To evaluate the diagnostic performance of the PET Assisted Reporting System (PARS) in nasopharyngeal carcinoma (NPC) patients without distant metastasis, and to investigate the prognostic significance of the metabolic parameters. Eighty-three NPC patients who underwent pretreatment 18F-FDG PET/CT were retrospectively collected. First, the sensitivity, specificity, and accuracy of PARS for diagnosing malignant lesions were calculated, using histopathology as the gold standard. Next, metabolic parameters of the primary tumor were derived using both PARS and manual segmentation. The differences and consistency between the 2 methods were analyzed. Finally, the prognostic value of PET metabolic parameters was evaluated. Prognostic analysis of progression-free survival (PFS) and overall survival (OS) was conducted. PARS demonstrated high patient-based accuracy (97.2%), sensitivity (88.9%), and specificity (97.4%), and 96.7%, 84.0%, and 96.9% based on lesions. Manual segmentation yielded higher metabolic tumor volume (MTV) and total lesion glycolysis (TLG) than PARS. Metabolic parameters from both methods were highly correlated and consistent. ROC analysis showed metabolic parameters exhibited differences in prognostic prediction, but generally performed well in predicting 3-year PFS and OS overall. MTV and age were independent prognostic factors; Cox proportional-hazards models incorporating them showed significant predictive improvements when combined. Kaplan-Meier analysis confirmed better prognosis in the low-risk group based on combined indicators (χ² = 42.25, P < 0.001; χ² = 20.44, P < 0.001). Preliminary validation of PARS in NPC patients without distant metastasis shows high diagnostic sensitivity and accuracy for lesion identification and classification, and metabolic parameters correlate well with manual. MTV reflects prognosis, and its combination with age enhances prognostic prediction and risk stratification.

Cardiac perfusion PET is commonly used to assess ischemia and cardiovascular risk, which enables quantitative measurements of myocardial blood flow (MBF) through kinetic modeling. However, the estimation of kinetic parameters is challenging due to the noisy nature of short dynamic frames and limited sample data points. This work aimed to investigate the errors in MBF estimation in PET through a simulation study and to evaluate different parameter estimation approaches, including a deep learning (DL) method. Simulated studies were generated using digital phantoms based on cardiac segmentations from 55 clinical CT images. We employed the irreversible 2-tissue compartmental model and simulated dynamic ¹³N-ammonia PET scans under both rest and stress conditions (220 cases each). The simulations covered a rest K₁ range of 0.6 to 1.2 and a stress K₁ range of 1.2 to 3.6 (unit: mL/min/g) in the myocardium. A transformer-based DL model was trained on the simulated dataset to predict parametric images (PIMs) from noisy PET image frames and was validated using 5-fold cross-validation. We compared the DL method with the voxel-wise nonlinear least squares (NLS) fitting applied to the dynamic images, using either Gaussian filter (GF) smoothing (GF-NLS) or a dynamic nonlocal means (DNLM) algorithm for denoising (DNLM-NLS). Two patients with coronary CT angiography (CTA) and fractional flow reserve (FFR) were enrolled to test the feasibility of applying DL models on clinical PET data. The DL method showed clearer image structures with reduced noise compared to the traditional NLS-based methods. In terms of mean absolute relative error (MARE), as the rest K₁ values increased from 0.6 to 1.2 mL/min/g, the overall bias in myocardium K₁ estimates decreased from approximately 58% to 45% for the NLS-based methods while the DL method showed a reduction in MARE from 42% to 18%. For stress data, as the stress K₁ decreased from 3.6 to 1.2 mL/min/g, the MARE increased from 30% to 70% for the GF-NLS method. In contrast, both the DNLM-NLS (average: 42%) and the DL methods (average: 20%) demonstrated significantly smaller MARE changes as stress K₁ varied. Regarding the regional mean bias (±standard deviation), the GF-NLS method had a bias of 6.30% (±8.35%) of rest K₁, compared to 1.10% (±8.21%) for DNLM-NLS and 6.28% (±14.05%) for the DL method. For the stress K₁, the GF-NLS showed a mean bias of 10.72% (±9.34%) compared to 1.69% (±8.82%) for DNLM-NLS and -10.55% (±9.81%) for the DL method. This study showed that an increase in the tracer uptake rate (K₁) corresponded to improved accuracy and precision in MBF quantification, whereas lower tracer uptake resulted in higher noise in dynamic PET and poorer parameter estimates. Utilizing denoising techniques or DL approaches can mitigate noise-induced bias in PET parametric imaging.

Reorienting cardiac positron emission tomography (PET) images to the transaxial plane is essential for cardiac PET image analysis. This study aims to design a convolutional neural network (CNN) for automatic reorientation and evaluate its generalizability. An artificial intelligence (AI) method integrating U-Net and the differentiable spatial to numeric transform module (DSNT-U) was proposed to automatically position three feature points (P_apex, P_base, and P_RV), with these three points manually located by an experienced radiologist as the reference standard (RS). A second radiologist performed manual location for reproducibility evaluation. The DSNT-U, initially trained and tested on a [¹¹C]acetate dataset (training/testing: 40/17), was further compared with a CNN-spatial transformer network (CNN-STN). The network fine-tuned with 4 subjects was tested on a [¹³N]ammonia dataset (n = 30). The performance of the DSNT-U was evaluated in terms of coordinates, volume, and quantitative indexes (pharmacokinetic parameters and total perfusion deficit). The proposed DSNT-U successfully achieved automatic myocardial reorientation for both [¹¹C]acetate and [¹³N]ammonia datasets. For the former dataset, the intraclass correlation coefficients (ICCs) between the coordinates predicted by the DSNT-U and the RS exceeded 0.876. The average normalized mean squared error (NMSE) between the short-axis (SA) images obtained through DSNT-U-based reorientation and the reference SA images was 0.051 ± 0.043. For pharmacokinetic parameters, the R² between the DSNT-U and the RS was larger than 0.968. Compared with the CNN-STN, the DSNT-U demonstrated a higher ICC between the estimated rigid transformation parameters and the RS. After fine-tuning on the [¹³N]ammonia dataset, the average NMSE between the SA images reoriented by the DSNT-U and the reference SA images was 0.056 ± 0.046. The ICC between the total perfusion deficit (TPD) values computed from DSNT-U-derived images and the reference values was 0.981. Furthermore, no significant differences were observed in the performance of the DSNT-U prediction among subjects with different genders or varying myocardial perfusion defect (MPD) statuses. The proposed DSNT-U can accurately position P_apex, P_base, and P_RV on the [¹¹C]acetate dataset. After fine-tuning, the positioning model can be applied to the [¹³N]ammonia perfusion dataset, demonstrating good generalization performance. This method can adapt to data of different genders (with or without MPD) and different tracers, displaying the potential to replace manual operations.

The study of the human phenome is essential for understanding the complexities of wellness and disease and their transitions, with molecular imaging being a vital tool in this exploration. Molecular imaging embodies the 4 principles of human phenomics: precise measurement, accurate calculation or analysis, well-controlled manipulation or intervention, and innovative invention or creation. Its application has significantly enhanced the precision, individualization, and effectiveness of medical interventions. This article provides an overview of molecular imaging's technologic advancements and presents the potential use of molecular imaging in human phenomics and precision medicine. The integration of molecular imaging with multiomics data and artificial intelligence has the potential to transform health care, promoting proactive and preventive strategies. This evolving approach promises to deepen our understanding of the human phenome, lead to preclinical diagnostics and treatments, and establish quantitative frameworks for precision health management.

Fast PET imaging is clinically important for reducing motion artifacts and improving patient comfort. While recent diffusion-based deep learning methods have shown promise, they often fail to capture the true PET degradation process, suffer from accumulated inference errors, introduce artifacts, and require extensive reconstruction iterations. To address these challenges, we propose a novel multistage diffusion framework tailored for fast PET imaging. At the coarse level, we design a multistage structure to approximate the temporal non-linear PET degradation process in a data-driven manner, using paired PET images collected under different acquisition duration. A Phase Error Correction Network (PECNet) ensures consistency across stages by correcting accumulated deviations. At the fine level, we introduce a deterministic cold diffusion mechanism, which simulates intra-stage degradation through interpolation between known acquisition durations-significantly reducing reconstruction iterations to as few as 10. Evaluations on [⁶⁸Ga]FAPI and [¹⁸F]FDG PET datasets demonstrate the superiority of our approach, achieving peak PSNRs of 36.2 dB and 39.0 dB, respectively, with average SSIMs over 0.97. Our framework offers high-fidelity PET imaging with fewer iterations, making it practical for accelerated clinical imaging.

Quantitative cardiovascular PET/CT imaging is useful in the diagnosis of multiple cardiac perfusion and motion pathologies. The common approach for cardiac segmentation consists in using co-registered CT images, exploiting publicly available deep learning (DL)-based segmentation models. However, the mismatch between structural CT images and PET uptake limits the usefulness of these approaches. Besides, the performance of DL models is not consistent over low-dose or ultra-low-dose CT images commonly used in clinical PET/CT imaging. In this work, we developed a DL-based methodology to tackle this issue by segmenting directly cardiac PET images. This study included 406 cardiac PET images from 146 patients (43 ¹⁸F-FDG, 329 ¹³N-NH₃, and 37 ⁸²Rb images). Using previously trained DL nnU-Net models in our group, we segmented the whole heart and the three main cardiac components, namely the left myocardium (LM), left ventricle cavity (LV), and right ventricle (RV) on co-registered CT images. The segmentation was resampled to PET resolution and edited through a combination of automated image processing and manual correction. The corrected segmentation masks and SUV PET images were fed to a nnU-Net V2 pipeline to be trained in fivefold data split strategy by defining two tasks: task #1 for whole cardiac segmentation and task #2 for segmentation of three cardiac components. Fifteen cardiac images were used as external validation set. The DL delineated masks were compared with standard of reference masks using Dice coefficient, Jaccard distance, mean surface distance, and segment volume relative error (%). Task #1 average Dice coefficient in internal validation fivefold was 0.932 ± 0.033. The average Dice on the 15 external cases were comparable with the fivefold Dice reaching an average of 0.941 ± 0.018. Task #2 average Dice in fivefold validation was 0.88 ± 0.063, 0.828 ± 0.091, and 0.876 ± 0.062 for LM, LV, and RV, respectively. There was no statistically significant difference among the Dice coefficients, neither between images acquired by three radiotracers nor between the different folds (P-values >  > 0.05). The overall average volume prediction error in cardiac components segmentation was less than 2%. We developed an automated DL-based segmentation pipeline to segment the whole heart and cardiac components with acceptable accuracy and robust performance in the external test set and over three radiotracers used in nuclear cardiovascular imaging. The proposed methodology can overcome unreliable segmentations performed on CT images.