Deep learning has demonstrated remarkable efficacy in reconstructing low-count PET (Positron Emission Tomography) images, attracting considerable attention in the medical imaging community. However, most existing deep learning approaches have not fully exploited the unique physical characteristics of PET imaging in the design of fidelity and prior regularization terms, resulting in constrained model performance and interpretability. In light of these considerations, we introduce an unrolled deep network based on maximum likelihood estimation for the Poisson distribution and a Generalized domain transformation for Sparsity learning, dubbed GS-Net. To address this complex optimization challenge, we employ the Alternating Direction Method of Multipliers (ADMM) framework, integrating a modified Expectation Maximization (EM) approach to address the primary objective and utilize the shrinkage thresholding approach to optimize the L1 norm term. Additionally, within this unrolled deep network, all hyperparameters are adaptively adjusted through end-to-end learning to eliminate the need for manual parameter tuning. Through extensive experiments on simulated patient brain datasets and real patient whole-body clinical datasets with multiple count levels, our method has demonstrated advanced performance compared to traditional non-iterative and iterative reconstruction, deep learning-based direct reconstruction, and hybrid unrolled methods, as demonstrated by qualitative and quantitative evaluations.

Prostate-specific membrane antigen (PSMA) PET/CT plays an increasing role in prostate cancer management. Radiomics analysis of PSMA PET/CT images may provide additional information for risk stratification. This study aimed to evaluate the performance of PSMA PET/CT radiomics analysis in differentiating between Gleason Grade Groups (GGG 1–3 vs. GGG 4–5) and predicting PSA levels (below vs. at or above 20 ng/ml) in patients with newly diagnosed prostate cancer. In this multicenter study, patients with confirmed primary prostate cancer were enrolled who underwent [68Ga]Ga-PSMA PET/CT for staging. Inclusion criteria required intraprostatic lesions on PET and the International Society of Urological Pathology (ISUP) grade information. Three different segments were delineated including intraprostatic PSMA-avid lesions on PET, the whole prostate in PET, and the whole prostate in CT. Radiomic features (RFs) were extracted from all segments. Dimensionality reduction was achieved through principal component analysis (PCA) prior to model training on data from two centers (186 cases) with 10-fold cross-validation. Model performance was validated with external data set (57 cases) using various machine learning models including random forest, nearest centroid, support vector machine (SVM), calibrated classifier CV and logistic regression. In this retrospective study, 243 patients with a median age of 69 (range: 46–89) were enrolled. For distinguishing GGG 1–3 from GGG 4–5, the nearest centroid classifier using radiomic features (RFs) from whole-prostate PET achieved the best performance in the internal test set, while the random forest classifier using RFs from PSMA-avid lesions in PET performed best in the external test set. However, when considering both internal and external test sets, a calibrated classifier CV using RFs from PSMA-avid PET data showed slightly improved overall performance. Regarding PSA level classification (< 20 ng/ml vs. ≥20 ng/ml), the nearest centroid classifier using RFs from the whole prostate in PET achieved the best performance in the internal test set. In the external test set, the highest performance was observed using RFs derived from the concatenation of PET and CT. Notably, when combining both internal and external test sets, the best performance was again achieved with RFs from the concatenated PET/CT data. Our research suggests that [68Ga]Ga-PSMA PET/CT radiomic features, particularly features derived from intraprostatic PSMA-avid lesions, may provide valuable information for pre-biopsy risk stratification in newly diagnosed prostate cancer.

We conducted a systematic review and meta-analysis to assess and compare the diagnostic performance of prostate-specific membrane antigen positron-emission tomography (PSMA PET) with conventional imaging modalities in detecting biochemical recurrence of prostate cancer, and to assess the role of artificial intelligence in this context. A comprehensive search of PubMed, Embase, Web of Science, the Cochrane Library, and Scopus was conducted for studies, initially on May 7, 2025, and updated on July 28, 2025. Studies that compared PSMA PET with conventional imaging and assessed artificial intelligence for detecting biochemical recurrence of prostate cancer were considered. The QUADAS-2 technique was employed to evaluate study quality. Diagnosis accuracy and detection rates were aggregated utilizing a bivariate random-effects model. A total of 7637 patients from 67 studies were included. PSMA PET demonstrated significantly higher overall diagnostic accuracy for biochemical recurrence of prostate cancer compared to mpMRI, CT, and AI test sets, with accuracy values of (0.89 vs. 0.71, 0.45, and 0.76, P<0.01). For local recurrence, mpMRI outperformed PSMA PET and CT (0.93 vs. 0.84 and 0.77, P<0.01). PSMA PET was superior in detecting lymph node metastasis than mpMRI and CT (0.89 vs. 0.79 and 0.72, P<0.05). For bone metastasis, PSMA PET outperformed mpMRI, CT, and Bone scan (0.95 vs. 0.85, 0.81, and 0.80, P<0.05). For visceral metastasis, PSMA PET outperformed mpMRI (0.96 vs. 0.89, P=0.23), and CT (0.96 vs. 0.78, P<0.05). 21 studies involving 3113 samples were included to evaluate the performance of artificial intelligence in detecting biochemical recurrence of prostate cancer. The pooled sensitivity, specificity, DOR, and AUC of AI test sets in detecting biochemical recurrence of prostate cancer were 0.77, 0.76, 10.39, and 0.79. Heterogeneity limits the generalizability of our findings. PSMA PET outperformed mpMRI and CT in detecting overall, local recurrence, bone, and visceral metastasis, while mpMRI was more effective for local recurrence. While AI exhibits potential diagnostic efficacy. Despite promising results, heterogeneity and limited validation highlight the need for further research to support routine clinical use.

&#xD;This study aimed to evaluate the use of deep learning techniques to produce measured attenuation-corrected (MAC) images from non-attenuation-corrected (NAC) F-FDG PET images, focusing on head and neck imaging.&#xD;Materials and Methods:&#xD;A Residual Network (ResNet) was used to train 2D head and neck PET images from 114 patients (12,068 slices) without pathology or artifacts. For validation during training and testing, 21 and 24 patient images without pathology and artifacts were used, and 12 images with pathologies were used for independent testing. Prediction accuracy was assessed using metrics such as RMSE, SSIM, PSNR, and MSE. The impact of unseen pathologies on the network was evaluated by measuring contrast and SNR in tumoral/hot regions of both reference and predicted images. Statistical significance between the contrast and SNR of reference and predicted images was assessed using a paired-sample t-test.&#xD;Results:&#xD;Two nuclear medicine physicians evaluated the predicted head and neck MAC images, finding them visually similar to reference images. In the normal test group, PSNR, SSIM, RMSE, and MSE were 44.02 ± 1.77, 0.99 ± 0.002, 0.007 ± 0.0019, and 0.000053 ± 0.000030, respectively. For the pathological test group, values were 43.14 ± 2.10, 0.99 ± 0.005, 0.0078 ± 0.0015, and 0.000063 ± 0.000026, respectively. No significant differences were found in SNR and contrast between reference and test images without pathology (p-value>0.05), but significant differences were found in pathological images (p-value <0.05)&#xD;Conclusion:&#xD;The deep learning network demonstrated the ability to directly generate head and neck MAC images that closely resembled the reference images. With additional training data, the model has the potential to be utilized in dedicated head and neck PET scanners without the requirement of computed tomography [CT] for attenuation correction.&#xD.

Combined PET/CT imaging provides critical insights into both anatomic and molecular processes, yet traditional single-tracer approaches limit multidimensional disease phenotyping; to address this, we developed the PET Unified Multitracer Alignment (PUMA) framework-an open-source, postprocessing tool that multiplexes serial PET/CT scans for comprehensive voxelwise tissue characterization. <b>Methods:</b> PUMA utilizes artificial intelligence-based CT segmentation from multiorgan objective segmentation to generate multilabel maps of 24 body regions, guiding a 2-step registration: affine alignment followed by symmetric diffeomorphic registration. Tracer images are then normalized and assigned to red-green-blue channels for simultaneous visualization of up to 3 tracers. The framework was evaluated on longitudinal PET/CT scans from 114 subjects across multiple centers and vendors. Rigid, affine, and deformable registration methods were compared for optimal coregistration. Performance was assessed using the Dice similarity coefficient for organ alignment and absolute percentage differences in organ intensity and tumor SUV_mean <b>Results:</b> Deformable registration consistently achieved superior alignment, with Dice similarity coefficient values exceeding 0.90 in 60% of organs while maintaining organ intensity differences below 3%; similarly, SUV_mean differences for tumors were minimal at 1.6% ± 0.9%, confirming that PUMA preserves quantitative PET data while enabling robust spatial multiplexing. <b>Conclusion:</b> PUMA provides a vendor-independent solution for postacquisition multiplexing of serial PET/CT images, integrating complementary tracer data voxelwise into a composite image without modifying clinical protocols. This enhances multidimensional disease phenotyping and supports better diagnostic and therapeutic decisions using serial multitracer PET/CT imaging.

&#xD;Deep learning has shown great promise for improving medical image reconstruction, including PET. However, concerns remain about the stability and robustness of these methods, especially when trained on limited data. This work aims to explore the use of the Plug-and-Play (PnP) framework in PET reconstruction to address these concerns.&#xD;&#xD;Approach:&#xD;We propose a convergent PnP algorithm for low-count PET reconstruction based on the Douglas-Rachford splitting method. We consider several denoisers trained to satisfy fixed-point conditions, with convergence properties ensured either during training or by design, including a spectrally normalized network and a deep equilibrium model. We evaluate the bias-standard deviation tradeoff across clinically relevant regions and an unseen pathological case in a synthetic experiment and a real study. Comparisons are made with model-based iterative reconstruction, post-reconstruction denoising, a deep end-to-end unfolded network and PnP with a Gaussian denoiser.&#xD;&#xD;Main Results:&#xD;Our method achieves lower bias than post-reconstruction processing and reduced standard deviation at matched bias compared to model-based iterative reconstruction. While spectral normalization underperforms in generalization, the deep equilibrium model remains competitive with convolutional networks for plug-and-play reconstruction and generalizes better to the unseen pathology. Compared to the end-to-end unfolded network, it also generalizes more consistently.&#xD;&#xD;Significance:&#xD;This study demonstrates the potential of the PnP framework to improve image quality and quantification accuracy in PET reconstruction. It also highlights the importance of how convergence conditions are imposed on the denoising network to ensure robust and generalizable performance.

Tebentafusp has emerged as the first systemic therapy to significantly prolong survival in treatment-naïve HLA-A*02:01 + patients with unresectable or metastatic uveal melanoma (mUM). Notably, a survival benefit has been observed even in the absence of radiographic response. This study aims to investigate the feasibility and prognostic value of artificial intelligence (AI)-assisted quantification and metabolic response assessment of [¹⁸F]FDG long axial field-of-view (LAFOV) PET/CT in mUM patients undergoing tebentafusp therapy. Fifteen patients with mUM treated with tebentafusp underwent [¹⁸F]FDG LAFOV PET/CT at baseline and 3 months post-treatment. Total metabolic tumor volume (TMTV) and total lesion glycolysis (TLG) were quantified using a deep learning-based segmentation tool On the RECOMIA platform. Metabolic response was assessed according to AI-assisted PERCIST 1.0 criteria. Associations between PET-derived parameters and overall survival (OS) were evaluated using Kaplan-Meier survival analysis. The median follow up (95% CI) was 14.1 months (12.9 months - not available). Automated TMTV and TLG measurements were successfully obtained in all patients. Elevated baseline TMTV and TLG were significantly associated with shorter OS (TMTV: 16.9 vs. 27.2 months; TLG: 16.9 vs. 27.2 months; p < 0.05). Similarly, higher TMTV and TLG at 3 months post-treatment predicted poorer survival outcomes (TMTV: 14.3 vs. 24.5 months; TLG: 14.3 vs. 24.5 months; p < 0.05). AI-assisted PERCIST response evaluation identified six patients with disease control (complete metabolic response, partial metabolic response, stable metabolic disease) and nine with progressive metabolic disease. A trend toward improved OS was observed in patients with disease control (24.5 vs. 14.6 months, p = 0.08). Circulating tumor DNA (ctDNA) levels based on GNAQ and GNA11 mutations were available in 8 patients; after 3 months Of tebentafusp treatment, 5 showed reduced Or stable ctDNA levels, and 3 showed an increase (median OS: 24.5 vs. 3.3 months; p = 0.13). Patients with increasing ctDNA levels exhibited significantly higher TMTV and TLG on follow-up imaging. AI-assisted whole-body quantification of [1⁸F]FDG PET/CT and PERCIST-based response assessment are feasible and hold prognostic significance in tebentafusp-treated mUM. TMTV and TLG may serve as non-invasive imaging biomarkers for risk stratification and treatment monitoring in this malignancy.

Lymphoma staging plays a pivotal role in treatment planning and prognosis. Yet, it still relies on manual interpretation of PET/computed tomography (CT) images, which is time-consuming, subjective, and prone to variability. This study introduces a novel radiomics-based machine learning model for automated lymphoma staging to improve diagnostic accuracy and streamline clinical workflow. Imaging data from 241 patients with histologically confirmed lymphoma were retrospectively analyzed. Radiomics features were extracted from segmented lymph nodes and extranodal lesions using PET/CT. Three machine learning classifiers (Logistic Regression, Random Forest, and XGBoost) were trained to distinguish between early-stage (I-II) and advanced-stage (III-IV) lymphoma. Model performance was evaluated using area under the curve (AUC), sensitivity, specificity, and accuracy together with survival analysis. Among the three models evaluated, the logistic regression model incorporating both nodal and extranodal radiomic features performed the best, achieving an AUC of 0.87 and a sensitivity of 0.88 in the external validation cohort. Including extranodal features significantly improved classification accuracy compared to nodal-only models (AUC: 0.87 vs. 0.75). Survival analysis revealed advanced-stage patients had a fourfold higher mortality risk (hazard ratio: 0.22-0.26, P = 0.0036) and a median survival of 84 months. Key radiomic features, such as tumor shape irregularity and heterogeneity, were strongly associated with staging, aligning with Lugano criteria for extranodal spread. This study demonstrated the potential of PET radiomics features for automated Lugano staging. Adding extranodal features significantly improved staging accuracy and informed treatment.

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Positron emission tomography/computed tomography (PET/CT) imaging plays a pivotal role in oncology, aiding tumor metabolism assessment, disease staging, and therapy response evaluation. Traditionally, semi-quantitative metrics such as SUVmax have been extensively used, though these methods face limitations in reproducibility and predictive capability. Recent advancements in artificial intelligence (AI), particularly deep learning, have revolutionized PET imaging, significantly enhancing image quantification accuracy, and biomarker extraction capabilities, thereby enabling more precise clinical decision-making.