Introduction: Positron emission tomography (PET) has undergone transformative advancements, evolving from a research tool into a cornerstone of precision medicine. Objective: This review highlights key developments in PET imaging, including the introduction of specialized systems such as brain and breast-dedicated scanners, total-body PET, and hybrid PET/CT and PET/MRI technologies. Methods: These innovations have significantly enhanced diagnostic accuracy and patient management across oncology, neurology and cardiology. The emergence of novel radiotracers beyond fluorodeoxyglucose (FDG) has expanded PET's clinical applications by targeting specific molecular pathways, improving sensitivity and specificity in disease characterization. Notable tracers include those for tumor proliferation, hypoxia and receptor-specific imaging, which facilitate personalized treatment strategies. The integration of artificial intelligence (AI) has revolutionized PET imaging by improving image reconstruction, noise reduction, motion correction and lesion segmentation. AI-driven tools enhance diagnostic precision while reducing scan times and radiation exposure, making PET safer and more efficient. Furthermore, AI accelerates radiotracer development by optimizing molecular design and enabling personalized dosimetry planning for theranostic applications. Total-body PET scanners represent a technological milestone, offering unparalleled sensitivity, reduced radiation doses, faster scans, the ability to track systemic diseases comprehensively and to enhance diagnosis by novel imaging biomarkers. These advancements enable earlier disease detection, precise monitoring of treatment efficacy and deeper insights into disease mechanisms. Results: Collectively, these innovations underscore PET's transformative role in advancing precision medicine through early diagnosis, disease monitoring and tailored therapeutic interventions. Conclusions: This review concludes that ongoing technological progress will continue to redefine the capabilities of PET imaging in clinical practice and research.

Background: PSMA PET radiomics is a promising tool for primary prostate cancer (PCa) characterisation. However, small single-centre studies and lack of external validation hinder definitive conclusions on the potential of PSMA PET radiomics in the initial workup of PCa. We aimed to validate a radiomics signature in a larger internal cohort and in an external cohort from a separate centre. Methods: One hundred and twenty-seven PCa patients were retrospectively enrolled across two independent hospitals. The first centre (IRCCS San Raffaele Scientific Institute, Centre 1) contributed 62 [⁶⁸Ga]Ga-PSMA-11 PET scans, 20 patients classified as low-grade (ISUP grade < 4), and 42 as high-grade (ISUP grade ≥ 4). The second centre (Stanford University Hospital, Centre 2) provided 65 [⁶⁸Ga]Ga-PSMA-11 PET scans, and 49 low-grade and 16 high-grade patients. A radiomics model previously generated in Centre 1 was tested on the two cohorts separately and afterward on the entire dataset. Then, we evaluated whether the radiomics features selected in the previous investigation could generalise to new data. Several machine learning (ML) models underwent training and testing using 100-fold Monte Carlo cross-validation, independently at both Centre 1 and Centre 2, with a 70–30% train–test split. Additionally, models were trained in one centre and tested in the other, and vice versa. Furthermore, data from both centres were combined for training and testing using Monte Carlo cross-validation. Finally, a new radiomics signature built on this bicentric dataset was proposed. Several performance metrics were computed. Results: The previously generated radiomics signature resulted in an area under the receiver operating characteristic curve (AUC) of 80.4% when tested on Centre 1, while it generalised poorly to Centre 2, where it reached an AUC of 62.7%. When the whole cohort was considered, AUC was 72.5%. Similarly, new ML models trained on the previously selected features yielded, at best, an AUC of 80.9% for Centre 1 and performed at chance for Centre 2 (AUC of 49.3%). A new signature built on this bicentric dataset reached, at best, an average AUC of 91.4% in the test set. Conclusions: The satisfying performance of radiomics models when used in the original development settings, paired with the poor performance otherwise observed, emphasises the need to consider centre-specific factors and dataset characteristics when developing radiomics models. Combining radiomics datasets is a viable strategy to reduce such centre-specific biases, but external validation is still needed.

Magnetic resonance imaging (MRI) is a widely used imaging modality to evaluate brain disorders. MRI generates huge volumes of data, which consist of a sequence of scans taken at different instances of time. As the presence of brain disorders has to be evaluated on all magnetic resonance (MR) sequences, manual brain disorder detection becomes a tedious process and is prone to inter- and intra-rater errors. A technique for detecting abnormalities in brain MRI using template matching is proposed. Bias filed correction is performed on volumetric scans using N4ITK filter, followed by volumetric registration. Normalized cross-correlation template matching is used for image registration taking into account, the rotation and scaling operations. A template of abnormality is selected which is then matched in the volumetric scans, if found, the corresponding image is retrieved. Post-processing of the retrieved images is performed by the thresholding operation; the coordinates and area of the abnormality are reported. The experiments are carried out on the glioma dataset obtained from Brain Tumor Segmentation Challenge 2013 database (BRATS 2013). Glioma dataset consisted of MR scans of 30 real glioma patients and 50 simulated glioma patients. NVIDIA Compute Unified Device Architecture framework is employed in this paper, and it is found that the detection speed using graphics processing unit is almost four times faster than using only central processing unit. The average Dice and Jaccard coefficients for a wide range of trials are found to be 0.91 and 0.83, respectively.

Amodal segmentation is a critical task in the field of computer vision as it involves accurately estimating object boundaries that extend beyond occlusion. This paper introduces a network named after the Amodal Segmentation Head, ASH-Net, a novel architecture specifically designed for amodal segmentation. ASH-Net is comprised of a ResNet-50 backbone, a Feature Pyramid Network middle layer, and an Amodal Segmentation Head. The evaluation encompasses three diverse datasets, namely COCOA-cls, KINS, and D2SA, providing a comprehensive analysis of ASH-Net’s capabilities. The results obtained demonstrate the superiority of ASH-Net in accurately estimating object boundaries beyond occlusion across multiple datasets. Specifically, ASH-Net achieves an Average Precision of 62.15% on the COCOA-cls dataset, 72.58% on the KINS dataset, and an impressive 91.4% on the D2SA dataset. Through extensive evaluation using average precision and average recall metrics, ASH-Net exhibits exceptional performance compared to state-of-the-art models. These findings highlight the remarkable performance of ASH-Net in overcoming occlusion challenges and accurately delineating object boundaries. This research identifies optimal training parameters, such as coefficient dimensions and aspect ratios, that significantly enhance segmentation performance while maintaining computational efficiency. The proposed ASH-Net architecture and its performance pave the way for improved object recognition, enhanced scene understanding, and the development of practical applications in various domains.