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Purpose: To systematically investigate kinetic metrics and metabolic trapping of [13N]NH3 in organs. Methods: Eleven participants performed total-body [13N]NH3 dynamic positron emission tomography (PET). Regions of interest were drawn in organs to obtain time-to-activity curves (TACs), which were fitted with an irreversible two-tissue compartment model (2TC) to investigate constant rates K1, k2 and k3, and to calculate Ki. Additionally, one-tissue compartment model using full data (1TCfull) and the first four minutes of data (1TC4min) were fitted to TAC data. K1 and k2 were compared among different models to assess [13N]NH3 trapping in organs. Results: Kinetic rates of [13N]NH3 varied significantly among organs. The mean K1 ranged from 0.049 mL/cm3/min in the muscle to 2.936 mL/cm3/min in the kidney. The k2 and k3 were lowest in the liver (0.001 min− 1) and in the pituitary (0.009 min− 1), while highest in the kidney (0.587 min− 1) and in the liver (0.800 min− 1), respectively. The Ki was largest in the myocardium (0.601 ± 0.259 mL/cm3/min) while smallest in the bone marrow (0.028 ± 0.022 mL/cm3/min). Three groups of organs with similar kinetic characteristics were revealed: (1) the thyroid, the lung, the spleen, the pancreas, and the kidney; (2) the liver and the muscle; and (3) the cortex, the white matter, the cerebellum, the pituitary, the parotid, the submandibular gland, the myocardium, the bone, and the bone marrow. Obvious k3 was identified in multiple organs, and significant changes of K1 in multiple organs and k2 in most organs were found between 2TC and 1TCfull, but both K1 and k2 were comparable between 2TC and 1TC4min. Conclusion: The kinetic rates of [13N]NH3 differed among organs with some have obvious 13N-anmmonia trapping. The normal distribution of kinetic metrics of 13N-anmmonia in organs can serve as a reference for its potential use in tumor imaging. [ABSTRACT FROM AUTHOR]

There is limited information on utilizing commercially supplied [18F]Fluoride from an off-site cyclotron for the synthesis of radiopharmaceuticals. This study explored the production of the PET hypoxia marker [18F]FMISO, using the Scintomics® GRP module and distantly produced [18F]Fluoride, which lacks published data. A radiochemical yield of 27.4 ± 3.6% (n = 5) and high radiochemical purity were achieved in synthesis time of 48 min. The [18F]FMISO met all Ph. Eur. standards, demonstrating the consistency and reliability of the module in generating a clinical-grade radiopharmaceutical using off-site produced [18F]Fluoride, but available patient doses were severely limited. [ABSTRACT FROM AUTHOR]

The current generation of total-body positron emission tomography (PET) scanners offer significant sensitivity increase with an extended axial imaging extent. With the large volume of lutetium-based scintillation crystals that are used as detector elements in these scanners, there is an increased flux of background radiation originating from 176Lu decay in the crystals and higher sensitivity for detecting it. Combined with the ability of scanning the entire body in a single bed position, this allows more effective utilization of the lutetium background as a transmission source for estimating 511 keV attenuation coefficients. In this study, utilization of the lutetium background radiation for attenuation correction in total-body PET was studied using Monte Carlo simulations of a 3D whole-body XCAT phantom in the uEXPLORER PET scanner, with particular focus on ultralow-dose PET scans that are now made possible with these scanners. Effects of an increased acceptance angle, reduced scan durations, and Compton scattering on PET quantification were studied. Furthermore, quantification accuracy of lutetium-based attenuation correction was compared for a 20-min scan of the whole body on the uEXPLORER, a one-meter-long, and a conventional 24-cm-long scanner. Quantification and lesion contrast were minimally affected in both long axial field-of-view scanners and in a whole-body 20-min scan, the mean bias in all analyzed organs of interest were within a ±10% range compared to ground-truth activity maps. Quantification was affected in certain organs, when scan duration was reduced to 5 min or a reduced acceptance angle of 17 was used. Analysis of the Compton scattered events suggests that implementing a scatter correction method for the transmission data will be required, and increasing the energy threshold from 250 keV to 290 keV can reduce the computational costs and data rates, with negligible effects on PET quantification. Finally, the current results can serve as groundwork for transferring lutetium-based attenuation correction into research and clinical practice. [ABSTRACT FROM AUTHOR]

ABSTRACT Background Objectives Methods Results Conclusions Pediatric‐onset Huntington's disease (POHD) exhibits a phenotype different from adult‐onset HD (AOHD), with hypokinetic movement disorders (eg, rigidity, bradykinesia, and dystonia) rather than chorea typical of AOHD.The aim was to identify pathophysiology‐based biomarkers specific to POHD (≥60 CAG repeats).Simultaneous hybrid imaging using [18F]fluoro‐2‐deoxy‐d‐glucose (FDG) positron emission tomography plus magnetic resonance imaging (FDG‐PET/MRI) and clinical assessment using standardized Huntington's disease (HD) scales were employed. Exploratory longitudinal analyses were also performed.Striatal volume loss was remarkable and more severe in POHD (n = 5) than in AOHD (n = 14). Widespread, significantly altered glucose metabolism occurred in several different POHD cortical areas and thalamus, but not AOHD cortex, consistent with differences in clinical progression.POHD patients' brains exhibited distinct morphologic and metabolic traits compared to AOHD patients’ brains, with longitudinal changes mirroring clinical progression. Hybrid FDG‐PET/MRI highlighted a variable regional brain dysfunction in vivo, as a biological consequence of highly expanded CAG repeats. Findings provide further evidence that POHD is a distinct disease from AOHD. [ABSTRACT FROM AUTHOR]

Background: Monolithic or semi‐monolithic detectors are attractive for positron emission tomography (PET) scanners with depth‐of‐interaction (DOI) capability. However, they often require complicated calibrations to determine the interaction positions of gamma photons. Purpose: We introduce a novel hybrid detector design that combines pixelated and semi‐monolithic elements to achieve DOI capability while simplifying the calibrations for positioning. Methods: A prototype detector with eight hybrid lutetium–yttrium oxyorthosilicate (LYSO) layers having dimensions of 25.8 × 12.9 × 15 mm3 was constructed. The energy‐weighted and energy‐squared weighted averages were used for estimating the x‐ (pixelated direction) and y‐positions (non‐pixelated direction). Pseudo‐pixels were defined as discrete areas on the flood image based on the crystal look‐up table (LUT). The intrinsic spatial resolutions in the pixelated and non‐pixelated directions were measured. The ratio of the maximum to the sum of the multipixel photon counter (MPPC) signals was used to estimate the DOI positions. The coincidence timing resolution (CTR) was measured using the average and energy‐weighted average of the earliest n time stamps. Two energy windows of 250–700 and 400–600 keV were applied for the measurements. Results: The pattern of the flood images showed discrete event clusters, demonstrating that simple calibrations for determining the x‐ and y‐positions of events could be achieved. Under 400–600 keV energy window, the average intrinsic spatial resolutions were 1.15 and 1.34 mm for the pixelated and non‐pixelated directions; the average DOI resolution of the second row of pseudo‐pixels was 5.1 mm in full width at half maximum (FWHM); when using the energy‐weighted average of the earliest four‐time stamps, the best CTR of 350 ps was achieved. Applying a broader energy window of 250–700 keV only slightly degrades the DOI resolution while maintaining the intrinsic resolution; the best CTR degrades to 410 ps. Conclusions: The proposed hybrid detector concept was verified, and a prototype detector showed high performance for 3D positioning and timing resolution. The novel detector concept shows promise for preclinical and clinical PET scanners with DOI capability. [ABSTRACT FROM AUTHOR]

Background: Lesion detection is one of the most important clinical tasks in positron emission tomography (PET) for oncology. An anthropomorphic model observer (MO) designed to replicate human observers (HOs) in a detection task is an important tool for assessing task‐based image quality. The channelized Hotelling observer (CHO) has been the most popular anthropomorphic MO. Recently, deep learning MOs (DLMOs), mostly based on convolutional neural networks (CNNs), have been investigated for various imaging modalities. However, there have been few studies on DLMOs for PET. Purpose: The goal of the study is to investigate whether DLMOs can predict HOs better than conventional MOs such as CHO in a two‐alternative forced‐choice (2AFC) detection task using PET images with real anatomical variability. Methods: Two types of DLMOs were implemented: (1) CNN DLMO, and (2) CNN‐SwinT DLMO that combines CNN and Swin Transformer (SwinT) encoders. Lesion‐absent PET images were reconstructed from clinical data, and lesion‐present images were reconstructed with adding simulated lesion sinogram data. Lesion‐present and lesion‐absent PET image pairs were labeled by eight HOs consisting of four radiologists and four image scientists in a 2AFC detection task. In total, 2268 pairs of lesion‐present and lesion‐absent images were used for training, 324 pairs for validation, and 324 pairs for test. CNN DLMO, CNN‐SwinT DLMO, CHO with internal noise, and non‐prewhitening matched filter (NPWMF) were compared in the same train‐test paradigm. For comparison, six quantitative metrics including prediction accuracy, mean squared errors (MSEs) and correlation coefficients, which measure how well a MO predicts HOs, were calculated in a 9‐fold cross‐validation experiment. Results: In terms of the accuracy and MSE metrics, CNN DLMO and CNN‐SwinT DLMO showed better performance than CHO and NPWMF, and CNN‐SwinT DLMO showed the best performance among the MOs evaluated. Conclusions: DLMO can predict HOs more accurately than conventional MOs such as CHO in PET lesion detection. Combining SwinT and CNN encoders can improve the DLMO prediction performance compared to using CNN only. [ABSTRACT FROM AUTHOR]

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

Although the precise molecular mechanisms responsible for neuronal death and motor dysfunction in late-onset Parkinson's disease (PD) are unknown, evidence suggests that mitochondrial dysfunction and neuroinflammation occur early, leading to a collective increase in reactive oxygen species (ROS) production and oxidative stress. However, the lack of methods for tracking oxidative stress in the living brain has precluded its use as a potential biomarker. The goal of the current study is to address this need through the evaluation of the first superoxide (O2•−)-sensitive radioactive tracer, [18F]ROStrace, in a model of late-onset PD. To achieve this goal, MitoPark mice with a dopaminergic (DA) neuron-specific deletion of transcription factor A mitochondrial (Tfam) were imaged with [18F]ROStrace from the prodromal phase to the end-stage of PD-like disease. Our data demonstrate [18F]ROStrace was sensitive to increased oxidative stress during the early stages of PD-like pathology in MitoPark mice, which persisted throughout the disease course. Similarly to PD patients, MitoPark males had the most severe parkinsonian symptoms and metabolic impairment. [18F]ROStrace retention was also highest in MitoPark males, suggesting oxidative stress as a potential mechanism underlying the male sex bias of PD. Furthermore, [18F]ROStrace may provide a method to identify patients at risk of Parkinson's before irreparable neurodegeneration occurs and enhance clinical trial design by identifying patients most likely to benefit from antioxidant therapies. [ABSTRACT FROM AUTHOR]

This study aimed to develop several new machine learning models based on hibernating myocardium to predict the major adverse cardiac events(MACE) of ischemic left ventricular systolic dysfunction(LVSD) patients receiving either percutaneous coronary intervention(PCI) or optimal medical therapy(OMT). This study included 329 LVSD patients, who were randomly assigned to the training or validation cohort. Least absolute shrinkage and selection operator(LASSO) regression was used to identify variables associated with MACE. Subsequently, various machine learning models were established. Model performance was compared using receiver operating characteristic(ROC) curves, the Brier score(BS), and the concordance index(C-index). A total of 329 LVSD patients were retrospectively enrolled between January 2016 and December 2021. Utilizing LASSO regression analysis, five factors were selected. Based on these factors, RSF, GBM, XGBoost, Cox, and DeepSurv models were constructed. In the development and validation cohorts, the C-indices were 0.888 vs. 0.955 (RSF). The RSF model (0.991 vs. 0.982 vs. 0.980) had the highest area under the ROC curve (AUC) compared with the other models. The BS (0.077 vs. 0.095vs. 0.077) of RSF model were less than 0.25 at 12, 18, and 24 months. This study developed a novel predictive model based on RSF to predict MACE in LVSD patients who underwent either PCI or OMT. [ABSTRACT FROM AUTHOR]

This systematic review aimed to evaluate the potential of deep learning algorithms for converting low-dose Positron Emission Tomography (PET) images to full-dose PET images in different body regions. A total of 55 articles published between 2017 and 2023 by searching PubMed, Web of Science, Scopus and IEEE databases were included in this review, which utilized various deep learning models, such as generative adversarial networks and UNET, to synthesize high-quality PET images. The studies involved different datasets, image preprocessing techniques, input data types, and loss functions. The evaluation of the generated PET images was conducted using both quantitative and qualitative methods, including physician evaluations and various denoising techniques. The findings of this review suggest that deep learning algorithms have promising potential in generating high-quality PET images from low-dose PET images, which can be useful in clinical practice. [ABSTRACT FROM AUTHOR]

A new type of hybrid polymer particles capable of carrying the cytostatic drug doxorubicin and labeled with a gallium compound was prepared. These microparticles consist of a core and a hydrogel shell, which serves as the structural matrix. The shell can be employed to immobilize gallium oxide hydroxide (GaOOH) nanoparticles and the drug, resulting in hybrid beads with sizes of approximately 3.81 ± 0.09 μm. The microparticles exhibit the ability to incorporate a remarkably large amount of doxorubicin, approximately 0.96 mg per 1 mg of the polymeric carrier. Additionally, GaOOH nanoparticles can be deposited within the hydrogel layer at an amount of 0.64 mg per 1 mg of the carrier. These nanoparticles, resembling rice grains with an average size of 593 nm by 155 nm, are located on the surface of the polymer carrier. In vitro studies on breast and colon cancer cell lines revealed a pronounced cytotoxic effect of the hybrid polymer particles loaded with doxorubicin, indicating their potential for cancer therapies. Furthermore, investigations on doping the hybrid particles with the Ga-68 radioisotope demonstrated their potential application in positron emission tomography (PET) imaging. The proposed structures present a promising theranostic platform, where particles could be employed in anticancer therapies while monitoring their accumulation in the body using PET. [ABSTRACT FROM AUTHOR]

Purpose: Accumulating evidence suggests that neurotensin (NTS) and neurotensin receptors (NTSRs) play key roles in lung cancer progression by triggering multiple oncogenic signaling pathways. This study aims to develop Cu-labeled neurotensin receptor 1 (NTSR1)-targeting agents with the potential for both imaging and therapeutic applications. Method: A series of neurotensin receptor antagonists (NRAs) with variable propylamine (PA) linker length and different chelators were synthesized, including [64Cu]Cu-CB-TE2A-iPA-NRA ([64Cu]Cu-4a-c, i = 1, 2, 3), [64Cu]Cu-NOTA-2PA-NRA ([64Cu]Cu-4d), [64Cu]Cu-DOTA-2PA-NRA ([64Cu]Cu-4e, also known as [64Cu]Cu-3BP-227), and [64Cu]Cu-DOTA-VS-2PA-NRA ([64Cu]Cu-4f). The series of small animal PET/CT were conducted in H1299 lung cancer model. The expression profile of NTSR1 was also confirmed by IHC using patient tissue samples. Results: For most of the compounds studied, PET/CT showed prominent tumor uptake and high tumor-to-background contrast, but the tumor retention was strongly influenced by the chelators used. For previously reported 4e, [64Cu]Cu-labeled derivative showed initial high tumor uptake accompanied by rapid tumor washout at 24 h. The newly developed [64Cu]Cu-4d and [64Cu]Cu-4f demonstrated good tumor uptake and tumor-to-background contrast at early time points, but were less promising in tumor retention. In contrast, our lead compound [64Cu]Cu-4b demonstrated 9.57 ± 1.35, 9.44 ± 2.38 and 9.72 ± 4.89%ID/g tumor uptake at 4, 24, and 48 h p.i., respectively. Moderate liver uptake (11.97 ± 3.85, 9.80 ± 3.63, and 7.72 ± 4.68%ID/g at 4, 24, and 48 h p.i.) was observed with low uptake in most other organs. The PA linker was found to have a significant effect on drug distribution. Compared to [64Cu]Cu-4b, [64Cu]Cu-4a had a lower background, including a greatly reduced liver uptake, while the tumor uptake was only moderately reduced. Meanwhile, [64Cu]Cu-4c showed increased uptake in both the tumor and the liver. The clinical relevance of NTSR1 was also demonstrated by the elevated tumor expression in patient tissue samples. Conclusions: Through the side-by-side comparison, [64Cu]Cu-4b was identified as the lead agent for further evaluation based on its high and sustained tumor uptake and moderate liver uptake. It can not only be used to efficiently detect NTSR1 expression in lung cancer (for diagnosis, patient screening, and treatment monitoring), but also has the great potential to treat NTSR-positive lesions once chelating to the beta emitter 67Cu. [ABSTRACT FROM AUTHOR]

Deep learning, particularly convolutional neural networks (CNNs), has advanced positron emission tomography (PET) image reconstruction. However, it requires extensive, high-quality training datasets. Unsupervised learning methods, such as deep image prior (DIP), have shown promise for PET image reconstruction. Although DIP-based PET image reconstruction methods demonstrate superior performance, they involve highly time-consuming calculations. This study proposed a two-step optimization method to accelerate end-to-end DIP-based PET image reconstruction and improve PET image quality. The proposed two-step method comprised a pre-training step using conditional DIP denoising, followed by an end-to-end reconstruction step with fine-tuning. Evaluations using Monte Carlo simulation data demonstrated that the proposed two-step method significantly reduced the computation time and improved the image quality, thereby rendering it a practical and efficient approach for end-to-end DIP-based PET image reconstruction. [ABSTRACT FROM AUTHOR]

Abstract This systematic review aimed to evaluate the potential of deep learning algorithms for converting low-dose Positron Emission Tomography (PET) images to full-dose PET images in different body regions. A total of 55 articles published between 2017 and 2023 by searching PubMed, Web of Science, Scopus and IEEE databases were included in this review, which utilized various deep learning models, such as generative adversarial networks and UNET, to synthesize high-quality PET images. The studies involved different datasets, image preprocessing techniques, input data types, and loss functions. The evaluation of the generated PET images was conducted using both quantitative and qualitative methods, including physician evaluations and various denoising techniques. The findings of this review suggest that deep learning algorithms have promising potential in generating high-quality PET images from low-dose PET images, which can be useful in clinical practice.

Abstract A new type of hybrid polymer particles capable of carrying the cytostatic drug doxorubicin and labeled with a gallium compound was prepared. These microparticles consist of a core and a hydrogel shell, which serves as the structural matrix. The shell can be employed to immobilize gallium oxide hydroxide (GaOOH) nanoparticles and the drug, resulting in hybrid beads with sizes of approximately 3.81 ± 0.09 μm. The microparticles exhibit the ability to incorporate a remarkably large amount of doxorubicin, approximately 0.96 mg per 1 mg of the polymeric carrier. Additionally, GaOOH nanoparticles can be deposited within the hydrogel layer at an amount of 0.64 mg per 1 mg of the carrier. These nanoparticles, resembling rice grains with an average size of 593 nm by 155 nm, are located on the surface of the polymer carrier. In vitro studies on breast and colon cancer cell lines revealed a pronounced cytotoxic effect of the hybrid polymer particles loaded with doxorubicin, indicating their potential for cancer therapies. Furthermore, investigations on doping the hybrid particles with the Ga-68 radioisotope demonstrated their potential application in positron emission tomography (PET) imaging. The proposed structures present a promising theranostic platform, where particles could be employed in anticancer therapies while monitoring their accumulation in the body using PET.

Abstract Objective Positron Emission Tomography (PET) is a well-known imaging technology for the diagnosis, treatment, and monitoring of several diseases. Most PET scanners use a Ring-Shaped Detector Configuration (RSDC), which helps obtain homogeneous image quality but are restricted to an invariable Field-of-View (FOV), scarce spatial resolution, and low sensitivity. Alternatively, few PET systems use Open Detector Configurations (ODC) to permit an accessible FOV adaptable to different target sizes, thus optimizing sensitivity. Yet, to compensate the lack of angular coverage in ODC-PET, developing a detector with high-timing performance is mandatory to enable Time-of-Flight (TOF) techniques during reconstruction. The main goal of this work is to provide a proof of concept PET scanner appropriate for constructing the new generation of ODC-PET suitable for biopsy guidance and clinical intervention during acquisition. The designed detector has to be compact and robust, and its requirements in terms of performance are spatial and time resolutions

Abstract Background Accurately redirecting reconstructed Positron emission tomography (PET) images into short-axis (SA) images shows great significance for subsequent clinical diagnosis. We developed a system for automatic redirection and quantitative analysis of myocardial PET images. Methods A total of 128 patients were enrolled for 18 F-FDG PET/CT myocardial metabolic images (MMIs), including 3 image classifications: without defects, with defects, and excess uptake. The automatic reorientation system includes five modules: regional division, myocardial segmentation, ellipsoid fitting, image rotation and quantitative analysis. First, the left ventricular geometry-based canny edge detection (LVG-CED) was developed and compared with the other 5 common region segmentation algorithms, the optimized partitioning was determined based on partition success rate. Then, 9 myocardial segmentation methods and 4 ellipsoid fitting methods were combined to derive 36 cross combinations for diagnostic performance in terms of Pearson correlation coefficient (PCC), Kendall correlation coefficient (KCC), Spearman correlation coefficient (SCC), and determination coefficient. Finally, the deflection angles were computed by ellipsoid fitting and the SA images were derived by affine transformation. Furthermore, the polar maps were used for quantitative analysis of SA images, and the redirection effects of 3 different image classifications were analyzed using correlation coefficients. Results On the dataset, LVG-CED outperformed other methods in the regional division module with a 100% success rate. In 36 cross combinations, PSO-FCM and LLS-SVD performed the best in terms of correlation coefficient. The linear results indicate that our algorithm (LVG-CED, PSO-FCM, and LLS-SVD) has good consistency with the reference manual method. In quantitative analysis, the similarities between our method and the reference manual method were higher than 96% at 17 segments. Moreover, our method demonstrated excellent performance in all 3 image classifications. Conclusion Our algorithm system could realize accurate automatic reorientation and quantitative analysis of PET MMIs, which is also effective for images suffering from interference.

Prostate cancer represents a significant public health challenge, with its management requiring precise diagnostic and prognostic tools. Prostate-specific membrane antigen (PSMA), a cell surface enzyme overexpressed in prostate cancer cells, has emerged as a pivotal biomarker. PSMA’s ability to increase the sensitivity of PET imaging has revolutionized its application in the clinical management of prostate cancer. The advancements in PET-PSMA imaging technologies and methodologies, including the development of PSMA-targeted radiotracers and optimized imaging protocols, led to diagnostic accuracy and clinical utility across different stages of prostate cancer. This highlights its superiority in staging and its comparative effectiveness against conventional imaging modalities. This paper analyzes the impact of PET-PSMA on prostate cancer management, discussing the existing challenges and suggesting future research directions. The integration of recent studies and reviews underscores the evolving understanding of PET-PSMA imaging, marking its significant but still expanding role in clinical practice. This comprehensive review serves as a crucial resource for clinicians and researchers involved in the multifaceted domains of prostate cancer diagnosis, treatment, and management.

Abstract Background Residual image noise is substantial in positron emission tomography (PET) and one of the factors limiting lesion detection, quantification, and overall image quality. Thus, improving noise reduction remains of considerable interest. This is especially true for respiratory-gated PET investigations. The only broadly used approach for noise reduction in PET imaging has been the application of low-pass filters, usually Gaussians, which however leads to loss of spatial resolution and increased partial volume effects affecting detectability of small lesions and quantitative data evaluation. The bilateral filter (BF) — a locally adaptive image filter — allows to reduce image noise while preserving well defined object edges but manual optimization of the filter parameters for a given PET scan can be tedious and time-consuming, hampering its clinical use. In this work we have investigated to what extent a suitable deep learning based approach can resolve this issue by training a suitable network with the target of reproducing the results of manually adjusted case-specific bilateral filtering. Methods Altogether, 69 respiratory-gated clinical PET/CT scans with three different tracers ( $$[^{18}\text {F}]$$ [ 18 F ] FDG, $$[^{18}\text {F}]$$ [ 18 F ] L-DOPA, $$[^{68}\text {Ga}]$$ [ 68 Ga ] DOTATATE) were used for the present investigation. Prior to data processing, the gated data sets were split, resulting in a total of 552 single-gate image volumes. For each of these image volumes, four 3D ROIs were delineated: one ROI for image noise assessment and three ROIs for focal uptake (e.g. tumor lesions) measurements at different target/background contrast levels. An automated procedure was used to perform a brute force search of the two-dimensional BF parameter space for each data set to identify the “optimal” filter parameters to generate user-approved ground truth input data consisting of pairs of original and optimally BF filtered images. For reproducing the optimal BF filtering, we employed a modified 3D U-Net CNN incorporating residual learning principle. The network training and evaluation was performed using a 5-fold cross-validation scheme. The influence of filtering on lesion SUV quantification and image noise level was assessed by calculating absolute and fractional differences between the CNN, manual BF, or original (STD) data sets in the previously defined ROIs. Results The automated procedure used for filter parameter determination chose adequate filter parameters for the majority of the data sets with only 19 patient data sets requiring manual tuning. Evaluation of the focal uptake ROIs revealed that CNN as well as BF based filtering essentially maintain the focal $$\text {SUV}_\text {max}$$ SUV max values of the unfiltered images with a low mean ± SD difference of $$\delta \text {SUV}_\text {max}^{\text {CNN},\text {STD}}$$ δ SUV max CNN , STD = (−3.9 ± 5.2)% and $$\delta \text {SUV}_\text {max}^{\text {BF},\text {STD}}$$ δ SUV max BF , STD = (−4.4 ± 5.3)%. Regarding relative performance of CNN versus BF, both methods lead to very similar $$\text {SUV}_\text {max}$$ SUV max values in the vast majority of cases with an overall average difference of $$\delta \text {SUV}_\text {max}^{\text {CNN},\text {BF}}$$ δ SUV max CNN , BF = (0.5 ± 4.8)%. Evaluation of the noise properties showed that CNN filtering mostly satisfactorily reproduces the noise level and characteristics of BF with $$\delta \text {Noise}^{\text {CNN},\text {BF}}$$ δ Noise CNN , BF = (5.6 ± 10.5)%. No significant tracer dependent differences between CNN and BF were observed. Conclusions Our results show that a neural network based denoising can reproduce the results of a case by case optimized BF in a fully automated way. Apart from rare cases it led to images of practically identical quality regarding noise level, edge preservation, and signal recovery. We believe such a network might proof especially useful in the context of improved motion correction of respiratory-gated PET studies but could also help to establish BF-equivalent edge-preserving CNN filtering in clinical PET since it obviates time consuming manual BF parameter tuning.

Positron Emission Tomography (PET) image reconstruction remains a pivotal area in PET technology, critically influencing clinical diagnostic outcomes. Addressing the need for enhanced image quality, this study introduces a novel algorithm for PET image reconstruction. This algorithm integrates a penalty mechanism, morphological filtering, and the Maximum Likelihood Expectation Maximization (MLEM) algorithm, aiming to improve reconstructed image quality significantly. The operational process of the algorithm within each iteration encompasses two primary phases. Initially, image reconstruction is accomplished via the MLEM algorithm, followed by the application of a morphological filter to attenuate noise in the reconstructed image. Simulation experiments demonstrate that this algorithm effectively mitigates noise while preserving crucial details, such as image edges. Notably, this method presents the dual benefits of straightforward parameter configuration and ease of implementation. The results indicate a substantial enhancement in noise suppression and fine structure preservation in the reconstructed images, marking a significant advancement in PET image reconstruction techniques.

Insulinomas are the most common functional pancreatic neuroendocrine neoplasm; when treatment is delayed, they induce hyperinsulinemic hypoglycemia, which is life-threatening. As surgical resection is the only curative treatment for insulinoma, preoperative localization is crucial; however, localization based on conventional imaging modalities such as computed tomography (CT) and magnetic resonance imaging is often inconclusive. Somatostatin receptor-targeted imaging is another option for detecting pancreatic neuroendocrine neoplasms but has low sensitivity and is not specific for insulinoma. The clinical application of other localizing approaches such as selective arterial calcium stimulation and endoscopic ultrasonography-guided fine needle aspiration (EUS-FNA) is limited by their being invasive and/or technically complex. Moreover, an EUS-FNA specimen of an insulinoma may be negative on insulin immunostaining. Thus, a noninvasive and clinically practical insulinoma-specific diagnostic tool to discriminate insulinomas with high accuracy is anticipated. Glucagon-like peptide-1 receptor (GLP-1R)-targeted imaging has emerged in the effort to fulfill this need. We recently developed the novel fluorine-18-labeled exendin-4-based probe conjugated with polyethylene glycol, [18F]FB(ePEG12)12-exendin-4 (18F-exendin-4) for positron emission tomography (PET) imaging and reported its clinical benefit in a case of insulinoma in the pancreatic tail. We report here a case of insulinoma in the pancreatic head in which an EUS-FNA specimen was negative on insulin immunostaining while precise preoperative localization and conclusive evidence for curative enucleation was provided by 18F-exendin-4 PET/CT (Japan Registry of Clinical Trials; jRCTs051200156).

Objective: Positron Emission Tomography (PET) is a well-known imaging technology for the diagnosis, treatment, and monitoring of several diseases. Most PET scanners use a Ring-Shaped Detector Configuration (RSDC), which helps obtain homogeneous image quality but are restricted to an invariable Field-of-View (FOV), scarce spatial resolution, and low sensitivity. Alternatively, few PET systems use Open Detector Configurations (ODC) to permit an accessible FOV adaptable to different target sizes, thus optimizing sensitivity. Yet, to compensate the lack of angular coverage in ODC-PET, developing a detector with high-timing performance is mandatory to enable Time-of-Flight (TOF) techniques during reconstruction. The main goal of this work is to provide a proof of concept PET scanner appropriate for constructing the new generation of ODC-PET suitable for biopsy guidance and clinical intervention during acquisition. The designed detector has to be compact and robust, and its requirements in terms of performance are spatial and time resolutions < 2 mm and < 200 ps, respectively. Methods: The present work includes a simulation study of an ODC-PET based on 2-panels with variable distance. The image quality (IQ) and Derenzo phantoms have been simulated and evaluated. The phantom simulations have also been performed using a ring-shaped PET for comparison purposes of the ODC approach with conventional systems. Then, an experimental evaluation of a prototype detector that has been designed following the simulation results is presented. This study focused on tuning the ASIC parameters and evaluating the scintillator surface treatment (ESR and TiO2), and configuration that yields the best Coincidence Time Resolution (CTR). Moreover, the scalability of the prototype to a module of 64 × 64mm2 and its preliminary evaluation regarding pixel identification are provided. Results: The simulation results reported sensitivity (%) values at the center of the FOV of 1.96, 1.63, and 1.18 for panel distances of 200, 250, and 300 mm, respectively. The IQ reconstructed image reported good uniformity (87%) and optimal CRC values, and the Derenzo phantom reconstruction suggests a system resolution of 1.6–2 mm. The experimental results demonstrate that using TiO2 coating yielded better detector performance than ESR. Acquired data was filtered by applying an energy window of ± 30% at the photopeak level. After filtering, best CTR of 230 ± 2 ps was achieved for an 8 × 8 LYSO pixel block with 2 × 2 × 12mm3 each. The detector performance remained constant after scaling-up the prototype to a module of 64 × 64mm2, and the flood map demonstrates the module's capabilities to distinguish the small pixels; thus, a spatial resolution < 2 mm (pixel size) is achieved. Conclusions: The simulated results of this biplanar scanner show high performance in terms of image quality and sensitivity. These results are comparable to state-of-the-art PET technology and, demonstrate that including TOF information minimizes the image artifacts due to the lack of angular projections. The experimental results concluded that using TiO2 coating provide the best performance. The results suggest that this scanner may be suitable for organ study, breast, prostate, or cardiac applications, with good uniformity and CRC. [ABSTRACT FROM AUTHOR]

Mutations of isocitrate dehydrogenase 1 (IDH1) are key biomarkers for glioma classification, but current methods for detection of mutated IDH1 (mIDH1) require invasive tissue sampling and cannot be used for longitudinal studies. Positron emission tomography (PET) imaging with mIDH1-selective radioligands is a promising alternative approach that could enable non-invasive assessment of the IDH status. In the present work, we developed efficient protocols for the preparation of four 18F-labeled derivatives of the mIDH1-selective inhibitor olutasidenib. All four probes were characterized by cellular uptake studies with U87 glioma cells harboring a heterozygous IDH1 mutation (U87-mIDH) and the corresponding wildtype cells (U87-WT). In addition, the most promising probe was evaluated by PET imaging in healthy mice and mice bearing subcutaneous U87-mIDH and U87-WT tumors. Although all four probes inhibited mIDH1 with variable potencies, only one of them ([18F]mIDH-138) showed significantly higher in vitro uptake into U87-mIDH compared to U87-WT cells. In addition, PET imaging with [18F]mIDH-138 in mice demonstrated good in vivo stability and low non-specific uptake of the probe, but also revealed significantly higher uptake into U87-WT compared to U87-mIDH tumors. Finally, application of a two-tissue compartment model (2TCM) to the PET data indicated that preferential tracer uptake into U87-WT tumors results from higher specific binding rather than from differences in tracer perfusion. In conclusion, these results corroborate recent findings that mIDH1-selective inhibition may not directly correlate with mIDH1-selective target engagement and indicate that in vivo engagement of wildtype and mutated IDH1 may be governed by factors that are not faithfully reproduced by in vitro assays, both of which could complicate development of PET probes. [ABSTRACT FROM AUTHOR]

An automated method has been developed for the synthesis of [N-methyl-11C]choline, a radiopharmaceutical (RP) for the diagnosis of cancer using positron emission tomography (PET). The synthesis was carried out on a home-made module, using combined technology of on-line 11C-methylation processes and solid-phase extraction methods. The radiochemical yield of [N-methyl-11C]choline was 80% (based on the activity of the methylating agent, [11C]CH3I, decay corrected), which ensures the production of several clinical doses of radiopharmaceutical in one batch. [N-Methyl-11C]choline was obtained with a radiochemical purity of more than 99% and an amount of 2-dimethylaminoethanol (the main chemical impurity) of 0.06 mg/mL, which meets the requirements of the Russian and European Pharmacopoeia. [ABSTRACT FROM AUTHOR]

Background: Accurately redirecting reconstructed Positron emission tomography (PET) images into short-axis (SA) images shows great significance for subsequent clinical diagnosis. We developed a system for automatic redirection and quantitative analysis of myocardial PET images. Methods: A total of 128 patients were enrolled for 18 F-FDG PET/CT myocardial metabolic images (MMIs), including 3 image classifications: without defects, with defects, and excess uptake. The automatic reorientation system includes five modules: regional division, myocardial segmentation, ellipsoid fitting, image rotation and quantitative analysis. First, the left ventricular geometry-based canny edge detection (LVG-CED) was developed and compared with the other 5 common region segmentation algorithms, the optimized partitioning was determined based on partition success rate. Then, 9 myocardial segmentation methods and 4 ellipsoid fitting methods were combined to derive 36 cross combinations for diagnostic performance in terms of Pearson correlation coefficient (PCC), Kendall correlation coefficient (KCC), Spearman correlation coefficient (SCC), and determination coefficient. Finally, the deflection angles were computed by ellipsoid fitting and the SA images were derived by affine transformation. Furthermore, the polar maps were used for quantitative analysis of SA images, and the redirection effects of 3 different image classifications were analyzed using correlation coefficients. Results: On the dataset, LVG-CED outperformed other methods in the regional division module with a 100% success rate. In 36 cross combinations, PSO-FCM and LLS-SVD performed the best in terms of correlation coefficient. The linear results indicate that our algorithm (LVG-CED, PSO-FCM, and LLS-SVD) has good consistency with the reference manual method. In quantitative analysis, the similarities between our method and the reference manual method were higher than 96% at 17 segments. Moreover, our method demonstrated excellent performance in all 3 image classifications. Conclusion: Our algorithm system could realize accurate automatic reorientation and quantitative analysis of PET MMIs, which is also effective for images suffering from interference. [ABSTRACT FROM AUTHOR]

Prostate cancer represents a significant public health challenge, with its management requiring precise diagnostic and prognostic tools. Prostate-specific membrane antigen (PSMA), a cell surface enzyme overexpressed in prostate cancer cells, has emerged as a pivotal biomarker. PSMA's ability to increase the sensitivity of PET imaging has revolutionized its application in the clinical management of prostate cancer. The advancements in PET-PSMA imaging technologies and methodologies, including the development of PSMA-targeted radiotracers and optimized imaging protocols, led to diagnostic accuracy and clinical utility across different stages of prostate cancer. This highlights its superiority in staging and its comparative effectiveness against conventional imaging modalities. This paper analyzes the impact of PET-PSMA on prostate cancer management, discussing the existing challenges and suggesting future research directions. The integration of recent studies and reviews underscores the evolving understanding of PET-PSMA imaging, marking its significant but still expanding role in clinical practice. This comprehensive review serves as a crucial resource for clinicians and researchers involved in the multifaceted domains of prostate cancer diagnosis, treatment, and management. [ABSTRACT FROM AUTHOR]

Positron emission tomography/computed tomography (PET/CT) with 18F-florbetapir, a novel amyloid-targeting radiotracer, can quantify left ventricular (LV) amyloid burden in systemic light-chain (AL) amyloidosis. However, its prognostic value is not known. The authors' aim was to evaluate the prognostic value of LV amyloid burden quantified by 18F-florbetapir PET/CT, and to identify mechanistic pathways mediating its association with outcomes. A total of 81 participants with newly diagnosed AL amyloidosis underwent 18F-florbetapir PET/CT imaging. Amyloid burden was quantified using 18F-florbetapir LV uptake as percent injected dose. The Mayo stage for AL amyloidosis was determined using troponin T, N-terminal pro-B-type natriuretic peptide (NT-proBNP), and free light chain levels. Major adverse cardiac events (MACE) were defined as all-cause death, heart failure hospitalization, or cardiac transplantation within 12 months. Among participants (median age, 61 years; 57% males), 36% experienced MACE, increasing from 7% to 63% across tertiles of LV amyloid burden (P < 0.001). LV amyloid burden was associated with MACE (HR: 1.46; 95% CI: 1.16-1.83; P = 0.001). However, this association became nonsignificant when adjusted for Mayo stage. In mediation analysis, the association between LV amyloid burden and MACE was mediated by NT-proBNP (P < 0.001), a marker of cardiomyocyte stretch and heart failure, and a component of Mayo stage. In this first study to link cardiac 18F-florbetapir uptake to subsequent outcomes, LV amyloid burden estimated by percent injected dose predicted MACE in AL amyloidosis. This effect was not independent of Mayo stage and was mediated primarily through NT-proBNP. These findings provide novel insights into the mechanism linking myocardial amyloid deposits to MACE. [Display omitted] [ABSTRACT FROM AUTHOR]

Purpose: A series of new 68Ga-labeled tracers based on [68Ga]Ga-PSMA-617 were developed to augment the tumor-to-kidney ratio and reduce the activity accumulation in bladder, ultimately minimize radiation toxicity to the urinary system. Methods: We introduced quinoline group, phenylalanine and decanoic acid into different tracers to enhance their lipophilicity, strategically limiting their metabolic pathway through the urinary system. Their binding affinity onto LNCaP cells was determined through in vitro saturation assays and competition binding assays. In vivo metabolic study, PET imaging and biodistribution experiment were performed in LNCaP tumor-bearing B-NSG male mice. The most promising tracer was selected for first-in-human study. Results: Four radiotracers were synthesized with radiochemical purity (RCP) > 95% and molar activity in a range of 20.0-25.5 GBq/μmol. The binding affinities (Ki) of TWS01, TWS02 to PSMA were in the low nanomolar range (< 10 nM), while TWS03 and TWS04 exhibited binding affinities with Ki > 20 nM (59.42 nM for TWS03 and 37.14 nM for TWS04). All radiotracers exhibited high stability in vivo except [68Ga]Ga-TWS03. Micro PET/CT imaging and biodistribution analysis revealed that [68Ga]Ga-TWS02 enabled clear tumor visualization in PET images at 1.5 h post-injection, with higher tumor-to-kidney ratio (T/K, 0.93) and tumor-to-muscle ratio (T/M, 107.62) compared with [68Ga]Ga-PSMA-617 (T/K: 0.39, T/M: 15.01) and [68Ga]Ga-PSMA-11 (T/K: 0.15, T/M: 24.00). In first-in-human study, [68Ga]Ga-TWS02 effectively detected PCa-associated lesions including primary and metastatic lesions, with lower accumulation in urinary system, suggesting that [68Ga]Ga-TWS02 might be applied in the detection of bladder invasion, with minimized radiation toxicity to the urinary system. Conclusion: Introduction of quinoline group, phenylalanine and decanoic acid into different tracers can modulate the binding affinity and pharmacokinetics of PSMA in vivo. [68Ga]Ga-TWS02 showed high binding affinity to PSMA, excellent pharmacokinetic properties and clear imaging of PCa-associated lesions, making it a promising radiotracer for the clinical diagnosis of PCa. Moreover, TWS02 with a chelator DOTA could also label 177Lu and 225Ac, which could be used for PCa treatment without significant side effects. Trial registration: The clinical evaluation of this study was registered On October 30, 2021 at https://www.chictr.org.cn/ (No: ChiCTR2100052545). [ABSTRACT FROM AUTHOR]

Simple Summary: Cholangiocarcinoma (CCA) is a type of liver cancer with few treatment options and low survival rates in advanced stages. Our study developed a mouse model to study this cancer type by implanting CCA cells into the liver of mice. We used advanced imaging techniques (MRI and PET scans) to monitor tumor growth and metabolism over four weeks. We observed that tumors became visible early and grew steadily over time. PET scans showed increasing tumor activity, and blood tests revealed liver damage. Most mice developed lung metastases after four weeks. Our research shows that combining MRI and PET scans effectively tracks CCA progression in mice, providing valuable insights into cancer development and investigating potential treatments. Cholangiocarcinoma (CCA) is a type of primary liver cancer originating from the biliary tract epithelium, characterized by limited treatment options for advanced cases and low survival rates. This study aimed to establish an orthotopic mouse model for CCA and monitor tumor growth using PET/MR imaging. Murine CCA cells were implanted into the liver lobe of male C57BL/6J mice. The imaging groups included contrast-enhanced (CE) MR, CE-MR with static [18F]FDG-PET, and dynamic [18F]FDG-PET. Tumor volume and FDG uptake were measured weekly over four weeks. Early tumor formation was visible in CE-MR images, with a gradual increase in volume over time. Dynamic FDG-PET revealed an increase in the metabolic glucose rate (MRGlu) over time. Blood analysis showed pathological changes in liver-related parameters. Lung metastases were observed in nearly all animals after four weeks. The study concludes that PET-MR imaging effectively monitors tumor progression in the CCA mouse model, providing insights into CCA development and potential treatment strategies. [ABSTRACT FROM AUTHOR]

Positron Emission Tomography (PET) image reconstruction remains a pivotal area in PET technology, critically influencing clinical diagnostic outcomes. Addressing the need for enhanced image quality, this study introduces a novel algorithm for PET image reconstruction. This algorithm integrates a penalty mechanism, morphological filtering, and the Maximum Likelihood Expectation Maximization (MLEM) algorithm, aiming to improve reconstructed image quality significantly. The operational process of the algorithm within each iteration encompasses two primary phases. Initially, image reconstruction is accomplished via the MLEM algorithm, followed by the application of a morphological filter to attenuate noise in the reconstructed image. Simulation experiments demonstrate that this algorithm effectively mitigates noise while preserving crucial details, such as image edges. Notably, this method presents the dual benefits of straightforward parameter configuration and ease of implementation. The results indicate a substantial enhancement in noise suppression and fine structure preservation in the reconstructed images, marking a significant advancement in PET image reconstruction techniques. [ABSTRACT FROM AUTHOR]

Background: Residual image noise is substantial in positron emission tomography (PET) and one of the factors limiting lesion detection, quantification, and overall image quality. Thus, improving noise reduction remains of considerable interest. This is especially true for respiratory-gated PET investigations. The only broadly used approach for noise reduction in PET imaging has been the application of low-pass filters, usually Gaussians, which however leads to loss of spatial resolution and increased partial volume effects affecting detectability of small lesions and quantitative data evaluation. The bilateral filter (BF) — a locally adaptive image filter — allows to reduce image noise while preserving well defined object edges but manual optimization of the filter parameters for a given PET scan can be tedious and time-consuming, hampering its clinical use. In this work we have investigated to what extent a suitable deep learning based approach can resolve this issue by training a suitable network with the target of reproducing the results of manually adjusted case-specific bilateral filtering. Methods: Altogether, 69 respiratory-gated clinical PET/CT scans with three different tracers ( [ 18 F ] FDG, [ 18 F ] L-DOPA, [ 68 Ga ] DOTATATE) were used for the present investigation. Prior to data processing, the gated data sets were split, resulting in a total of 552 single-gate image volumes. For each of these image volumes, four 3D ROIs were delineated: one ROI for image noise assessment and three ROIs for focal uptake (e.g. tumor lesions) measurements at different target/background contrast levels. An automated procedure was used to perform a brute force search of the two-dimensional BF parameter space for each data set to identify the "optimal" filter parameters to generate user-approved ground truth input data consisting of pairs of original and optimally BF filtered images. For reproducing the optimal BF filtering, we employed a modified 3D U-Net CNN incorporating residual learning principle. The network training and evaluation was performed using a 5-fold cross-validation scheme. The influence of filtering on lesion SUV quantification and image noise level was assessed by calculating absolute and fractional differences between the CNN, manual BF, or original (STD) data sets in the previously defined ROIs. Results: The automated procedure used for filter parameter determination chose adequate filter parameters for the majority of the data sets with only 19 patient data sets requiring manual tuning. Evaluation of the focal uptake ROIs revealed that CNN as well as BF based filtering essentially maintain the focal SUV max values of the unfiltered images with a low mean ± SD difference of δ SUV max CNN , STD = (−3.9 ± 5.2)% and δ SUV max BF , STD = (−4.4 ± 5.3)%. Regarding relative performance of CNN versus BF, both methods lead to very similar SUV max values in the vast majority of cases with an overall average difference of δ SUV max CNN , BF = (0.5 ± 4.8)%. Evaluation of the noise properties showed that CNN filtering mostly satisfactorily reproduces the noise level and characteristics of BF with δ Noise CNN , BF = (5.6 ± 10.5)%. No significant tracer dependent differences between CNN and BF were observed. Conclusions: Our results show that a neural network based denoising can reproduce the results of a case by case optimized BF in a fully automated way. Apart from rare cases it led to images of practically identical quality regarding noise level, edge preservation, and signal recovery. We believe such a network might proof especially useful in the context of improved motion correction of respiratory-gated PET studies but could also help to establish BF-equivalent edge-preserving CNN filtering in clinical PET since it obviates time consuming manual BF parameter tuning. [ABSTRACT FROM AUTHOR]

• Metabolic PET radiomics features were strong predictors of seizure recurrence. • PET-based radiomics score, SGS, and Durmon were significant predictors of seizure-free outcomes. • A nomogram incorporating PET-based radiomics and clinical risk factors demonstrated good performance in predicting surgery outcomes. To establish and validate a novel nomogram based on clinical characteristics and [18F]FDG PET radiomics for the prediction of postsurgical seizure freedom in patients with temporal lobe epilepsy (TLE). 234 patients with drug-refractory TLE patients were included with a median follow-up time of 24 months after surgery. The correlation coefficient redundancy analysis and LASSO Cox regression were used to characterize risk factors. The Cox model was conducted to develop a Clinic-PET nomogram to predict the relapse status in the training set (n = 171). The nomogram's performance was estimated through discrimination, calibration, and clinical utility. The prognostic prediction model was validated in the test set (n = 63). Eight radiomics features were selected to assess the radiomics score (radscore) of the operation side (Lat_radscore) and the asymmetric index (AI) of the radiomics score (AI_radscore). AI_radscor, Lat_radscor, secondarily generalized seizures (SGS), and duration between seizure onset and surgery (Durmon) were significant predictors of seizure-free outcomes. The final model had a C-index of 0.68 (95 %CI: 0.59–0.77) for complete freedom from seizures and time-dependent AUROC was 0.65 at 12 months, 0.65 at 36 months, and 0.59 at 60 months in the test set. A web application derived from the primary predictive model was displayed for economic and efficient use. A PET-based radiomics nomogram is clinically promising for predicting seizure outcomes after temporal lobe epilepsy surgery. [ABSTRACT FROM AUTHOR]

Accurately diagnosing Alzheimer's disease (AD) and frontotemporal lobar degeneration (FTLD) is challenging due to overlapping symptoms and limitations of current imaging methods. This study investigates the use of [11C]PBB3 PET/CT imaging to visualize tau pathology and improve diagnostic accuracy. Given diagnostic challenges with symptoms and conventional imaging, [11C]PBB3 PET/CT's potential to enhance accuracy was investigated by correlating tau pathology with cerebrospinal fluid (CSF) biomarkers, positron emission tomography (PET), computed tomography (CT), amyloid-beta, and Mini-Mental State Examination (MMSE). We conducted [11C]PBB3 PET/CT imaging on 24 patients with suspected AD or FTLD, alongside [11C]PiB PET/CT (13 patients) and [18F]FDG PET/CT (15 patients). Visual and quantitative assessments of [11C]PBB3 uptake using standardized uptake value ratios (SUV-Rs) and correlation analyses with clinical assessments were performed. The scans revealed distinct tau accumulation patterns; 13 patients had no or faint uptake (PBB3-negative) and 11 had moderate to pronounced uptake (PBB3-positive). Significant inverse correlations were found between [11C]PBB3 SUV-Rs and MMSE scores, but not with CSF-tau or CSF-amyloid-beta levels. Here, we show that [11C]PBB3 PET/CT imaging can reveal distinct tau accumulation patterns and correlate these with cognitive impairment in neurodegenerative diseases. Our study demonstrates the potential of [11C]PBB3-PET imaging for visualizing tau pathology and assessing disease severity, offering a promising tool for enhancing diagnostic accuracy in AD and FTLD. Further research is essential to validate these findings and refine the use of tau-specific PET imaging in clinical practice, ultimately improving patient care and treatment outcomes. [ABSTRACT FROM AUTHOR]

Background: A growing number of advanced neuroimaging studies have compared brain structure and function in long term meditators to non-meditators. The goal is to determine if there may be long term effects on the brain from practicing meditation. In this paper, we present new data on the long term effects of a novel meditation practice in which the focus is on clitoral stimulation. The findings from such a study have implications for potential therapeutic uses with regard to various neurological or psychiatric conditions. Methods: We evaluated the cerebral glucose metabolism in 40 subjects with an extended history (>1 year of practice, 2-3 times per week) performing the meditation practice called Orgasmic Meditation (OM) and compared their brains to a group of non-meditating healthy controls (N = 19). Both meditation and non-meditation subjects underwent brain PET after injection with 148 to 296 MBq of FDG using a standard imaging protocol. Resting FDG PET scans of theOM group were compared to the resting scans of healthy, non-meditating, controls using statistical parametric mapping. Results: The OM group showed significant differences in metabolic activity at rest compared to the controls. Specifically, there was significantly lower metabolism in select areas of the frontal, temporal, and parietal lobes, as well as the anterior cingulate, insula, and thalamus, in the OM group compared to the controls. In addition, there were notable distinctions between the males and females with the females demonstrating significantly lower metabolism in the thalamus and insula. Conclusions: Overall, these findings suggest that the long term meditation practitioners of OM have different patterns of resting brain metabolism. Since these areas of the brain in which OM practitioners differ from controls are involved in cognition, attention, and emotional regulation, such findings have implications for understanding how this meditation practice might affect practitioners over long periods of time. [ABSTRACT FROM AUTHOR]

Objective: Accurate imaging biomarkers that indicate disease progression at an early stage are highly important to enable timely mitigation of symptoms in progressive lung disease. In this context, reproducible experimental models and readouts are key. Here, we aim to show reproducibility of a lung injury rat model by inducing disease and assessing disease progression by multi-modal non-invasive imaging techniques at two different research sites. Furthermore, we evaluated the potential of fibroblast activating protein (FAP) as an imaging biomarker in the early stage of lung fibrosis. Methods: An initial lung injury rat model was set up at one research site (Lund University, Lund, Sweden) and repeated at a second site (Radboudumc, Nijmegen, The Netherlands). To induce lung injury, Sprague-Dawley rats received intratracheal instillation of bleomycin as one single dose (1,000 iU in 200 µL) or saline as control. Thereafter, longitudinal images were acquired to track inflammation in the lungs, at 1 and 2 weeks after the bleomycin challenge by magnetic resonance imaging (MRI) and [18F]FDG-PET. After the final [18F] FDG-PET scan, rats received an intravenous tracer [89Zr]Zr-DFO-28H1 (anti-FAP antibody) and were imaged at day 15 to track fibrogenesis. Upon termination, bronchoalveolar lavage (BAL) was performed to assess cell and protein concentration. Subsequently, the biodistribution of [89Zr]Zr-DFO-28H1 was measured ex vivo and the spatial distribution in lung tissue was studied by autoradiography. Lung sections were stained and fibrosis assessed using the modified Ashcroft score. Results: Bleomycin-challenged rats showed body weight loss and increased numbers of immune cells and protein concentrations after BAL compared with control animals. The initiation and progression of the disease were reproduced at both research sites. Lung lesions in bleomycin-exposed rats were visualized by MRI and confirmed by histology. [18F]FDG uptake was higher in the lungs of bleomycin-challenged rats compared with the controls, similar to that observed in the Lund study. [89Zr]Zr-DFO-28H1 tracer uptake in the lung was increased in bleomycin-challenged rats compared with control rats (p = 0.03). Conclusion: Here, we demonstrate a reproducible lung injury model and monitored disease progression using conventional imaging biomarkers MRI and [18F]FDG-PET. Furthermore, we showed the first proof-of-concept of FAP imaging. This reproducible and robust animal model and imaging experimental set-up allows for future research on new therapeutics or biomarkers in lung disease. [ABSTRACT FROM AUTHOR]

Positron Emission Tomography (PET) is a powerful medical imaging technique widely used for detection and monitoring of disease. However, PET imaging can be adversely affected by patient motion, leading to degraded image quality and diagnostic capability. Hence, motion gating schemes have been developed to monitor various motion sources including head motion, respiratory motion, and cardiac motion. The approaches for these techniques have commonly come in the form of hardware-driven gating and data-driven gating, where the distinguishing aspect is the use of external hardware to make motion measurements vs. deriving these measures from the data itself. The implementation of these techniques helps correct for motion artifacts and improves tracer uptake measurements. With the great impact that these methods have on the diagnostic and quantitative quality of PET images, much research has been performed in this area, and this paper outlines the various approaches that have been developed as applied to whole-body PET imaging. [ABSTRACT FROM AUTHOR]

Simple Summary: Time-to-event analysis holds significant relevance for diseases like cancer since accurate disease prognosis is crucial for better patient management and for personalizing treatment. In recent years, survival models using machine learning (ML)-based tools have shown promise in cancer prognosis. We compared four survival models in the ML framework to predict adverse outcomes—all-cause mortality (ACM), locoregional recurrence/residual disease (LR), and distant metastasis (DM)—in head and neck cancer patients. Using radiomic features from pre-treatment positron emission tomography (PET) images, we assessed the performance of these models in an external independent validation cohort. The best-performing model for each outcome was identified based on the highest concordance index and the lowest error in training data. The penalized Cox model for ACM and DM and the random forest model for LR showed promising results. Further training and validation of these models in a larger cohort is required for clinical implementation. High-dimensional radiomics features derived from pre-treatment positron emission tomography (PET) images offer prognostic insights for patients with head and neck squamous cell carcinoma (HNSCC). Using 124 PET radiomics features and clinical variables (age, sex, stage of cancer, site of cancer) from a cohort of 232 patients, we evaluated four survival models—penalized Cox model, random forest, gradient boosted model and support vector machine—to predict all-cause mortality (ACM), locoregional recurrence/residual disease (LR) and distant metastasis (DM) probability during 36, 24 and 24 months of follow-up, respectively. We developed models with five-fold cross-validation, selected the best-performing model for each outcome based on the concordance index (C-statistic) and the integrated Brier score (IBS) and validated them in an independent cohort of 102 patients. The penalized Cox model demonstrated better performance for ACM (C-statistic = 0.70, IBS = 0.12) and DM (C-statistic = 0.70, IBS = 0.08) while the random forest model displayed better performance for LR (C-statistic = 0.76, IBS = 0.07). We conclude that the ML-based prognostic model can aid clinicians in quantifying prognosis and determining effective treatment strategies, thereby improving favorable outcomes in HNSCC patients. [ABSTRACT FROM AUTHOR]

Purpose: The aim of this retrospective analysis was to assess the diagnostic accuracy of the latest-generation digital positron emission tomography/computed tomography (PET/CT) scanner in the detection of cervical lymph node metastasis in patients undergoing staging work-up for head and neck cancer. Materials and methods: A total of 55 consecutive patients with head and neck cancer at our institution who had a PET/CT after installation of the latestgeneration PET/CT (Siemens Biograph Vision) who subsequently underwent surgical neck dissection were included. The nodal station location and number of reported PET/CT-positive metastatic lymph nodes were compared to a gold standard of final surgical pathology after neck dissection. Results: In total, 188 neck levels and 1,373 lymph nodes were resected; 56 neck levels (118 nodes) in 31 (56%) patients contained nodal metastases on surgical pathology. On a nodal level-by-level analysis, the overall sensitivity for the detection of lymph node metastases on the latest-generation PET/CT scanner was 96.4% and the specificity was 86.4%. The sensitivity and specificity for the neck side analysis were 94.0% and 63.7%, and for the individual patient analysis were 100% and 71%, respectively. Conclusions: In this single-institution study, latest-generation PET/CT had a high sensitivity and moderate to high specificity for detecting cervical node metastasis in head and neck cancer. Compared to data from older PET/CT scanners, the sensitivity of the latest-generation PET/CT was slightly higher, while the specificity was similar or slightly lower. Physicians involved in the management of head and neck cancer should be aware of possible changes in the overall diagnostic accuracy when changing to a latest-generation PET/CT scanner. [ABSTRACT FROM AUTHOR]

Purpose: Neuropilin-1 (NRP-1) is a multifunctional protein involved in a variety of biological processes such as angiogenesis, tumorigenesis and immunomodulation. It was usually overexpressed in many cancer cell lines and correlated with poor prognosis of breast cancer. Positron emission tomography (PET) is an advanced imaging technique for detecting the function and metabolism of tumor-associated molecules in real time, dynamically, quantitatively and noninvasively. To improve the level of early diagnosis and evaluate the prognosis of breast cancer, an NRP-1 targeting peptide-based tracer [68 Ga]Ga-NOTA-PEG4-CK2 was designed to sensitively and specifically detect the NRP-1 expression in vivo via PET imaging. Methods: In silico modeling and microscale thermophoresis (MST) assay were carried out to design the NRP-1 targeting peptide NOTA-PEG4-CK2, and it was further radiolabeled with 68 Ga to prepare the tracer [68 Ga]Ga-NOTA-PEG4-CK2. The radiochemical yield (RCY), radiochemical purity (RCP), molar activity (Am), lipid-water partition coefficient (Log P) and stability of [68 Ga]Ga-NOTA-PEG4-CK2 were assessed. The targeting specificity of the tracer for NRP-1 was investigated by in vitro cellular uptake assay and in vivo PET imaging as well as blocking studies. The sensitivity of the tracer in monitoring the dynamic changes of NRP-1 expression induced by chemical drug was also investigated in vitro and in vivo. Ex vivo biodistribution, autoradiography, western blot, and immunofluorescence staining were also performed to study the specificity of [68 Ga]Ga-NOTA-PEG4-CK2 for NRP-1. Results: [68 Ga]Ga-NOTA-PEG4-CK2 was designed and synthesized with high RCY (> 98%), high stability (RCP > 95%) and high affinity to NRP-1 (KD = 25.39 ± 1.65 nM). In vitro cellular uptake assay showed that the tracer [68 Ga]Ga-NOTA-PEG4-CK2 can specifically bind to NRP-1 positive cancer cells MDA-MB-231 (1.04 ± 0.04% at 2 h) rather than NRP-1 negative cancer cells NCI-H1299 (0.43 ± 0.05%). In vivo PET imaging showed the maximum tumor uptake of [68 Ga]Ga-NOTA-PEG4-CK2 in MDA-MB-231 xenografts (4.16 ± 0.67%ID/mL) was significantly higher than that in NCI-H1299 xenografts (1.03 ± 0.19%ID/mL) at 10 min post injection, and the former exhibited higher tumor-to-muscle uptake ratio (5.22 ± 0.18) than the latter (1.07 ± 0.27) at 60 min post injection. MDA-MB-231 xenografts pretreated with nonradioactive precursor NOTA-PEG4-CK2 showed little tumor uptake of [68 Ga]Ga-NOTA-PEG4-CK2 (1.67 ± 0.38%ID/mL at 10 min post injection). Both cellular uptake assay and PET imaging revealed that NRP-1 expression in breast cancer MDA-MB-231 could be effectively suppressed by SB-203580 treatment and can be sensitively detected by [68 Ga]Ga-NOTA-PEG4-CK2. Ex vivo analysis also proved the high specificity and sensitivity of [68 Ga]Ga-NOTA-PEG4-CK2 for NRP-1 expression in MDA-MB-231 xenografts. Conclusion: A promising NRP-1 targeting PET tracer [68 Ga]Ga-NOTA-PEG4-CK2 was successfully prepared. It showed remarkable specificity and sensitivity in monitoring the dynamic changes of NRP-1 expression. Hence, it could provide valuable information for early diagnosis of NRP-1 relevant cancers and evaluating the prognosis of cancer patients. A novel promising NRP-1 targeting PET tracer [68 Ga]Ga-NOTA-PEG4-CK2 was developed based on a series of in vitro and in vivo investigations. The tracer showed remarkable specificity and sensitivity in detecting the expression of NRP-1. It could be applied for noninvasively and dynamically monitoring the NRP-1 expression in tumors and predicting the prognosis of breast cancer. [ABSTRACT FROM AUTHOR]

A PET CT uses x-rays and radionucleotide combined with a pharmaceutical to help reveal the metabolic or biochemical function of tissues or organs. It is an effective and highly sensitive way to detect a variety of conditions including cancer, heart disease and brain disorders. The length of the scanner and scan itself, in combination with the radiation dose can present challenges both the patient and staff. [ABSTRACT FROM AUTHOR]

BackgroundDespite clinical suspicion, most non-invasive ischemia tests for coronary artery disease (CAD) reveal unremarkable results. Patients with a coronary artery calcium score (CACS) of zero rarely have an abnormal positron emission tomography (PET) and could be deferred from further testing. However, most patients have some extent of coronary calcification.ObjectivesCACS percentiles could be useful to exclude abnormal perfusion in patients with CACS >0, but data from patients with 82Rb PET are lacking. The aim of this study was to assess the diagnostic utility of CACS percentiles in comparison to zero calcium and absolute CACS classes.MethodsConsecutive patients with suspected CAD (n = 1,792) referred for 82Rb PET were included and analyzed for abnormal PET (SSS ≥4) and relevant ischemia (>10% myocardium). Test characteristics were calculated.ResultsThe mean age was 65 ± 11 years, 43% were female, and typical angina was reported in 21%. Abnormal PET/relevant ischemia (>10%) were observed in 19.8%/9.3%. Overall, the sensitivity/negative predictive value (NPV) of a 90.9% in all age groups.ConclusionIn patients >50 years, the 10%). They could be used to extend the scope of application of CACS 0 by 8%–10% to 32%–34% overall of patients who could be deferred from further testing.