Frontiers in nuclear medicine (Lausanne, Switzerland)最新文献

Objective: To investigate a cascaded conditional generative adversarial network (cGAN)-based method for synthesizing ¹⁸F-FDG PET images from ¹⁸F-octreotide, optimize model parameter configurations, and evaluate the quality of the synthesized images.

Methods: Imaging data from 71 tumor patients who underwent dual-tracer PET/CT examinations with ¹⁸F-FDG and ¹⁸F-octreotide between May 2022 and November 2025 were retrospectively collected. A cascaded deep learning architecture (first stage: 3D U-Net; second stage: self-attention 3D V-Net) was employed to evaluate the effects of image preprocessing, resampling size, and patch size on synthesis quality. Three preprocessing strategies (log transformation, normalization, and a combined approach) were compared. Image quality was assessed using five objective metrics (SSIM, PSNR, MSE, MAE, and NMI) and Likert 5-point ratings from three nuclear medicine physicians; liver and lesion-level SUV agreement were further evaluated.

Results: In the held-out test cohort (n = 14), the combined preprocessing method (log transformation + normalization) significantly outperformed the individual methods (SSIM: 0.8454 ± 0.0328 vs. 0.7831 ± 0.0337 vs. 0.7438 ± 0.0362, P < 0.001). On the identical held-out test cohort (n = 14), the Group 2 parameter configuration (resampling size: 256 × 256 × 512; patch size: 128 × 128 × 128) outperformed Group 1, with an SSIM of 0.8854 ± 0.0455 and a PSNR of 28.15 ± 3.35 dB, representing relative improvements of 19.1% and 83.5%, respectively, over Group 1 (paired-samples t-test, both P < 0.001). The mean Likert score was 4.53 ± 0.19. Quantitative analysis of liver SUVmean showed no significant difference between synthesized and real images (P = 0.967, paired t-test). Lesion-level quantitative analysis demonstrated high agreement between synthetic and real ¹⁸F-FDG images, with Pearson correlation coefficients of 1.0000, 0.9995, and 0.9991 for SUVmax and SUVmean (n = 14) and SUVpeak (n = 13), respectively (all P < 0.001), no significant differences on paired t-tests (all P > 0.05), and all Bland-Altman differences within the 95% limits of agreement.

Conclusion: The cascaded deep learning model enabled cross-tracer synthesis from ¹⁸F-octreotide to ¹⁸F-FDG PET images. In this exploratory, single-center pilot cohort, the optimized configuration demonstrated promising quantitative and visual performance, supporting image-synthesis feasibility and potential clinical utility of a single-injection, dual-parameter molecular imaging approach for neuroendocrine tumors. Prospective multicenter validation is warranted before any clinical application.

Artificial intelligence in PET/CT is evolving from narrowly optimized systems toward reusable pretrained representations and multimodal models. PET/CT is a demanding test case because clinically meaningful interpretation must preserve quantitative molecular information while integrating anatomic CT, acquisition metadata, longitudinal examinations, and free-text imaging reports. This focused narrative review synthesizes literature reported from January 2021 through July 2026, spanning PET reconstruction and enhancement, attenuation correction, segmentation, outcome modeling, pretrained representations, vision-language systems, and agentic workflows. We distinguish task-specific models, reusable pretrained representations, promptable models, vision-language systems, generalist foundation-model candidates, and agentic systems according to demonstrated capabilities, while separately considering evaluation benchmarks. Task-specific deep learning has demonstrated promising technical performance in low-count imaging and whole-body tumor analysis, but most evidence remains retrospective and restricted by radiotracer, disease, scanner, institution, or endpoint. Emerging PET-native studies demonstrate label-efficient segmentation, image-text grounding, report generation, and modular tool orchestration; however, they do not yet establish broad cross-tracer generalization or prospective clinical benefit. We therefore propose MOSAIC-PET, an author-developed heuristic six-domain evaluation framework for PET/CT foundation models encompassing Molecular fidelity, Organ and anatomic grounding, Semantic faithfulness, Adaptability, Integration and clinical impact, and Continuous governance. MOSAIC-PET has not undergone Delphi consensus or formal validation and is intended to organize PET-specific evidence requirements rather than function as a validated clinical-readiness tool. The near-term translational priority is the development of modular, auditable systems in which validated quantitative tools and evidence-linked language outputs remain under specialist oversight, with progression from external testing to silent evaluation, assisted-use studies, clinical-impact assessment, and continuous lifecycle monitoring.

Purpose: Rubidium-82 (⁸²Rb) PET is typically interpreted using perfusion category and left ventricular ejection fraction (LVEF), but absolute myocardial blood flow (MBF) and flow reserve (MFR) may add information. We investigated whether absolute stress MBF differs between patients with and without prior cardiac disease when studies appear normal, in an Arabian Gulf population with high rates of diabetes and obesity. A secondary exploratory aim compared quantitative parameters between predefined PET categories.

Patients and methods: This retrospective single-centre study included 292 source-verified patients (mean age 63.1 ± 12.1 years; 56.8% male) who underwent adenosine-stress ⁸²Rb PET/CT, classified by the PET C-category system. The primary analysis compared, within normal-appearing studies (C1 perfusion with resting LVEF ≥ 50%; n = 84), those with prior cardiac disease (n = 38) and those without (n = 46). A secondary exploratory analysis compared these same 46 patients (Normal) with the remaining 246 (Abnormal). Analyses used Mann-Whitney U tests with Cohen's d and 95% confidence intervals, with Bonferroni correction.

Results: Among normal-appearing studies, patients with prior cardiac disease had lower global stress MBF (2.25 vs. 2.99 mL/min/g; d = -0.78, 95% CI -1.22 to -0.33) and lower RCA stress MBF (2.25 vs. 3.29; d = -1.02, -1.47 to -0.56); rest MBF was also lower (d = -0.47, -0.91 to -0.04), whereas MFR (d = -0.37, -0.80 to 0.07), LVEF and LV volumes did not differ. In the secondary comparison, stress MBF and MFR separated the predefined categories comparably (DeLong Z = 0.89, p = 0.372) and both more strongly than LVEF (Z = 3.54, p < 0.001); because the categories are defined in part by these parameters, this represents relative separation within the classification scheme and not diagnostic accuracy.

Conclusions: In this Arabian Gulf cohort, patients with prior cardiac disease had lower absolute stress MBF despite normal perfusion category and ejection fraction. These exploratory findings support further prospective evaluation of absolute stress MBF as an adjunct to conventional PET interpretation. Because rest and stress flow fell together with a preserved ratio, and because haemodynamic, medication and infarct-related explanations could not be excluded, no mechanism is inferred.

Background: Quantitative bone SPECT/CT enables objective assessment of skeletal radiotracer uptake using standardised uptake values (SUVs) and related parameters. Advances in reconstruction, correction methods, calibration and detector technology have improved measurement reliability and expanded interest in oncological and benign skeletal applications. This review evaluates current clinical applications, technical limitations and future directions of quantitative bone SPECT/CT.

Methods: PubMed was searched for English-language full-text human studies published from January 2015 to March 2025 using a reproducible Boolean strategy focused on quantitative or semi-quantitative bone SPECT/CT. Screening followed PRISMA 2020 principles. Thirteen primary studies were included in the qualitative synthesis; reviews, case reports, guidelines, position papers and other supporting publications were used only for narrative context.

Results: Quantitative bone SPECT/CT showed potential value for differentiating benign from malignant bone lesions, comparison with 18F-NaF PET/CT, longitudinal response assessment and prognostic evaluation before bone-targeted radionuclide therapy. Potential applications were also identified in condylar hyperplasia, fibrous dysplasia, vertebral compression fractures and degenerative joint disease. However, most studies were retrospective, single-centre and technically heterogeneous. Reported SUV thresholds were influenced by tumour type, lesion phenotype, uptake time, acquisition/reconstruction parameters, calibration, segmentation and recovery-coefficient methods, limiting transferability.

Conclusions: Quantitative bone SPECT/CT is a promising technique for objective assessment of bone metabolism, but current evidence supports cautious use as an adjunct to morphology and clinical context. Prospective multicentre studies with harmonised protocols, risk-of-bias control and patient-relevant endpoints are required. Artificial intelligence, radiomics, advanced detectors and theranostics should currently be considered research opportunities rather than established applications.

Artificial intelligence (AI) has been studied across radiopharmaceutical therapy (RPT); however, evidence for treatment-changing use remains limited. We conducted a structured narrative review with descriptive mapping of learned models for patient stratification, segmentation, tumor-burden quantification, quantitative preprocessing, dosimetry, toxicity prediction, response assessment, and radiation-safety/logistics support. The mapped set contained 73 direct full journal reports and eight direct conference abstracts, with one additional meeting abstract retained as contextual evidence. Among the full reports, 17 were PSMA-related, 16 SSTR/PRRT, 15 radioiodine, 14 ⁹⁰Y radioembolization, and 11 cross-platform or emerging-target reports; 16 addressed segmentation or quantification, 32 selection, response, prognosis, toxicity, or safety/logistics, and 25 registration, preprocessing, or dosimetry. Fifty-one reports were retrospective, one was an explicitly prospective clinical/technical evaluation, nine were technical, synthetic, or phantom evaluations, and temporal design was unclear or conflicting in 12. Seven reported an external-type held-out evaluation, including four that clearly held out an institution; one reported explicit calibration, one propagated task-level uncertainty, and none evaluated a prospective AI-guided treatment policy. The mapped evidence most directly supports human-reviewed measurement and workflow assistance. An eight-domain RPT evidence-to-decision synthesis organizes quantitative fidelity, reference standards, validation, endpoints, biological linkage, oversight, uncertainty and quality assurance, and decision impact; it is not a validated score or adoption standard.

Background and objective: Positron Emission Tomography (PET) is widely used for assessing metabolic activity and diagnosing cancer. Due to the inherently high noise levels in PET data, advanced reconstruction algorithms are essential for accurate imaging. Convolutional Neural Networks (CNNs) within the Learned Primal-Dual (LPD) framework have shown good performance for PET reconstruction; however, the locality assumption imposed by CNNs can limit their ability to capture long-range contextual dependencies in sinogram data. Motivated by our previous work on transformer-based architecture for low-dose and low-count sinogram denoising, we comprehensively investigate the integration of attention mechanisms, both self-attention and cross-attention, within the LPD framework.

Methods: We propose and evaluate three novel transformer-based LPD architectures: the Dual-Domain Stacked Transformer-based LPD, which employs sinusoidal patches to model long-range dependencies in the dual domain (sinogram); the Dual-Domain Restormer-based LPD, a hybrid design that combines CNNs for fine-grained local feature extraction with transformers for global information exchange; and the Dual-Domain UNet-based LPD incorporating Cross-Attention mechanism, which augments the U-Net LPD with cross-attention to capture complementary information across image and sinogram domains. To enable robust transfer learning, we further introduce a system-aligned synthetic data generation process that replicates the MiniPET-3 system, our in-house preclinical PET scanner dedicated to small-animal imaging, incorporating realistic acquisition geometry, resolution blurring, positron range effects, and Poisson noise.

Results: Evaluations on synthetic data demonstrate that both the Stacked Transformer-based LPD and Dual-Domain Restormer-based LPD outperform the UNet-based LPD baseline in MSE and PSNR across all noise levels, with the Restormer additionally achieving higher SSIM at low noise. The UNet-based LPD incorporating a cross-attention mechanism also improves MSE and PSNR over the baseline, while exhibiting a slight reduction in SSIM, indicating a trade-off between pixel-wise accuracy and structural similarity. Trained exclusively on synthetic data, the proposed models generalize well to experimental measurements, producing high-quality reconstructions.

Conclusions: Overall, this work highlights the promise of transformer-integrated LPD frameworks for enhancing PET reconstruction quality and robustness in low-count imaging settings.

Aim: We evaluated whether Magnetic Resonance Imaging (MRI)-Derived T Stage independently predicts nodal disease on Prostate-Specific Membrane Antigen (PSMA) PET/CT in patients with high-risk primary prostate cancer, and developed a predictive model for clinical decision support.

Materials and methods: MRI-derived T Stage, Prostate-Specific Antigen (PSA), Gleason score, age and PSMA node status were analysed using univariable and multivariable logistic regression in 152 men with high-risk primary prostate cancer who underwent pre-treatment multiparametric MRI and PSMA PET/CT. Model performance was assessed with area under the receiver operating characteristic (ROC) curve (AUC), calibration plots, and decision curve analysis. Subgroup analyses evaluated Gleason 3 + 4 and 4 + 3 disease separately.

Results: MRI T3b and T4 stages were independently and significantly associated with increased risk of PSMA-positive nodal disease (p < 0.001 and p = 0.016, respectively). T3a disease was not significantly independently associated with PSMA-positive nodal disease (p = 0.086). T3b remained an independent predictor even in Gleason 4 + 3 cases (p = 0.026; odds ratio ≈ 23.57). The final model (AUC = 0.863) was well-calibrated and demonstrated clinical net benefit on decision curve analysis. In our cohort, patients with PSA < 20 ng/mL, MRI-derived T2 disease, and Gleason score < 8 (including both 3 + 4 and 4 + 3 patterns) demonstrated a 0% rate of PSMA-positive nodal disease.

Conclusion: MRI-derived T stage T3b disease is a strong independent predictor of PSMA-positive nodal disease. These findings support guideline-based staging criteria and support the integration of MRI-derived T Stage into PSMA PET/CT triage workflows.

We report a case that combines several aspects of molecular diagnostics and clinical follow-up with innovative imaging of [⁸⁹Zr]Zr-DFO-trastuzumab positron emission computed tomography (PET/CT) to depict human epidermal growth factor receptor 2 (HER2)-targeted tracer uptake in several metastatic sites in advanced platinum-resistant high-grade serous ovarian cancer (HGSOC). A 74-year-old woman underwent this procedure, and PET/CT images were interpreted alongside conventional biomarkers [CA125 and immunohistochemistry (IHC)] and contrast-enhanced computed tomography (CE-CT). PET imaging revealed intense tracer uptake in the primary ovarian tumour site (SUVratio 13.8), with corresponding HER2 + expression (3+) observed on the IHC of the primary tumour. Moreover, moderate uptake was observed in abdominal wall metastases (SUVratio 3.6) and near-background uptake in hepatic lesions (SUVratio 1.6), consistent with image-suggestive spatial heterogeneity in HER2-targeted tracer uptake. CA125 measurements corroborated early biochemical response, with values declining from 676 to 254 U/mL after six weeks of trastuzumab deruxtecan (T-DXd) treatment as third-line therapy. Follow-up CE-CT showed qualitative regression of [⁸⁹Zr]Zr-DFO-trastuzumab-avid lesions, despite non-measurable disease by RECIST and a subsequent CE-CT imaging after 20 weeks demonstrated a mixed response pattern, without a clear relationship to pre-T-DXd [⁸⁹Zr]Zr-DFO-trastuzumab uptake. This case study illustrates how integrating non-invasive [⁸⁹Zr]Zr-DFO-trastuzumab PET molecular imaging with conventional radiology, IHC, and biochemical markers within the clinical pathway may support individualised therapeutic decision-making.

Objective: To investigate the correlation between baseline ¹⁸F-FDG PET/CT metabolic parameters and progression-free survival (PFS) in EGFR-mutant lung adenocarcinoma receiving EGFR-TKI treatment, with subgroup analysis for exon 19 deletion (19del) and L858R mutations.

Methods: This single-center retrospective study included 175 patients with pathologically confirmed EGFR-mutant lung adenocarcinoma who underwent baseline ¹⁸F-FDG PET/CT prior to EGFR-TKI therapy between 2020 and 2023. Quantitative metabolic parameters, including SUVmax, SUVpeak, metabolic tumor volume (MTV), and total lesion glycolysis (TLG), were measured. Progression-free survival was the primary endpoint. Survival analyses were performed using Kaplan-Meier estimates, log-rank tests, and Cox proportional hazards regression models, with prespecified subgroup analyses according to EGFR mutation subtype (19del vs. L858R).

Results: The median follow-up duration was 48 months. In univariate analysis, higher SUVmax, SUVpeak, MTV, and TLG were significantly associated with shorter PFS (all P < .05), while MTV and TLG failed to reach statistical significanceOn multivariate Cox regression analysis, SUVmax remained independently associated with PFS [hazard ratio (HR) = 1.031, 95% confidence interval 1.001-1.059, P = .045]. Patients with EGFR 19del mutations demonstrated a significantly longer median PFS compared with those harboring L858R mutations (13.57 vs. 10.37 months, P = .02). Third-generation EGFR-TKIs were associated with a significant prolongation of PFS in the 19del subgroup (P < .05), whereas no significant benefit was observed in the L858R subgroup, these subgroup findings should be interpreted cautiously as preliminary exploratory observations limited by small sample sizes.

Conclusion: Baseline ¹⁸F-FDG PET/CT metabolic parameters, especially SUVmax, are independent prognostic biomarkers for PFS in EGFR-mutant lung adenocarcinoma patients receiving EGFR-TKIs therapy. The prognostic implications of metabolic imaging differ according to EGFR mutation subtype, and further large prospective cohorts are needed to validate our exploratory subgroup findings supporting an integrated approach combining metabolic imaging and molecular stratification for individualized risk assessment.

Introduction: Sparse-view computed tomography (CT) reconstruction is crucial for clinical diagnostics, as reducing radiation exposure is essential to minimize risks to patients. Existing dual-domain reconstruction methods leverage both image and projection domains but often process them sequentially, overlooking their implicit correlations.

Methods: To address this limitation, we propose Cross-Domain TransNet, a Transformer-based dual-domain framework for sparse-view CT reconstruction. The proposed model captures long-range dependencies within each domain and integrates image and sinogram representations through a hybrid self-attention mechanism. In addition, a Convolution Fusion Layer (CFL) is introduced to enhance feature interactions and facilitate more effective utilization of dual-domain information.

Results: Extensive experiments on the NIH-AAPM dataset demonstrate the superior performance and generalization capability of the proposed method under various sparse-view settings. The results show that Cross-Domain TransNet consistently improves reconstruction quality, effectively suppresses noise, and reduces artifacts, outperforming both conventional reconstruction algorithms and state-of-the-art deep learning approaches.

Conclusion: Cross-Domain TransNet provides an effective and robust solution for sparse-view CT reconstruction. By fully exploiting complementary information from both image and projection domains, the proposed framework enhances diagnostic image quality while supporting radiation dose reduction.