizes above 10 mm. Significance. The oversize-VOI approach facilitates image quantification of small tumours. It is simple and effective to correct for the PVE and may be used in pre-therapeutic (small) tumour dosimetry. [ABSTRACT FROM AUTHOR]

Objective. This study aims to address the issue of long scan durations required by traditional graphical analysis methods, such as the Logan plot and its variant, the reversible equilibrium (RE) Logan plot, for dynamic PET imaging of tracer kinetics. Approach. We propose a relative RE Logan model that builds on the principles of the Logan plot and its variant to significantly reduce scan time without compromising the accuracy of tracer kinetics analysis. The model is supported by theoretical evidence and experimental validations, including two computer simulations and one clinical data analysis. Main results. The proposed model demonstrates a significant linear relationship between the variable x and the slope DVT of the RE Logan plot, and the variable x ′ and the slope D V T ′ of the relative RE Logan plot. The Pearson correlation coefficients (r) of the linear fitting of the x ′ to the x equal 0.9849 in the simulated data and 0.9912 in the clinical data. Similarly, the r values for the linear fitting of D V T ′ to DVT equal 0.9989 and 0.9988 in the simulated data, and 0.9954 in the clinical data. Significance. These results demonstrate the model's capability to maintain strong linear relationships and produce parametric images comparable to the traditional RE Logan plot, but with the considerable advantage of shorter scan durations. This innovation holds significant potential for enhancing the efficiency and feasibility of PET imaging in clinical settings. [ABSTRACT FROM AUTHOR]

We previously proposed range-guided adaptive proton therapy (RGAPT) that uses mid-range treatment beams as probing beams and intra-fractionated range measurements for online adaptation. In this work, we demonstrated experimental verification and reported the dosimetric accuracy for RGAPT. A STEEV phantom was used for the experiments, and a 3 × 3 × 3 cm3 cube inside the phantom was assigned to be the treatment target. We simulated three online range shift scenarios: reference, overshoot, and undershoot, by placing upstream Lucite sheets, 4, 0, and 8 that corresponded to changes of 0, 6.8, and −6.8 mm, respectively, in water-equivalent path length. The reference treatment plan was to deliver single-field uniform target doses in pencil beam scanning mode and generated on the Eclipse treatment planning system. Different numbers of mid-range layers, including single, three, and five layers, were selected as probing beams to evaluate beam range (BR) measurement accuracy in positron emission tomography (PET). Online plans were modified to adapt to BR shifts and compensate for probing beam doses. In contrast, non-adaptive plans were also delivered and compared to adaptive plans by film measurements. The mid-range probing beams of three (5.55MU) and five layers (8.71MU) yielded accurate range shift measurements in 60 s of PET acquisition with uncertainty of 0.5 mm while the single-layer probing (1.65MU) was not sufficient for measurements. The adaptive plans achieved an average gamma (2%/2 mm) passing rate of 95%. In contrast, the non-adaptive plans only had an average passing rate of 69%. RGAPT planning and delivery are feasible and verified by the experiments. The probing beam delivery, range measurements, and adaptive planning and delivery added a small increase in treatment delivery workflow time but resulted in substantial dose improvement. The three-layer mid-range probing was most suitable considering the balance of high range measurement accuracy and the low number of probing beam layers. [ABSTRACT FROM AUTHOR]

Objective. Develop a prototype on-line positron emission tomography (PET) scanner and evaluate its capability of on-line imaging and intra-fractionated proton-induced radioactivity range measurement. Approach. Each detector consists of 32 × 32 array of 2 × 2 × 30 mm3 Lutetium–Yttrium Oxyorthosilicate scintillators with single-scintillator-end readout through a 20 × 20 array of 3 × 3 mm2 Silicon Photomultipliers. The PET can be configurated with a full-ring of 20 detectors for conventional PET imaging or a partial-ring of 18 detectors for on-line imaging and range measurement. All detector-level readout and processing electronics are attached to the backside of the system gantry and their output signals are transferred to a field-programable-gate-array based system electronics and data acquisition that can be placed 2 m away from the gantry. The PET imaging performance and radioactivity range measurement capability were evaluated by both the offline study that placed a radioactive source with known intensity and distribution within a phantom and the online study that irradiated a phantom with proton beams under different radiation and imaging conditions. Main results. The PET has 32 cm diameter and 6.5 cm axial length field-of-view (FOV), ∼2.3–5.0 mm spatial resolution within FOV, 3% sensitivity at the FOV center, 18%–30% energy resolution, and ∼9 ns coincidence time resolution. The offline study shows the PET can determine the shift of distal falloff edge position of a known radioactivity distribution with the accuracy of 0.3 ± 0.3 mm even without attenuation and scatter corrections, and online study shows the PET can measure the shift of proton-induced positron radioactive range with the accuracy of 0.6 ± 0.3 mm from the data acquired with a short-acquisition (60 s) and low-dose (5 MU) proton radiation to a human head phantom. Significance. This study demonstrated the capability of intra-fractionated PET imaging and radioactivity range measurement and will enable the investigation on the feasibility of intra-fractionated, range-shift compensated adaptive proton therapy. [ABSTRACT FROM AUTHOR]

Objective. Head and neck (H&N) cancers are among the most prevalent types of cancer worldwide, and [18F]F-FDG PET/CT is widely used for H&N cancer management. Recently, the diffusion model has demonstrated remarkable performance in various image-generation tasks. In this work, we proposed a 3D diffusion model to accurately perform H&N tumor segmentation from 3D PET and CT volumes. Approach. The 3D diffusion model was developed considering the 3D nature of PET and CT images acquired. During the reverse process, the model utilized a 3D UNet structure and took the concatenation of 3D PET, CT, and Gaussian noise volumes as the network input to generate the tumor mask. Experiments based on the HECKTOR challenge dataset were conducted to evaluate the effectiveness of the proposed diffusion model. Several state-of-the-art techniques based on U-Net and Transformer structures were adopted as the reference methods. Benefits of employing both PET and CT as the network input, as well as further extending the diffusion model from 2D to 3D, were investigated based on various quantitative metrics and qualitative results. Main results. Results showed that the proposed 3D diffusion model could generate more accurate segmentation results compared with other methods (mean Dice of 0.739 compared to less than 0.726 for other methods). Compared to the diffusion model in 2D form, the proposed 3D model yielded superior results (mean Dice of 0.739 compared to 0.669). Our experiments also highlighted the advantage of utilizing dual-modality PET and CT data over only single-modality data for H&N tumor segmentation (with mean Dice less than 0.570). Significance. This work demonstrated the effectiveness of the proposed 3D diffusion model in generating more accurate H&N tumor segmentation masks compared to the other reference methods. [ABSTRACT FROM AUTHOR]

Objective. Accurate and reproducible tumor delineation on positron emission tomography (PET) images is required to validate predictive and prognostic models based on PET radiomic features. Manual segmentation of tumors is time-consuming whereas semi-automatic methods are easily implementable and inexpensive. This study assessed the reliability of semi-automatic segmentation methods over manual segmentation for tumor delineation in head and neck squamous cell carcinoma (HNSCC) PET images. Approach. We employed manual and six semi-automatic segmentation methods (just enough interaction (JEI), watershed, grow from seeds (GfS), flood filling (FF), 30% SUVmax and 40%SUVmax threshold) using 3D slicer software to extract 128 radiomic features from FDG-PET images of 100 HNSCC patients independently by three operators. We assessed the distributional properties of all features and considered 92 log-transformed features for subsequent analysis. For each paired comparison of a feature, we fitted a separate linear mixed effect model using the method (two levels; manual versus one semi-automatic method) as a fixed effect and the subject and the operator as the random effects. We estimated different statistics—the intraclass correlation coefficient agreement (aICC), limits of agreement (LoA), total deviation index (TDI), coverage probability (CP) and coefficient of individual agreement (CIA)—to evaluate the agreement between the manual and semi-automatic methods. Main results. Accounting for all statistics across 92 features, the JEI method consistently demonstrated acceptable agreement with the manual method, with median values of aICC = 0.86, TDI = 0.94, CP = 0.66, and CIA = 0.91. Significance. This study demonstrated that JEI method is a reliable semi-automatic method for tumor delineation on HNSCC PET images. [ABSTRACT FROM AUTHOR]

Objective. Equivalent uniform aerobic dose (EUAD) is proposed for comparison of integrated cell survival in tumors with different distributions of hypoxia and radiation dose. Approach. The EUAD assumes that for any non-uniform distributions of radiation dose and oxygen enhancement ratio (OER) within a tumor, there is a uniform distribution of radiation dose under hypothetical aerobic conditions with OER = 1 that produces equal integrated survival of clonogenic cells. This definition of EUAD has several advantages. First, the EUAD allows one to compare survival of clonogenic cells in tumors with intra-tumor and inter-tumor variation of radio sensitivity due to hypoxia because the cell survival is recomputed under the same benchmark oxygen level (OER = 1). Second, the EUAD for homogeneously oxygenated tumors is equal to the concept of equivalent uniform dose. Main results. We computed the EUAD using radiotherapy dose and the OER derived from the 18F-Fluoromisonidazole PET (18F-FMISO PET) images of hypoxia in patients with glioblastoma, the most common and aggressive type of primary malignant brain tumor. The 18F-FMISO PET images include a distribution of SUV (Standardized Uptake Value); therefore, the SUV is converted to partial oxygen pressure (pO2) and then to the OER. The prognostic value of EUAD in radiotherapy for hypoxic tumors is demonstrated using correlation between EUAD and overall survival (OS) in radiotherapy for glioblastoma. The correction to the EUAD for the absolute hypoxic volume that traceable to the tumor control probability improves the correlation with OS. Significance. While the analysis proposed in this research is based on the 18F-FMISO PET images for glioblastoma, the EUAD is a universal radiobiological concept and is not associated with any specific cancer or any specific PET or MRI biomarker of hypoxia. Therefore, this research can be generalized to other cancers, for example stage III lung cancer, and to other hypoxia biomarkers. [ABSTRACT FROM AUTHOR]

Objective. Monolithic crystal detectors are increasingly being applied in positron emission tomography (PET) devices owing to their excellent depth-of-interaction (DOI) resolution capabilities and high detection efficiency. In this study, we constructed and evaluated a dual-ended readout monolithic crystal detector based on a multiplexing method. Approach. We employed two 12 × 12 silicon photomultiplier (SiPM) arrays for readout, and the signals from the 12 × 12 array were merged into 12 X and 12 Y channels using channel multiplexing. In 2D reconstruction, three methods based on the centre of gravity (COG) were compared, and the concept of thresholds was introduced. Furthermore, a light convolutional neural network (CNN) was employed for testing. To enhance depth localization resolution, we proposed a method by utilizing the mutual information from both ends of the SiPMs. The source width and collimation effect were simulated using GEANT4, and the intrinsic spatial resolution was separated from the measured values. Main results. At an operational voltage of 29 V for the SiPM, an energy resolution of approximately 12.5 % was achieved. By subtracting a 0.8 % threshold from the total energy in every channel, a 2D spatial resolution of approximately 0.90 mm full width at half maximum (FWHM) can be obtained. Furthermore, a higher level of resolution, approximately 0.80 mm FWHM, was achieved using a CNN, with some alleviation of edge effects. With the proposed DOI method, a significant 1.36 mm FWHM average DOI resolution can be achieved. Additionally, it was found that polishing and black coating on the crystal surface yielded smaller edge effects compared to a rough surface with a black coating. Significance. The introduction of a threshold in COG method and a dual-ended readout scheme can lead to excellent spatial resolution for monolithic crystal detectors, which can help to develop PET systems with both high sensitivity and high spatial resolution. [ABSTRACT FROM AUTHOR]

Objective. Performing positron emission tomography (PET) denoising within the image space proves effective in reducing the variance in PET images. In recent years, deep learning has demonstrated superior denoising performance, but models trained on a specific noise level typically fail to generalize well on different noise levels, due to inherent distribution shifts between inputs. The distribution shift usually results in bias in the denoised images. Our goal is to tackle such a problem using a domain generalization technique. Approach. We propose to utilize the domain generalization technique with a novel feature space continuous discriminator (CD) for adversarial training, using the fraction of events as a continuous domain label. The core idea is to enforce the extraction of noise-level invariant features. Thus minimizing the distribution divergence of latent feature representation for different continuous noise levels, and making the model general for arbitrary noise levels. We created three sets of 10%, 13%–22% (uniformly randomly selected), or 25% fractions of events from 97 18F-MK6240 tau PET studies of 60 subjects. For each set, we generated 20 noise realizations. Training, validation, and testing were implemented using 1400, 120, and 420 pairs of 3D image volumes from the same or different sets. We used 3D UNet as the baseline and implemented CD to the continuous noise level training data of 13%–22% set. Main results. The proposed CD improves the denoising performance of our model trained in a 13%–22% fraction set for testing in both 10% and 25% fraction sets, measured by bias and standard deviation using full-count images as references. In addition, our CD method can improve the SSIM and PSNR consistently for Alzheimer-related regions and the whole brain. Significance. To our knowledge, this is the first attempt to alleviate the performance degradation in cross-noise level denoising from the perspective of domain generalization. Our study is also a pioneer work of continuous domain generalization to utilize continuously changing source domains. [ABSTRACT FROM AUTHOR]

Objective. The TOol for PArticle Simulation (TOPAS) is a Geant4-based Monte Carlo software application that has been used for both research and clinical studies in medical physics. So far, most users of TOPAS have focused on radiotherapy-related studies, such as modeling radiation therapy delivery systems or patient dose calculation. Here, we present the first set of TOPAS extensions to make it easier for TOPAS users to model medical imaging systems.Approach. We used the extension system of TOPAS to implement pre-built, user-configurable geometry components such as detectors (e.g. flat-panel and multi-planar detectors) for various imaging modalities and pre-built, user-configurable scorers for medical imaging systems (e.g. digitizer chain).Main results. We developed a flexible set of extensions that can be adapted to solve research questions for a variety of imaging modalities. We then utilized these extensions to model specific examples of cone-beam CT (CBCT), positron emission tomography (PET), and prompt gamma (PG) systems. The first of these new geometry components, the FlatImager, was used to model example CBCT and PG systems. Detected signals were accumulated in each detector pixel to obtain the intensity of x-rays penetrating objects or prompt gammas from proton-nuclear interaction. The second of these new geometry components, the RingImager, was used to model an example PET system. Positron-electron annihilation signals were recorded in crystals of the RingImager and coincidences were detected. The simulated data were processed using corresponding post-processing algorithms for each modality and obtained results in good agreement with the expected true signals or experimental measurement.Significance. The newly developed extension is a first step to making it easier for TOPAS users to build and simulate medical imaging systems. Together with existing TOPAS tools, this extension can help integrate medical imaging systems with radiotherapy simulations for image-guided radiotherapy.

Objective. PET (Positron Emission Tomography) inherently involves radiotracer injections and long scanning time, which raises concerns about the risk of radiation exposure and patient comfort. Reductions in radiotracer dosage and acquisition time can lower the potential risk and improve patient comfort, respectively, but both will also reduce photon counts and hence degrade the image quality. Therefore, it is of interest to improve the quality of low-dose PET images. Approach. A supervised multi-modality deep learning model, named M3S-Net, was proposed to generate standard-dose PET images (60 s per bed position) from low-dose ones (10 s per bed position) and the corresponding CT images. Specifically, we designed a multi-branch convolutional neural network with multi-self-attention mechanisms, which first extracted features from PET and CT images in two separate branches and then fused the features to generate the final generated PET images. Moreover, a novel multi-modality structure-promoting term was proposed in the loss function to learn the anatomical information contained in CT images. Main results. We conducted extensive numerical experiments on real clinical data collected from local hospitals. Compared with state-of-the-art methods, the proposed M3S-Net not only achieved higher objective metrics and better generated tumors, but also performed better in preserving edges and suppressing noise and artifacts. Significance. The experimental results of quantitative metrics and qualitative displays demonstrate that the proposed M3S-Net can generate high-quality PET images from low-dose ones, which are competable to standard-dose PET images. This is valuable in reducing PET acquisition time and has potential applications in dynamic PET imaging. [ABSTRACT FROM AUTHOR]

Objective. Measurement of the time-of-flight (TOF) difference of each coincident pair of photons increases the effective sensitivity of positron emission tomography (PET). Many authors have analyzed the benefit of TOF for quantification and hot spot detection in the reconstructed activity images. However, TOF not only improves the effective sensitivity, it also enables the joint reconstruction of the tracer concentration and attenuation images. This can be used to correct for errors in CT- or MR-derived attenuation maps, or to apply attenuation correction without the help of a second modality. This paper presents an analysis of the effect of TOF on the variance of the jointly reconstructed attenuation and (attenuation corrected) tracer concentration images. Approach. The analysis is performed for PET systems that have a distribution of possibly non-Gaussian TOF-kernels, and includes the conventional Gaussian TOF-kernel as a special case. Non-Gaussian TOF-kernels are often observed in novel detector designs, which make use of two (or more) different mechanisms to convert the incoming 511 keV photon to optical photons. The analytical result is validated with a simple 2D simulation. Main results. We show that if two different TOF-kernels are equivalent for image reconstruction with known attenuation, then they are also equivalent for joint reconstruction of the activity and the attenuation images. The variance increase in the activity, caused by also jointly reconstructing the attenuation image, vanishes when the TOF-resolution approaches perfection. Significance. These results are of interest for PET detector development and for the development of stand-alone PET systems. [ABSTRACT FROM AUTHOR]

Objective. Head and neck (H&N) cancers are prevalent globally, and early and accurate detection is absolutely crucial for timely and effective treatment. However, the segmentation of H&N tumors is challenging due to the similar density of the tumors and surrounding tissues in CT images. While positron emission computed tomography (PET) images provide information about the metabolic activity of the tissue and can distinguish between lesion regions and normal tissue. But they are limited by their low spatial resolution. To fully leverage the complementary information from PET and CT images, we propose a novel and innovative multi-modal tumor segmentation method specifically designed for H&N tumor segmentation. Approach. The proposed novel and innovative multi-modal tumor segmentation network (LSAM) consists of two key learning modules, namely L2-Norm self-attention and latent space feature interaction, which exploit the high sensitivity of PET images and the anatomical information of CT images. These two advanced modules contribute to a powerful 3D segmentation network based on a U-shaped structure. The well-designed segmentation method can integrate complementary features from different modalities at multiple scales, thereby improving the feature interaction between modalities. Main results. We evaluated the proposed method on the public HECKTOR PET-CT dataset, and the experimental results demonstrate that the proposed method convincingly outperforms existing H&N tumor segmentation methods in terms of key evaluation metrics, including DSC (0.8457), Jaccard (0.7756), RVD (0.0938), and HD95 (11.75). Significance. The innovative Self-Attention mechanism based on L2-Norm offers scalability and is effective in reducing the impact of outliers on the performance of the model. And the novel method for multi-scale feature interaction based on Latent Space utilizes the learning process in the encoder phase to achieve the best complementary effects among different modalities. [ABSTRACT FROM AUTHOR]

Objective. This study aimed to investigate the dosimetric impact of using population-based relative electron density (RED) overrides in lieu of simulation computerized tomography (CT) in a magnetic resonance linear accelerator (MRL) workflow for male pelvis patients. Additionally, the feasibility of using prostate specific membrane antigen positron emission tomography/CT (PSMA-PET/CT) scans to assess patients' eligibility for this proposed workflow was examined. Approach. In this study, 74 male pelvis patients treated on an Elekta Unity 1.5 T MRL were retrospectively selected. The patients' individual RED values for 8 organs of interest were extracted from their simulation-CT images to establish population-based RED values. These values were used to generate individual (IndD) and population-based (PopD) RED dose plans, representing current and proposed MRL workflows, respectively. Lastly, this study compared RED values obtained from CT and PET-CT scanners in a phantom and a subset of patients. Results. Population-based RED values were mostly within two standard deviations of ICRU Report 46 values. PopD plans were comparable to IndD plans, with the average %difference magnitudes of 0.5%, 0.6%, and 0.6% for mean dose (all organs), D0.1cm3 (non-target organs) and D95%/D98% (target organs), respectively. Both phantom and patient PET-CT derived RED values had high agreement with corresponding CT-derived values, with correlation coefficients ≥ 0.9. Significance. Population-based RED values were considered suitable in a simulation-free MRL treatment workflow. Utilizing these RED values resulted in similar dosimetric uncertainties as per the current workflow. Initial findings also suggested that PET-CT scans may be used to assess prospective patients' eligibility for the proposed workflow. Future investigations will evaluate the clinical feasibility of implementing this workflow for prospective patients in the clinical setting. This is aimed to reduce patient burden during radiotherapy and increase department efficiencies. [ABSTRACT FROM AUTHOR]

Objective. Tumour response to radiation therapy appears as changes in tumour vascular condition. There are several methods for analysing tumour blood circulatory changes one of which is dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI), but there is no method that can observe the tumour vascular condition and physiological changes at the site of radiation therapy. Positron emission tomography (PET) has been applied for treatment verification in charged particle therapy, which is based on the detection of positron emitters produced through nuclear fragmentation reactions in a patient's body. However, the produced positron emitters are washed out biologically depending on the tumour vascular condition. This means that measuring the biological washout rate may allow evaluation of the tumour radiation response, in a similar manner to DCE-MRI. Therefore, this study compared the washout rates in rats between in-beam PET during 12C ion beam irradiation and DCE-MRI. Approach. Different vascular conditions of the tumour model were prepared for six nude rats. The tumour of each nude rat was irradiated by a 12C ion beam with simultaneous in-beam PET measurement. In 10–12 h, the DCE-MRI experiment was performed for the same six nude rats. The biological washout rate of the produced positron emitters (k 2,1st) and the MRI contrast agent (k 2a) were derived using the single tissue compartment model. Main results. A linear correlation was observed between k 2,1st and k 2a, and they were inversely related to fractional necrotic volume. Significance. This is the first animal study which confirmed the biological washout rate of in-beam PET correlates closely with tumour vascular condition measured with the MRI contrast agent administrated intravenously. [ABSTRACT FROM AUTHOR]

Background. Recently, approaches have utilized the superior anatomical information provided by magnetic resonance imaging (MRI) to guide the reconstruction of positron emission tomography (PET). One of those approaches is the Bowsher's prior, which has been accelerated lately with a convolutional neural network (CNN) to reconstruct MR-guided PET in the imaging domain in routine clinical imaging. Two differently trained Bowsher-CNN methods (B-CNN0 and B-CNN) have been trained and tested on brain PET/MR images with non-PSMA tracers, but so far, have not been evaluated in other anatomical regions yet. Methods. A NEMA phantom with five of its six spheres filled with the same, calibrated concentration of 18F-DCFPyL-PSMA, and thirty-two patients (mean age 64 ± 7 years) with biopsy-confirmed PCa were used in this study. Reconstruction with either of the two available Bowsher-CNN methods were performed on the conventional MR-based attenuation correction (MRAC) and T1-MR images in the imaging domain. Detectable volume of the spheres and tumors, relative contrast recovery (CR), and background variation (BV) were measured for the MRAC and the Bowsher-CNN images, and qualitative assessment was conducted by ranking the image sharpness and quality by two experienced readers. Results. For the phantom study, the B-CNN produced 12.7% better CR compared to conventional reconstruction. The small sphere volume (<1.8 ml) detectability improved from MRAC to B-CNN by nearly 13%, while measured activity was higher than the ground-truth by 8%. The signal-to-noise ratio, CR, and BV were significantly improved (p < 0.05) in B-CNN images of the tumor. The qualitative analysis determined that tumor sharpness was excellent in 76% of the PET images reconstructed with the B-CNN method, compared to conventional reconstruction. Conclusions. Applying the MR-guided B-CNN in clinical prostate PET/MR imaging improves some quantitative, as well as qualitative imaging measures. The measured improvements in the phantom are also clearly translated into clinical application. [ABSTRACT FROM AUTHOR]

Objective. Whole-body positron emission tomography/computed tomography (PET/CT) scans are an important tool for diagnosing various malignancies (e.g. malignant melanoma, lymphoma, or lung cancer), and accurate segmentation of tumors is a key part of subsequent treatment. In recent years, convolutional neural network based segmentation methods have been extensively investigated. However, these methods often give inaccurate segmentation results, such as oversegmentation and undersegmentation. To address these issues, we propose a postprocessing method based on a graph convolutional network (GCN) to refine inaccurate segmentation results and improve the overall segmentation accuracy. Approach. First, nnU-Net is used as an initial segmentation framework, and the uncertainty in the segmentation results is analyzed. Certain and uncertain pixels are used to establish the nodes of a graph. Each node and its 6 neighbors form an edge, and 32 nodes are randomly selected as uncertain nodes to form edges. The highly uncertain nodes are used as the subsequent refinement targets. Second, the nnU-Net results of the certain nodes are used as labels to form a semisupervised graph network problem, and the uncertain part is optimized by training the GCN to improve the segmentation performance. This describes our proposed nnU-Net + GCN segmentation framework. Main results. We perform tumor segmentation experiments with the PET/CT dataset from the MICCIA2022 autoPET challenge. Among these data, 30 cases are randomly selected for testing, and the experimental results show that the false-positive rate is effectively reduced with nnU-Net + GCN refinement. In quantitative analysis, there is an improvement of 2.1% for the average Dice score, 6.4 for the 95% Hausdorff distance (HD95), and 1.7 for the average symmetric surface distance. Significance. The quantitative and qualitative evaluation results show that GCN postprocessing methods can effectively improve the tumor segmentation performance. [ABSTRACT FROM AUTHOR]

Time-of-flight (TOF) positron emission tomography (PET) detectors improve the signal-to-noise ratio of PET images by limiting the position of the generation of two 511 keV gamma-rays in space using the arrival time difference between the two photons. Unfortunately, bismuth germanate (BGO), widely used in conventional PET detectors, was limited as a TOF PET scintillator due to the relatively slow decay time of the scintillation photons. However, prompt Cerenkov light in BGO has been identified in addition to scintillation photons. Using Cerenkov photons for timing has significantly improved the coincidence timing resolution (CTR) of BGO. Based on this, further research on improving the CTR for a BGO-based TOF PET system is being actively conducted. Wrapping materials for BGO pixels have primarily employed white reflectors to most efficiently collect scintillation light. White reflectors have customarily been used as reflectors for BGO pixels even after Cerenkov light began to be utilized for timing calculations in pixel-level experiments. However, when the arrival-time differences of the two 511 keV annihilations photons were measured with pure Cerenkov radiators, painting the lateral sides of the radiators black can improve CTR by suppressing the reflection of Cerenkov photons. The use of BGO for TOF PET detectors requires simultaneously minimizing scintillation loss for good energy information and suppressing reflected Cerenkov photons for better timing performance. Thus, reflectors for BGO pixels should be optimized for better timing and energy performance. In this study, colored polytetrafluoroethylene (PTFE) tapes with discontinuous reflectance values at specific wavelengths were applied as a BGO reflector. We hypothesized that CTR could be enhanced by selectively suppressing reflected Cerenkov photons with an optimum colored reflector on the BGO pixel while minimizing scintillation photon loss. CTRs were investigated utilizing white and three colors (yellow, red, and green) PTFE tapes as a reflector. In addition, black-painted PTFE tape and enhanced specular reflector film were investigated as reference reflector materials. When 3 × 3 × 20 mm3 BGO pixels were wrapped with the yellow PTFE reflector, the CTR was significantly improved to 365 ± 5 ps from 403 ± 14 ps measured with the conventional white PTFE reflector. Adequate energy information was still obtained with only 4.1% degradation in light collection compared to the white reflector. Colored reflectors show the possibility to further improve CTR for BGO pixels with optimum reflectance design. [ABSTRACT FROM AUTHOR]

Objective. Positron emission tomography (PET) is a functional imaging widely used in various applications such as tumour detection. PET image reconstruction is an ill-posed inverse problem, and the model-based iterative reconstruction methods commonly used in clinical practice have disadvantages such as long time consumption and low signal-to-noise ratio, especially at low doses. Approach. In this study, we propose a deep learning-based reconstruction method that is capable of reconstructing images directly from low-count sinograms. Our network consists of two parts, a truncated inverse radon layer for implementing domain transform and a U-shaped network for image enhancement. Main result. We validated our method on both simulation data and real data. Compared to ordered subset expectation maximization with a post-Guassian filter, the structural similarity can be improved from 0.9357 to 0.9613 and the peak signal-to-noise ratio can be improved by 5 dB. Significance. The proposed method can directly convert low-count sinograms into PET images, while obtaining improved image quality and having less time consumption than iterative reconstruction algorithms and the state-of-the-art convolutional neural network. [ABSTRACT FROM AUTHOR]

Objective. Since medical images generated by medical devices have low spatial resolution and quality, fusion approaches on medical images can generate a fused image containing a more comprehensive range of different modal features to help physicians accurately diagnose diseases. Conventional methods based on deep learning for medical image fusion usually extract only local features without considering their global features, which often leads to the problem of unclear detail information in the final fused image. Therefore, medical image fusion is a challenging task of great relevance. Approach. This paper proposes a novel end-to-end medical image fusion model for PET and MRI images to achieve information interaction between different pathways, termed as hyper-densely connected compression-and-decomposition network based on trident dilated perception for PET and MRI image fusion (HyperTDP-Net). In particular, in the compression network, a dual residual hyper densely module is constructed to take full advantage of middle layer information. Moreover, we establish the trident dilated perception module to precisely determine the location information of features, and improve the feature representation capability of the network. In addition, we abandon the ordinary mean square error as the content loss function and propose a new content-aware loss consisting of structural similarity loss and gradient loss, so that the fused image not only contains rich texture details but also maintains sufficient structural similarity with the source images. Main results. The experimental dataset used in this paper is derived from multimodal medical images published by Harvard Medical School. Extensive experiments illustrate that our model contains more edge information and texture detail information in the fusion result than the 12 state-of-the-art fusion models and ablation study results demonstrate the effectiveness of three technical innovations. Significance. As medical images continue to be used in clinical diagnosis, our method is expected to be a tool that can effectively improve the accuracy of physician diagnosis and automatic machine detection. [ABSTRACT FROM AUTHOR]

We demonstrate the first ever recorded positron-emission tomography (PET) imaging and dosimetry of a FLASH proton beam at the Proton Center of the MD Anderson Cancer Center. Two scintillating LYSO crystal arrays, read out by silicon photomultipliers, were configured with a partial field of view of a cylindrical poly-methyl methacrylate (PMMA) phantom irradiated by a FLASH proton beam. The proton beam had a kinetic energy of 75.8 MeV and an intensity of about 3.5 × 1010 protons that were extracted over 101.5 ms-long spills. The radiation environment was characterized by cadmium–zinc–telluride and plastic scintillator counters. Preliminary results indicate that the PET technology used in our tests can efficiently record FLASH beam events. The instrument yielded informative and quantitative imaging and dosimetry of beam-activated isotopes in a PMMA phantom, as supported by Monte Carlo simulations. These studies open a new PET modality that can lead to improved imaging and monitoring of FLASH proton therapy. [ABSTRACT FROM AUTHOR]

Objective. Identifying the inter-crystal scatter (ICS) events and recovering the first interaction position enables the accurate determination of the line-of-response in positron emission tomography (PET). However, conventional silicon photomultiplier (SiPM) signal multiplexing methods based on two-dimensional (2D) charge-division circuits do not allow the detection of multiple gamma-ray interaction positions in a scintillation array coupled with a SiPM array. In this study, we propose a novel multiplexing method that can restore all the individual channel data from a smaller number of multiplexed channels using high-pass filters and neural networks. Approach. The number of output channels is reduced by summing the SiPM signals that have passed through high-pass filters with different time constants. Then, the signal amplitude of each SiPM channel is restored from the combined signal using an artificial neural network. This study explains the principle of this method in detail and demonstrates the results using 4:1 multiplexing as an example. The usefulness of this method was also demonstrated by its application in the identification of ICS events in 1-to-1 coupled LSO-SiPM PET detectors. Main results. The artificial neural network enabled accurate energy estimation for each SiPM channel. One of the high-pass filter sets with the lowest Cramér–Rao lower bound provided the best results, yielding R 2 value of 0.99 between the true and estimated signals. The energy and flood histograms generated using the best-estimated signals were in good agreement with the ground truth. Additionally, the proposed method accurately estimated 2D energy deposit distribution in the LSO crystal array, allowing ICS event identification. Significance. The proposed method is potentially useful for ICS event recovery with a reduced number of array signal readout channels from a SiPM array. [ABSTRACT FROM AUTHOR]

Objective. Accurate segmentation of head and neck (H&N) tumors is critical in radiotherapy. However, the existing methods lack effective strategies to integrate local and global information, strong semantic information and context information, and spatial and channel features, which are effective clues to improve the accuracy of tumor segmentation. In this paper, we propose a novel method called dual modules convolution transformer network (DMCT-Net) for H&N tumor segmentation in the fluorodeoxyglucose positron emission tomography/computed tomography (FDG-PET/CT) images. Approach. The DMCT-Net consists of the convolution transformer block (CTB), the squeeze and excitation (SE) pool module, and the multi-attention fusion (MAF) module. First, the CTB is designed to capture the remote dependency and local multi-scale receptive field information by using the standard convolution, the dilated convolution, and the transformer operation. Second, to extract feature information from different angles, we construct the SE pool module, which not only extracts strong semantic features and context features simultaneously but also uses the SE normalization to adaptively fuse features and adjust feature distribution. Third, the MAF module is proposed to combine the global context information, channel information, and voxel-wise local spatial information. Besides, we adopt the up-sampling auxiliary paths to supplement the multi-scale information. Main results. The experimental results show that the method has better or more competitive segmentation performance than several advanced methods on three datasets. The best segmentation metric scores are as follows: DSC of 0.781, HD95 of 3.044, precision of 0.798, and sensitivity of 0.857. Comparative experiments based on bimodal and single modal indicate that bimodal input provides more sufficient and effective information for improving tumor segmentation performance. Ablation experiments verify the effectiveness and significance of each module. Significance. We propose a new network for 3D H&N tumor segmentation in FDG-PET/CT images, which achieves high accuracy. [ABSTRACT FROM AUTHOR]

Objective. Blood pool region of interest (ROI) data extracted from the field of view of a PET scanner can be impacted by both dispersive and background effects. This circumstance compromises the ability to correctly extract the arterial input function (AIF) signal. The paper explores a novel approach to addressing this difficulty. Approach. The method involves representing the AIF in terms of the whole-body impulse response (IR) to the injection profile. Analysis of a collection/population of directly sampled arterial data sets allows the statistical behaviour of the tracer's impulse response to be evaluated. It is proposed that this information be used to develop a penalty term for construction of a data-adaptive method of regularisation estimator of the AIF when dispersive and/or background effects maybe impacting the blood pool ROI data. Main results. Computational efficiency of the approach derives from the linearity of the impulse response representation of the AIF and the ability to substantially rely on quadratic programming techniques for numerical implementation. Data from eight different tracers, used in PET cancer imaging studies, are considered. Sample image-based AIF extractions for brain studies with: 18F-labeled fluoro-deoxyglucose and fluoro-thymidine (FLT), 11C-labeled carbon dioxide (CO2) and 15O-labeled water (H2O) are presented. Results are compared to the true AIF based on direct arterial sampling. Formal numerical simulations are used to evaluate the performance of the AIF extraction method when the ROI data has varying amounts of contamination, in comparison to a direct approach that ignores such effects. It is found that even with quite small amounts of contamination, the mean squared error of the regularised AIF is significantly better than the error associated with direct use of the ROI data. Significance. The proposed IR-based AIF extraction scheme offers a practical methodological approach for situations where the available image ROI data may be contaminated by background and/or dispersion effects. [ABSTRACT FROM AUTHOR]

Objective. Simplified calculation approaches and geometries are usually adopted for salivary glands (SGs) dosimetry. Our aims were (i) to compare different dosimetry methods to calculate SGs absorbed doses (ADs) following [18F]-PSMA-1007 injection, and (ii) to assess the AD variation across patients and single SG components. Approach. Five patients with prostate cancer underwent sequential positron-emission tomography/computed tomography (PET/CT) acquisitions of the head and neck, 0.5, 2 and 4 h after [18F]-PSMA-1007 injection. Parotid and submandibular glands were segmented on CT to derive SGs volumes and masses, while PET images were used to derive Time-Integrated Activity Coefficients. Average ADs to single SG components or total SG (tSG) were calculated with the following methods: (i) direct Monte Carlo simulation with GATE/GEANT4 considering radioactivity in the entire PET/CT field-of-view (MC) or in the SGs only (MCsgo); (ii) spherical model (SM) of OLINDA/EXM 2.1, adopting either patient-specific or standard ICRP89 organ masses (SMstd); (iii) ellipsoidal model (EM); (iv) MIRD approach with organ S -factors from OLINDA/EXM 2.1 and OpenDose collaboration, with or without contribution from cross irradiation originating outside the SGs. The maximum percent AD difference across SG components (δ max) and across patients (Δmax) were calculated. Main results. Compared to MC, ADs to single SG components were significantly underestimated by all methods (average relative differences ranging between −11.9% and −30.5%). δ max values were never below 25%. The highest δ max (=702%) was obtained with SMstd. Concerning tSG, results within 10% of the MC were obtained only if cross-irradiation from the remainder of the body or from the remainder of the head was accounted for. The Δmax ranged between 58% and 78% across patients. Significance. Simple geometrical models for SG dosimetry considerably underestimated ADs compared to MC, particularly if neglecting cross-irradiation from neighboring regions. Specific masses of single SG components should always be considered given their large intra- and inter-patient variability. [ABSTRACT FROM AUTHOR]

Objective. Synthetic images generated by simulation studies have a well-recognized role in developing and evaluating imaging systems and methods. However, for clinically relevant development and evaluation, the synthetic images must be clinically realistic and, ideally, have the same distribution as that of clinical images. Thus, mechanisms that can quantitatively evaluate this clinical realism and, ideally, the similarity in distributions of the real and synthetic images, are much needed. Approach. We investigated two observer-study-based approaches to quantitatively evaluate the clinical realism of synthetic images. In the first approach, we presented a theoretical formalism for the use of an ideal-observer study to quantitatively evaluate the similarity in distributions between the real and synthetic images. This theoretical formalism provides a direct relationship between the area under the receiver operating characteristic curve, AUC, for an ideal observer and the distributions of real and synthetic images. The second approach is based on the use of expert-human-observer studies to quantitatively evaluate the realism of synthetic images. In this approach, we developed a web-based software to conduct two-alternative forced-choice (2-AFC) experiments with expert human observers. The usability of this software was evaluated by conducting a system usability scale (SUS) survey with seven expert human readers and five observer-study designers. Further, we demonstrated the application of this software to evaluate a stochastic and physics-based image-synthesis technique for oncologic positron emission tomography (PET). In this evaluation, the 2-AFC study with our software was performed by six expert human readers, who were highly experienced in reading PET scans, with years of expertise ranging from 7 to 40 years (median: 12 years, average: 20.4 years). Main results. In the ideal-observer-study-based approach, we theoretically demonstrated that the AUC for an ideal observer can be expressed, to an excellent approximation, by the Bhattacharyya distance between the distributions of the real and synthetic images. This relationship shows that a decrease in the ideal-observer AUC indicates a decrease in the distance between the two image distributions. Moreover, a lower bound of ideal-observer AUC = 0.5 implies that the distributions of synthetic and real images exactly match. For the expert-human-observer-study-based approach, our software for performing the 2-AFC experiments is available at https://apps.mir.wustl.edu/twoafc. Results from the SUS survey demonstrate that the web application is very user friendly and accessible. As a secondary finding, evaluation of a stochastic and physics-based PET image-synthesis technique using our software showed that expert human readers had limited ability to distinguish the real images from the synthetic images. Significance. This work addresses the important need for mechanisms to quantitatively evaluate the clinical realism of synthetic images. The mathematical treatment in this paper shows that quantifying the similarity in the distribution of real and synthetic images is theoretically possible by using an ideal-observer-study-based approach. Our developed software provides a platform for designing and performing 2-AFC experiments with human observers in a highly accessible, efficient, and secure manner. Additionally, our results on the evaluation of the stochastic and physics-based image-synthesis technique motivate the application of this technique to develop and evaluate a wide array of PET imaging methods. [ABSTRACT FROM AUTHOR]

Objective. Together with novel photodetector technologies and emerging electronic front-end designs, scintillator material research is one of the key aspects to obtain ultra-fast timing in time-of-flight positron emission tomography (TOF-PET). In the late 1990s, Cerium-doped lutetium–yttrium oxyorthosilicate (LYSO:Ce) has been established as the state-of-the-art PET scintillator due to its fast decay time, high light yield and high stopping power. It has been shown that co-doping with divalent ions, such as Ca 2+ and Mg 2+, is beneficial for its scintillation characteristics and timing performance. Therefore, this work aims to identify a fast scintillation material to combine it with novel photosensor technologies to push the state of the art in TOF-PET. Approach. This study evaluates commercially available LYSO:Ce,Ca and LYSO:Ce,Mg samples manufactured by Taiwan Applied Crystal Co., LTD regarding their rise and decay times as well as their coincidence time resolution (CTR) with both ultra-fast high-frequency (HF) readout and commercially available readout electronics, i.e. the TOFPET2 ASIC. Main results. The co-doped samples exhibit state-of-the-art rise times of on average 60 ps and effective decay times of on average 35 ns. Using the latest technological improvements made on NUV-MT SiPMs by Fondazione Bruno Kessler and Broadcom Inc., a 3 × 3 × 19 mm3 LYSO:Ce,Ca crystal achieves a CTR of 95 ps (FWHM) with ultra-fast HF readout and 157 ps (FWHM) with the system-applicable TOFPET2 ASIC. Evaluating the timing limits of the scintillation material, we even show a CTR of 56 ps (FWHM) for small 2 × 2 × 3 mm3 pixels. A complete overview of the timing performance obtained with different coatings (Teflon, BaSO4) and different crystal sizes coupled to standard Broadcom AFBR-S4N33C013 SiPMs will be presented and discussed. Significance. This work thoroughly evaluates commercially available co-doped LYSO:Ce crystals and, in combination with novel NUV-MT SiPMs, shows a TOF performance that significantly exceeds the current state of the art. [ABSTRACT FROM AUTHOR]

Objective. Time-of-flight positron emission tomography based on bismuth germanate (BGO) detectors is made possible due to fast emission of Cerenkov light. Only around 17 Cerenkov photons are produced per 511 keV photoelectric event, making high photon collection efficiency crucial for obtaining good time-of-flight capabilities. In this study, we investigate how different lateral and back surface finishes affect the photon collection efficiency and Cerenkov based timing performance in monolithic BGO. Approach. The study is performed using GATE for gamma and optical photon modeling, with surface reflections of photons simulated by the LUT Davis model. We compare for different detector configurations (regarding size and surface finishes) the photon collection efficiency, detection delays of the first few optical photons and coincidence time resolution estimations obtained by modeling the SiPM signals and performing leading edge discrimination. An additional comparison is made to LYSO scintillators and pixelated detectors. Main results. Although Cerenkov photon emission is directional, many high incidence angle Cerenkov photons are emitted due to electron scattering in the crystal. Substituting a polished back (photodetector side) surface for a rough surface increases the collection efficiency of these high angle of incidence photons. Results show that for a monolithic 50 × 50 × 12 mm3 BGO detector with reflective side surfaces, this leads to an overall increase in photon collection efficiency of 34%. Cerenkov photon collection efficiency is also improved, resulting in a reduction of the photon detection delays (and the variation therein) of the first few optical photons. This leads to a better coincidence time resolution, primarily achieved by a shortening of the tails in the time-of-flight kernel, with an 18% reduction in full width at tenth maximum. Significance. This study shows the importance of the photon collection efficiency for timing performance in Cerenkov based monolithic detectors, and how it can be improved with different surface finishes. [ABSTRACT FROM AUTHOR]

Objective. In this work, the irradiation of natural titanium foils in the beam-stop of a compact medical cyclotron, an IBA CYCLONE 18/9, is simulated to assess the efficacy of using a beam-stop as a target holder, and using two different target geometries, in the production of vanadium-48, a positron-emitting radioisotope with potential utility as a cancer imaging agent in positron emission tomography. Approach. TOPAS, the TOol for PArticle Simulation, a Geant4-based Monte Carlo program, was used to model the cyclotron beam parameters, choose an appropriate physics list, and simulate the irradiation of targets made from foils of 12 or 12.5 μ m thickness. These simulation yields were compared to theoretical yields calculated using cross section data from the literature, as well as assayed yields from experimental irradiations. Main results. We found that most physics lists in TOPAS overestimate the cross section in the desired energy range (16–20 MeV) by at least 136%, with the exception of those using the Bertini Cascade Model. Compared to assayed yields, TOPAS provided a minimum of 0.4% error for cup-shaped targets and at least a 12% overestimation for sphere-shaped targets. Significance. These simulations provide a tool to help explain irregularities in radioisotope production yield and motivate modifications to increase target yield. [ABSTRACT FROM AUTHOR]

Objective. The aim of this study is to evaluate the performance characteristics of a brain positron emission tomography (PET) scanner composed of four-layer independent read-out time-of-flight depth-of-interaction (TOF-DOI) detectors capable of first interaction position (FIP) detection, using Geant4 application for tomographic emission(GATE). This includes the spatial resolution, sensitivity, count rate capability, and reconstructed image quality. Approach. The proposed TOF-DOI PET detector comprises four layers of a 50 × 50 cerium-doped lutetium–yttrium oxyorthosilicate (LYSO:Ce) scintillator array with 1 mm pitch size, coupled to a 16 × 16 multi-pixel photon counter array with 3.0 mm × 3.0 mm photosensitive segments. Along the direction distant from the center field-of-view (FOV), the scintillator thickness of the four layers is 2.5, 3, 4, and 6 mm. The four layers were simulated with a 150 ps coincidence time resolution and the independent readout make the FIP detection capable. The spatial resolution and imaging performance were compared among the true-FIP, winner-takes-all (WTA) and front-layer FIP (FL-FIP) methods (FL-FIP selects the interaction position located on the front-most interaction layer in all the interaction layers). The National Electrical Manufacturers Association NU 2-2018 procedure was referred and modified to evaluate the performance of proposed scanner. Main results. In detector evaluation, the intrinsic spatial resolutions were 0.52 and 0.76 mm full width at half-maximum (FWHM) at 0° and 30° incident γ -rays in the first layer pair, respectively. The reconstructed spatial resolution by the filter backprojection (FBP) achieved sub-millimeter FWHM on average over the whole FOV. The maximum true count rate was 207.6 kcps at 15 kBq ml−1 and the noise equivalent count rate (NECR_2R) was 54.7 kcps at 6.0 kBq ml−1. Total sensitivity was 45.2 cps kBq−1 and 48.4 cps kBq−1 at the center and 10 cm off-center FOV, respectively. The TOF and DOI reconstructions significantly improved the image quality in the phantom studies. Moreover, the FL-FIP outperformed the conventional WTA method in terms of the spatial resolution and image quality. Significance. The proposed brain PET scanner could achieve sub-millimeter spatial resolution and high image quality with TOF and DOI reconstruction, which is meaningful to the clinical oncology research. Meanwhile, the comparison among the three positioning methods indicated that the FL-FIP decreased the image degradation caused by Compton scatter more than WTA. [ABSTRACT FROM AUTHOR]

Objective. Beams of stable ions have been a well-established tool for radiotherapy for many decades. In the case of ion beam therapy with stable 12C ions, the positron emitters 10,11C are produced via projectile and target fragmentation, and their decays enable visualization of the beam via positron emission tomography (PET). However, the PET activity peak matches the Bragg peak only roughly and PET counting statistics is low. These issues can be mitigated by using a short-lived positron emitter as a therapeutic beam. Approach. An experiment studying the precision of the measurement of ranges of positron-emitting carbon isotopes by means of PET has been performed at the FRS fragment-separator facility of GSI Helmholtzzentrum für Schwerionenforschung GmbH, Germany. The PET scanner used in the experiment is a dual-panel version of a Siemens Biograph mCT PET scanner. Main results. High-quality in-beam PET images and activity distributions have been measured from the in-flight produced positron emitting isotopes 11C and 10C implanted into homogeneous PMMA phantoms. Taking advantage of the high statistics obtained in this experiment, we investigated the time evolution of the uncertainty of the range determined by means of PET during the course of irradiation, and show that the uncertainty improves with the inverse square root of the number of PET counts. The uncertainty is thus fully determined by the PET counting statistics. During the delivery of 1.6 × 107 ions in 4 spills for a total duration of 19.2 s, the PET activity range uncertainty for 10C, 11C and 12C is 0.04 mm, 0.7 mm and 1.3 mm, respectively. The gain in precision related to the PET counting statistics is thus much larger when going from 11C to 10C than when going from 12C to 11C. The much better precision for 10C is due to its much shorter half-life, which, contrary to the case of 11C, also enables to include the in-spill data in the image formation. Significance. Our results can be used to estimate the contribution from PET counting statistics to the precision of range determination in a particular carbon therapy situation, taking into account the irradiation scenario, the required dose and the PET scanner characteristics. [ABSTRACT FROM AUTHOR]

This document is a correction notice for an article titled "Virtual cylindrical PET for efficient DOI image reconstruction with sub-millimetre resolution." The authors of the article acknowledge that they omitted a reference to a previous study by Scheins et al. Although Scheins et al. did not specifically address depth-of-interaction (DOI) PET reconstruction, their work on virtual cylinders laid the groundwork for the implementation of virtual cylinders in DOI PET reconstruction. The authors provide the correct reference and emphasize the importance of acknowledging Scheins et al.'s contribution in this context. [Extracted from the article]

Objective. In carbon ion therapy, the visualization of the range of incident particles in a patient body is important for treatment verification. In-beam positron emission tomography (PET) imaging is one of the methods to verify the treatment in ion therapy due to the high quality of PET images. We have shown the feasibility of in-beam PET imaging of radioactive 15O and 11C ion beams for range verification using our OpenPET system. Recently, we developed a whole gamma imager (WGI) that can simultaneously work as PET, single gamma ray and triple gamma ray imaging. The WGI has high potential to detect the location of 10C, which emits positrons with a simultaneous gamma ray of 718 keV, within the patient’s body during ion therapy. Approach. In this work, we focus on investigating the performance of WGI for 10C imaging and its feasibility for range verification in carbon ion therapy. First, the performance of the WGI was studied to image a 10C point source using the Geant4 toolkit. Then, the feasibility of WGI was investigated for an irradiated polymethyl methacrylate (PMMA) phantom with a 10C ion beam at the carbon therapy facility of the Heavy Ion Medical Accelerator in Chiba. Main results. The average spatial resolution and sensitivity for the simulated 10C point source at the centre of the field of view were 5.5 mm FWHM and 0.010%, respectively. The depth dose of the 10C ion beam was measured, and the triple gamma image of 10C nuclides for an irradiated PMMA phantom was obtained by applying a simple back projection to the detected triple gammas. Significance. The shift between Bragg peak position and position of the peak of the triple gamma image in an irradiated PMMA phantom was 2.8 ± 0.8 mm, which demonstrates the capability of triple gamma imaging using WGI for range verification of 10C ion beams. [ABSTRACT FROM AUTHOR]

In this paper, the authors review the field of motion detection and correction in nuclear cardiology with single photon emission computed tomography (SPECT) and positron emission tomography (PET) imaging systems. We start with a brief overview of nuclear cardiology applications and description of SPECT and PET imaging systems, then explaining the different types of motion and their related artefacts. Moreover, we classify and describe various techniques for motion detection and correction, discussing their potential advantages including reference to metrics and tasks, particularly towards improvements in image quality and diagnostic performance. In addition, we emphasize limitations encountered in different motion detection and correction methods that may challenge routine clinical applications and diagnostic performance. [ABSTRACT FROM AUTHOR]

Objective: Dual-ended readout depth-encoding detectors based on bismuth germanate (BGO) scintillation crystal arrays are good candidates for high-sensitivity small animal positron emission tomography used for very-low-dose imaging. In this paper, the performance of three dual-ended readout detectors based on 15 × 15 BGO arrays with three different reflector arrangements and 8 × 8 silicon photomultiplier arrays were evaluated and compared. Approach: The three BGO arrays, denoted wo-ILG (without internal light guide), wp-ILG (with partial internal light guide), and wf-ILG (with full internal light guide), share a pitch size of 1.6 mm and thickness of 20 mm. Toray E60 with a thickness of 50 μ m was used as inter-crystal reflector. All reflector lengths in the wo-ILG and wf-ILG BGO arrays were 20 and 18 mm, respectively; the reflectors in the wp-ILG BGO array were 18 mm at the central region of the array and 20 mm at the edge. By using 18 mm reflectors, part of the crystals in the wp-ILG and wf-ILG BGO arrays worked as internal light guides. Main results: The results showed that the detector based on the wo-ILG BGO array provided the best flood histogram. The energy, timing and DOI resolutions of the three detectors were similar. The energy resolutions full width at half maximum (FWHM value) based on the wo-ILG, wp-ILG and wf-ILG BGO arrays were 27.2 ± 3.9%, 28.7 ± 4.6%, and 29.5 ± 4.7%, respectively. The timing resolutions (FWHM value) were 4.7 ± 0.5 ns, 4.9 ± 0.5 ns, and 5.0 ± 0.6 ns, respectively. The DOI resolution (FWHM value) were 3.0 ± 0.2 mm, 2.9 ± 0.2 mm, and 3.0 ± 0.2 mm, respectively. Over all, the wo-ILG detector provided the best performance. [ABSTRACT FROM AUTHOR]

Patient motion impacts single photon emission computed tomography (SPECT), positron emission tomography (PET) and x-ray computed tomography (CT) by giving rise to projection data inconsistencies that can manifest as reconstruction artifacts, thereby degrading image quality and compromising accurate image interpretation and quantification. Methods to estimate and correct for patient motion in SPECT, PET and CT have attracted considerable research effort over several decades. The aims of this effort have been two-fold: to estimate relevant motion fields characterizing the various forms of voluntary and involuntary motion; and to apply these motion fields within a modified reconstruction framework to obtain motion-corrected images. The aims of this review are to outline the motion problem in medical imaging and to critically review published methods for estimating and correcting for the relevant motion fields in clinical and preclinical SPECT, PET and CT. Despite many similarities in how motion is handled between these modalities, utility and applications vary based on differences in temporal and spatial resolution. Technical feasibility has been demonstrated in each modality for both rigid and non-rigid motion but clinical feasibility remains an important target. There is considerable scope for further developments in motion estimation and correction, and particularly in data-driven methods that will aid clinical utility. State-of-the-art deep learning methods may have a unique role to play in this context. [ABSTRACT FROM AUTHOR]

A multilayer depth-of-interaction positron emission tomography DOI PET) detector with an independent readout structure has a potential advantage as a time-of-flight (TOF) PET detector. The thin scintillator block of each detector layer can afford an improved coincidence time resolution (CTR) of ∼100 ps because the photon transfer time spread within the scintillator inherently decreases. To evaluate the potential TOF capabilities of a multilayer DOI-PET detector, which consists of thin layers of a cerium-doped lutetium–yttrium oxyorthosilicate (LYSO:Ce) scintillator coupled to a multi-pixel photon counter (MPPC) array, we examined the detector's CTR performance via Monte Carlo simulations. We used several types of scintillator structures: a monolithic plate, laser-processing array with 3.2 mm pitch, fine laser-processing array with 1.6 mm pitch, and pixelated array with 3.2 mm pitch, with 2, 4, 6, and 8 mm thickness values of a 25.6 mm 25.6 mm scintillator cross-section. The MPPC array was composed of 3.0 mm 3.0 mm photosensitive segments arranged in an 8 × 8 array. Here, we note that the CTR performance also significantly depends on the timing detection method, which generates a timing trigger signal for coincidence detection. Thus, we evaluated the CTRs for each scintillator structure by adopting four timing detection methods: using the total sum signal of 64 MPPC chips (T sum), the maximum signal in the 64 MPPC chips (Max), the sum signal of a partial number of MPPC chips located at and in the vicinity of the γ-ray interaction position (P sum), and the average of the timestamps generated at several MPPC chips (Ave). When using the T sum for timing detection, the CTR full width at half-maximum values were ∼100 ps regardless of the scintillator structure. However, when using the Max signal approach, the CTRs of the monolithic plates, laser-processing arrays, and fine-pitch laser-processing arrays were drastically degraded with increasing thickness. On the other hand, the CTRs of the pixelated arrays exhibited almost no degradation. To improve the CTRs of the monolithic plate and the (fine-pitch) laser-processing array that exhibit a large light spread in the scintillator block, we applied the P sum and Ave methods. The resulting CTRs significantly improved upon using P sum; however, in the Ave approach the improvement effect disappeared when the thickness was 6 mm in case of our simulation. [ABSTRACT FROM AUTHOR]

Dynamic whole body (DWB) PET acquisition protocols enable the use of whole body parametric imaging for clinical applications. In FDG imaging, accurate parametric images of Patlak Ki can be complementary to regular standardised uptake value images and improve on current applications or enable new ones. In this study we consider DWB protocols implemented on clinical scanners with a limited axial field of view with the use of multiple whole body sweeps. These protocols result in temporal gaps in the dynamic data which produce noisier and potentially more biased parametric images, compared to single bed (SB) dynamic protocols. Dynamic reconstruction using the Patlak model has been previously proposed to overcome these limits and shown improved DWB parametric images of Ki. In this work, we propose and make use of a spectral analysis based model for dynamic reconstruction and parametric imaging of Patlak Ki. Both dynamic reconstruction methods were evaluated for DWB FDG protocols and compared against 3D reconstruction based parametric imaging from SB dynamic protocols. This work was conducted on simulated data and results were tested against real FDG dynamic data. We showed that dynamic reconstruction can achieve levels of parametric image noise and bias comparable to 3D reconstruction in SB dynamic studies, with the spectral model offering additional flexibility and further reduction of image noise. Comparisons were also made between step and shoot and continuous bed motion (CBM) protocols, which showed that CBM can achieve lower parametric image noise due to reduced acquisition temporal gaps. Finally, our results showed that dynamic reconstruction improved VOI parametric mean estimates but did not result to fully converged values before resulting in undesirable levels of noise. Additional regularisation methods need to be considered for DWB protocols to ensure both accurate quantification and acceptable noise levels for clinical applications. [ABSTRACT FROM AUTHOR]

Inter-crystal scattering (ICS) is a type of Compton scattering of photons from one crystal to adjacent crystals and causes inaccurate assignment of the annihilation photon interaction position in positron emission tomography (PET). Because ICS frequently occurs in highly light-shared PET detectors, its recovery is crucial for the spatial resolution improvement. In this study, we propose two different convolutional neural networks (CNNs) for ICS recovery, exploiting the good pattern recognition ability of CNN techniques. Using the signal distribution of a photosensor array as input, one network estimates the energy deposition in each crystal (ICS-eNet) and another network chooses the first-interacted crystal (ICS-cNet). We performed GATE Monte Carlo simulations with optical photon tracking to test PET detectors comprising different crystal arrays (8 × 8 to 21 × 21) with lengths of 20 mm and the same photosensor array (3 mm 8 × 8 array) covering an area of 25.8 × 25.8 mm2. For each detector design, we trained ICS-eNet and ICS-cNet and evaluated their respective performance. ICS-eNet accurately identified whether the events were ICS (accuracy > 90%) and selected interacted crystals (accuracy > 60%) with appropriate energy estimation performance (R2 > 0.7) in the 8 × 8, 12 × 12, and 16 × 16 arrays. ICS-cNet also exhibited satisfactory performance, which was less dependent on the crystal-to-sensor ratio, with an accuracy enhancement that exceeds 10% in selecting the first-interacted crystal and a reduction in error distances compared when no recovery was applied. Both ICS-eNet and ICS-cNet exhibited consistent performances under various optical property settings of the crystals. For spatial resolution measurements in PET rings, both networks achieved significant enhancements particularly for highly pixelated arrays. We also discuss approaches for training the networks in an actual experimental setup. This proof-of-concept study demonstrated the feasibility of CNNs for ICS recovery in various light-sharing designs to efficiently improve the spatial resolution of PET in various applications. [ABSTRACT FROM AUTHOR]

Monte Carlo simulations (MCS) represent a fundamental approach to modelling the photon interactions in positron emission tomography (PET). A variety of PET-dedicated MCS tools are available to assist and improve PET imaging applications. Of these, GATE has evolved into one of the most popular software for PET MCS because of its accuracy and flexibility. However, simulations are extremely time-consuming. The use of graphics processing units (GPU) has been proposed as a solution to this, with reported acceleration factors about 400–800. These factors refer to GATE benchmarks performed on a single CPU core. Consequently, CPU-based MCS can also be easily accelerated by one order of magnitude or beyond when exploiting multi-threading on powerful CPUs. Thus, CPU-based implementations become competitive when further optimisations can be achieved. In this context, we have developed a novel, CPU-based software called the PET physics simulator (PPS), which combines several efficient methods to significantly boost the performance. PPS flexibly applies GEANT4 cross-sections as a pre-calculated database, thus obtaining results equivalent to GATE. This is demonstrated for an elaborated PET scanner with 3-layer block detectors. All code optimisations yield an acceleration factor of ≈20 (single core). Multi-threading on a high-end CPU workstation (96 cores) further accelerates the PPS by a factor of 80. This results in a total speed-up factor of ≈1600, which outperforms comparable GPU-based MCS by a factor of ≳2. Optionally, the proposed method of coincidence multiplexing can further enhance the throughput by an additional factor of ≈15. The combination of all optimisations corresponds to an acceleration factor of ≈24 000. In this way, the PPS can simulate complex PET detector systems with an effective throughput of 106 photon pairs in less than 10 milliseconds. [ABSTRACT FROM AUTHOR]

Sensitivity and spatial resolution of positron emission tomography (PET) scanners can be improved by using thicker scintillation crystals with depth-of-interaction (DOI) encoding. Subsurface laser engraving (SSLE) can be used to segment crystals of a scintillation detector in order to fabricate a DOI detector. We previously applied SSLE to crystal bars of 3 × 3 × 20 mm3 and 1.5 × 1.5 × 20 mm3 and developed two dual-ended detectors with DOI segments of 3 mm and 1.5 mm, respectively. To further improve the DOI resolution, our SSLE detector design can be used with smaller pitch crystal bars, making them excellent detector candidates for small animal PET scanners with submillimetre resolution. In the present study, three small crystal bars of 1 × 1 × 20 mm3, 2 × 1 × 20 mm3, and 2 × 1 × 40 mm3 were laser engraved to 12, 20 and 40 segments, respectively, by applying SSLE in their height directions. The segmented crystal bars were characterised in three prototype detector arrangements. First, the 1 × 1 × 20 mm3 crystal bars were characterised in an 8 × 8 crystal array designed for DOI encoding along crystal height in a conventional small animal PET design. Second, a 4 × 8 crystal array of 2 × 1 × 20 mm3 crystal bars was characterised for using the DOI information for crystal interaction positioning along the axial axis of a small animal PET scanner. Finally, the third part of the study was performed on a single 2 × 1 × 40 mm3 crystal bar with 40 segments to investigate the feasibility of DOI estimation in longer crystals for application in a system with extended axial length. We evaluated the capability of segment identification and energy resolution of theses detectors. The 3D position maps of the detectors were obtained using the Anger-type calculation and the crystal identification performance was evaluated for each detector. Clear segment separation was obtained for the crystal arrays with 12 (segment pitch of 1.67 mm) and 20 (segment pitch of 1 mm) segments. Mean energy resolutions of 8.8% ± 0.4% and 9.6% ± 0.8% at 511 keV were obtained for the segments in the central regions of the 8 × 8 array with 12 segments and the 4 × 8 array with 20 segments, respectively. Clear segment identification was found to be difficult for the detector with 40 segments, especially for the segments at the middle of the crystal. Energy and interaction positioning characterisation results suggest that both prototype detectors with 12 and 20 segments are well suited for small animal PET scanners with high spatial resolution. [ABSTRACT FROM AUTHOR]

The construction of photon propagation has a close relationship with the quality of reconstructed images. The classical Monte Carlo (MC) based method can model the photon propagation precisely, but it is time-consuming. The analytical method can often quickly construct a model, but its precision is a problem. How to fully exploit the advantages of the MC simulation and analytical model is an open problem. Inspired by the characteristics of the depth of interaction (DOI) detectors, which can help confirm the deposited position of a photon wit encoding technology, we virtually discretize each crystal into several subcrystals to obtain the statistical distribution by MC-based simulation. Then, the statistical distribution is combined with a spatially variant solid-angle model. This combination strategy provides a hybrid model to describe photon propagation with relatively high accuracy and low computational cost. Three discretization schemes are compared to optimize the constructed photon propagation model. Several experiments are carried out to evaluate the performance of the proposed hybrid method. The metrics of full width at half maximum (FWHM), contrast recovery (CR), and coefficient of variation (COV) are adopted to quantitate the imaging results. The classical MC-based method is compared as a gold-standard reference. When a crystal is divided into two discretized positions, the convergent tendencies of CRs and COVs are consistent with that based on MC simulation method, respectively. In terms of FWHMs, the resolutions of using the MC-based model and the proposed hybrid model are 0.71 mm and 0.68 mm in the direction parallel to the detector head, respectively. This indicates the potential of the proposed method in positron emission tomography imaging. [ABSTRACT FROM AUTHOR]

Conventional positron emission tomography (PET) image reconstruction is achieved by the statistical iterative method. Deep learning provides another opportunity for speeding up the image reconstruction process. However, conventional deep learning-based image reconstruction requires a fully connected network for learning the Radon transform. The use of fully connected networks greatly complicated the network and increased hardware cost. In this study, we proposed a novel deep learning-based image reconstruction method by utilizing the DIRECT data partitioning method. The U-net structure with only convolutional layers was used in our approach. Patch-based model training and testing were used to achieve 3D reconstructions within current hardware limitations. Time-of-flight (TOF)-histoimages were first generated from the listmode data to replace conventional sinograms. Different projection angles were used as different channels in the input. A total of 15 patient data were used in this study. For each patient, the dynamic whole-body scanning protocol was used to expand the training dataset and a total of 372 separate scans were included. The leave-one-patient-out validation method was used. Two separate studies were carried out. In the first study, the measured TOF-histoimages were directly used for model training and testing, to study the performance of the method in real-world applications. In the second study, TOF-histoimages were simulated from already reconstructed images to exclude the scatters, randoms, attenuation-activity mismatch effects. This study was used to evaluate the optimal performance when all other corrections are ideal. Volumes of interests were placed in the liver and lesion region to study image noise and lesion quantitations. The reconstructed images using the proposed deep learning method showed similar image quality when compared with the conventional expectation-maximization approach. A minimal difference was observed when the simulated TOF-histoimages were used as model input and testing, suggesting the deep learning model can indeed learn the reconstruction process. Some quantitative difference was observed when the measured TOF-histoimages were used. The two studies suggested that the major difference is caused by inaccurate corrections performed by the network itself, which indicated that physics-based corrections are still required for better quantitative performance. In conclusion, we have proposed a novel deep learning-based image reconstruction method for TOF PET. With the help of the DIRECT data partitioning method, no fully connected layers were used and 3D image reconstruction can be directly achieved within the limits of the current hardware. [ABSTRACT FROM AUTHOR]

We investigate the reconstruction of low-count positron emission tomography (PET) projection, which is an important, but challenging, task. Using the texture feature extraction method of radiomics, i.e. the gray-level co-occurrence matrix (GLCM), texture features can be extracted from magnetic resonance imaging images with high-spatial resolution. In this work, we propose a kernel reconstruction method combining autocorrelation texture features derived from the GLCM. The new kernel function includes the correlations of both the intensity and texture features from the prior image. By regarding the GLCM as a discrete approximation of a probability density function, the asymptotically gray-level-invariant autocorrelation texture feature is generated, which can maintain the accuracy of texture features extracted from small image regions by reducing the number of quantized image gray levels. A computer simulation shows that the proposed method can effectively reduce the noise in the reconstructed image compared to the maximum likelihood expectation maximum method and improve the image quality and tumor region accuracy compared to the original kernel method for low-count PET reconstruction. A simulation study on clinical patient images also shows that the proposed method can improve the whole image quality and that the reconstruction of a high-uptake lesion is more accurate than that achieved by the original kernel method. [ABSTRACT FROM AUTHOR]

The system spatial resolution of whole-body positron emission tomography (PET) is limited to around 2 mm due to positron physics and the large diameter of the bore. To stay below this 'physics'-limit a scintillation detector with an intrinsic spatial resolution of around 1.3 mm is needed. Currently used detector technology consists of arrays of 2.6–5 mm segmented scintillator pixels which are the dominant factor contributing to the system resolution. Pixelated detectors using smaller pixels exist but face major drawbacks in sensitivity, timing, energy resolution and cost. Monolithic continuous detectors, where the spatial resolution is determined by the shape of the light distribution on the photodetector array, are a promising alternative. Without having the drawbacks of pixelated detectors, monolithic ones can also provide depth-of-interaction (DOI) information. In this work we present a monolithic detector design aiming to serve high-resolution clinical PET systems while maintaining high sensitivity. A 50 × 50 × 16 mm3 Lutetium–Yttrium oxyorthosilicate scintillation crystal with silicon photomultiplier (SiPM) backside readout is calibrated in singles mode by a collimated beam obtaining a reference dataset for the event positioning. A mean nearest neighbour (MNN) algorithm and an artificial neural network for positioning are compared. The targeted intrinsic detector resolution of 1.3 mm needed to reach a 2 mm resolution on system level was accomplished with both algorithms. The neural network achieved a mean spatial resolution of 1.14 mm FWHM for the whole detector and 1.02 mm in the centre (30 × 30 mm2). The MNN algorithm performed slightly worse with 1.17 mm for the whole detector and 1.13 mm in the centre. The intrinsic DOI information will also result in uniform system spatial resolution over the full field of view. [ABSTRACT FROM AUTHOR]

Myocardial blood flow (MBF) and flow reserve are usually quantified in the clinic with positron emission tomography (PET) using a perfusion-specific radiotracer (e.g. 82Rb-chloride). However, the clinical accessibility of existing perfusion tracers remains limited. Meanwhile, 18F-fluorodeoxyglucose (FDG) is a commonly used radiotracer for PET metabolic imaging without similar limitations. In this paper, we explore the potential of 18F-FDG for myocardial perfusion imaging by comparing the myocardial FDG delivery rate K1 with MBF as determined by dynamic 82Rb PET in fourteen human subjects with heart disease. Two sets of FDG K1 were derived from one-hour dynamic FDG scans. One was the original FDG K1 estimates and the other was the corresponding K1 values that were linearly normalized for blood glucose levels. A generalized Renkin-Crone model was used to fit FDG K1 with Rb MBF, which then allowed for a nonlinear extraction fraction correction for converting FDG K1 to MBF. The linear correlation between FDG-derived MBF and Rb MBF was moderate (r = 0.79) before the glucose normalization and became much improved (r > 0.9) after glucose normalization. The extraction fraction of FDG was also similar to that of Rb-chloride in the myocardium. The results from this pilot study suggest that dynamic cardiac FDG-PET with tracer kinetic modeling has the potential to provide MBF in addition to its conventional use for metabolic imaging. [ABSTRACT FROM AUTHOR]

Our study aims to improve the signal-to-noise ratio of positron emission tomography (PET) imaging using conditional unsupervised learning. The proposed method does not require low- and high-quality pairs for network training which can be easily applied to existing PET/computed tomography (CT) and PET/magnetic resonance (MR) datasets. This method consists of two steps: populational training and individual fine-tuning. As for populational training, a network was first pre-trained by a group of patients' noisy PET images and the corresponding anatomical prior images from CT or MR. As for individual fine-tuning, a new network with initial parameters inherited from the pre-trained network was fine-tuned by the test patient's noisy PET image and the corresponding anatomical prior image. Only the last few layers were fine-tuned to take advantage of the populational information and the pre-training efforts. Both networks shared the same structure and took the CT or MR images as the network input so that the network output was conditioned on the patient's anatomic prior information. The noisy PET images were used as the training and fine-tuning labels. The proposed method was evaluated on a 68Ga-PPRGD2 PET/CT dataset and a 18F-FDG PET/MR dataset. For the PET/CT dataset, with the original noisy PET image as the baseline, the proposed method has a significantly higher contrast-to noise ratio (CNR) improvement (71.85% ± 27.05%) than Gaussian (12.66% ± 6.19%, P = 0.002), nonlocal mean method (22.60% ± 13.11%, P = 0.002) and conditional deep image prior method (52.94% ± 21.79%, P = 0.0039). For the PET/MR dataset, compared to Gaussian (18.73% ± 9.98%, P < 0.0001), NLM (26.01% ± 19.40%, P < 0.0001) and CDIP (47.48% ± 25.36%, P < 0.0001), the CNR improvement ratio of the proposed method (58.07% ± 28.45%) is the highest. In addition, the denoised images using both datasets also showed that the proposed method can accurately restore tumor structures while also smoothing out the noise. [ABSTRACT FROM AUTHOR]