Positron emission tomography (PET) imaging allows for measurement of activity concentrations of a given radiotracer in vivo . The quantitative capabilities of PET imaging are particularly important in the context of monitoring response to treatment, where quantitative changes in tracer uptake could be used as a biomarker of treatment response. Reconstruction algorithms and settings have a significant impact on PET quantification. In this work we introduce a novel harmonization methodology requiring only a simple cylindrical phantom and show that it can match the performance of more complex harmonization approaches based on phantoms with spherical inserts. Resolution and noise measurements from cylindrical phantoms are used to simulate the spherical inserts from NEMA image quality phantoms. An optimization algorithm was used to find the optimal smoothing filters for the simulated NEMA phantom images to identify those that best harmonized the PET scanners. Our methodology was tested on seven different PET models from two manufacturers installed at five institutions. Our methodology is able to predict contrast recovery coefficients (CRCs) from NEMA phantoms with errors within ±5.2% for CRCmax and ±3.7% for CRCmean (limits of agreement = 95%). After applying the proposed harmonization protocol, all the CRC values were within the tolerances from EANM. Quantitative harmonization in compliance with the EARL FDG-PET/CT accreditation program is achieved in a simpler way, without the need of NEMA phantoms. This may lead to simplified scanner harmonization workflows more accessible to smaller institutions.

Background/Objectives: Artificial intelligence (AI) is an emerging technology for diagnosing liver fibrosis in Metabolic-Dysfunction-Associated Steatotic Liver Disease (MASLD), but a comprehensive synthesis of its performance is lacking. This systematic review (SR) aimed to evaluate the current evidence of AI models for diagnosing or staging liver fibrosis in patients with MASLD compared to conventional diagnostic tools. Methods: A comprehensive search was conducted in PubMed, Scopus, Web of Science, ScienceDirect, Embase, LILACS, IEEE Series, and Association for Computing Machinery (ACM). Primary studies applying AI to diagnose fibrosis in adults with MASLD were included. Risk of bias was assessed using the QUADAS-2 tool, and methodological reporting was evaluated according to the MINimum Information for Medical AI Reporting (MINIMAR) guideline. A narrative synthesis was performed, grouping studies by data type (clinical/laboratory vs. imaging) and summarizing diagnostic performance and clinical application. A frequency-based analysis was applied to identify the most recurrent predictive features, and an analysis of the AI architecture and application was reported. The review was registered in PROSPERO (CRD420251035919). Results: Twenty-one studies were included, encompassing 19,221 patients and 5237 images. Across studies, AI models consistently outperformed non-invasive scores such as Fibrosis-4 Index (FIB-4) and NAFLD Fibrosis Score (NFS). The most frequent predictive variables were identified. Despite an overall low risk of bias, methodological transparency and external validation were limited. Conclusions: AI is feasible for the non-invasive diagnosis of liver fibrosis in MASLD, demonstrating superior accuracy to standard clinical scores. Broader clinical application is limited by the lack of external validation and high heterogeneity among the studies. Prospective validation in diverse, multicenter cohorts is essential before AI can be integrated into routine clinical practice.

Purpose: Oncological ¹⁸F-FDG PET/CT acquisition and reconstruction protocols need to be optimized for both quantitative and detection tasks. To date, most studies have focused on either quantification or noise, leading to quantitative harmonization guidelines or appropriate noise levels. We developed and evaluated protocols that provide harmonized quantitation with optimal amount of noise as a function of acquisition parameters and body mass. Methods: Multiple image acquisitions (N = 17) of the IEC/NEMA PET image quality phantom were performed with variable counting statistics. Phantom images were reconstructed with OSEM3D and PSF reconstructions for harmonized CRCmax quantification. The lowest counting statistics that resulted in compliance with EANM recommendations for CRCmax and CRCmax variability were used as optimization metrics. Image noise in the liver of 48 typical oncological ¹⁸F-FDG PET/CT studies was analysed with OSEM3D and PSF harmonized reconstructions. 164 additional ¹⁸F-FDG PET/CT reconstructed list mode images were also evaluated to derive analytical expressions that predict image quality and noise variability. Phantom to subject translational analysis was used to derive optimized acquisition and reconstruction protocols. Results: For harmonized quantitation levels, PSF reconstructions yielded decreased noise and lower CRCmax variability compared with regular OSEM3D reconstructions, suggesting they could enable a decreased activity regimen for matched performance. Conclusion: A PSF reconstruction with 7mm post-filter can provide harmonized quantification performance and acceptable image noise levels with injected activity, duration, and mass settings of 260 MBq.s/kg acquisition parameter at scan time. Similarly, the OSEM3D with 5mm post filter can provide similar performance with 401 MBq.s/kg.

Purpose To evaluate the classification performance of structured report features, radiomics, and machine learning (ML) models to differentiate between Coronavirus Disease 2019 (COVID-19) and other types of pneumonia using chest computed tomography (CT) scans. Methods Sixty-four COVID-19 subjects and 64 subjects with non-COVID-19 pneumonia were selected. The data was split into two independent cohorts: one for the structured report, radiomic feature selection and model building (n = 73), and another for model validation (n = 55). Physicians performed readings with and without machine learning support. The model's sensitivity and specificity were calculated, and inter-rater reliability was assessed using Cohen’s Kappa agreement coefficient. Results Physicians performed with mean sensitivity and specificity of 83.4 and 64.3%, respectively. When assisted with machine learning, the mean sensitivity and specificity increased to 87.1 and 91.1%, respectively. In addition, machine learning improved the inter-rater reliability from moderate to substantial. Conclusion Integrating structured reports and radiomics promises assisted classification of COVID-19 in CT chest scans.

We aimed to explore the variability of PET radiomic features for varying reconstruction methods and quantification settings. The IQ-NEMA phantom was scanned 5 times with a sphere to background F-18 concentration ratio of 10:1. The activity and the image duration were matched to result typical counting statistics for 18F-FDG oncologic examinations. The images were reconstructed with OSEM and PSF reconstructions, then 99 radiomic features were extracted using two discretization methods: fixed bin number (FBN = 16, 32 and 64 gray levels) and fixed bin width (FBW=0.25). This scheme resulted in a total of 1,188 features, classified as having low (<5.0%), intermediate (5-29.9%) or high (≥30%) variability. In general, FBW discretization yielded more stable features. A total of 499, 558 and 131 features had low, intermediate and high variability, respectively. First order features such as energy and entropy and textural features such as entropy (GLCM), long run emphasis and short run emphasis (GLRLM) were more likely to present low variability, regardless the reconstruction and discretization method. Other textural features such as large area emphasis (GLSZM), zone percentage (GLSZM) and complexity (NGTDM) had more frequently intermediate or high variability.These findings could facilitate features’ selection for further PET radiomic applications.