BACKGROUND: 18F-FDG PET/CT has become an important tool in diagnosis of prosthetic vascular graft infections (PVGI). The aim of the study was to identify the patterns of vascular graft infection in 18F-FDG PET/CT. MATERIAL AND METHODS: The study was performed in 24 patients with vascular graft infection, in 17 patients implanted in an open surgery mode and in 7 patients by endovascular aortic repair (EVAR). Vascular prostheses were evaluated by two visual scales and semi-quantitative analysis with maximum standardized uptake values (SUV max). RESULTS: In the 3-point scale: 23 patients were in grade 1 and one patient was in grade 2. In the 5-point scale: 19 patients were in grade 5 with the highest activity in the focal area, 4 patients were in grade 4 and one patient in grade 3. The visual evaluation of 18F-FDG PET/CT study revealed that peri-graft high metabolic activity was associated with occurrence of morphological abnormalities (n = 21) like gas bubbles and peri-graft fluid retention or without abnormal CT findings (n = 3). The presence of the gas bubbles was linked to higher uptake of 18F-FDG (p < 0.01, SUVmax 11.81 ± 4.35 vs 7.36 ± 2.80, 15 vs 9 pts). In EVAR procedure, the highest metabolic activity was greater than in classical prosthesis (SUVmax 21.5 vs 13). CONCLUSIONS: 18F-FDG PET/CT is a very useful tool for assessment of vascular graft infections. CT findings like gas bubbles, or peri-graft fluid retention were associated with significantly higher glucose metabolism; however, in some cases without anatomic alterations, increased metabolic activity was the only sign of infection. BACKGROUND: 18F-FDG PET/CT has become an important tool in diagnosis of prosthetic vascular graft infections (PVGI). The aim of the study was to identify the patterns of vascular graft infection in 18F-FDG PET/CT. MATERIAL AND METHODS: The study was performed in 24 patients with vascular graft infection, in 17 patients implanted in an open surgery mode and in 7 patients by endovascular aortic repair (EVAR). Vascular prostheses were evaluated by two visual scales and semi-quantitative analysis with maximum standardized uptake values (SUV max). RESULTS: In the 3-point scale: 23 patients were in grade 1 and one patient was in grade 2. In the 5-point scale: 19 patients were in grade 5 with the highest activity in the focal area, 4 patients were in grade 4 and one patient in grade 3. The visual evaluation of 18F-FDG PET/CT study revealed that peri-graft high metabolic activity was associated with occurrence of morphological abnormalities (n = 21) like gas bubbles and peri-graft fluid retention or without abnormal CT findings (n = 3). The presence of the gas bubbles was linked to higher uptake of 18F-FDG (p < 0.01, SUVmax 11.81 ± 4.35 vs 7.36 ± 2.80, 15 vs 9 pts). In EVAR procedure, the highest metabolic activity was greater than in classical prosthesis (SUVmax 21.5 vs 13). CONCLUSIONS: 18F-FDG PET/CT is a very useful tool for assessment of vascular graft infections. CT findings like gas bubbles, or peri-graft fluid retention were associated with significantly higher glucose metabolism; however, in some cases without anatomic alterations, increased metabolic activity was the only sign of infection.

Background/Objectives: This study evaluates whether combining ⁶⁸Ga-PSMA-11-PET/CT derived imaging biomarkers with clinical risk factors improves the prediction of early biochemical recurrence (eBCR) or clinical progress in patients with high-risk prostate cancer (PCa) after primary treatment, using machine learning (ML) models. Methods: We analyzed data from 93 high-risk PCa patients who underwent ⁶⁸Ga-PSMA-11 PET/CT and received primary treatment at a single center. Two predictive models were developed: a logistic regression (LR) model and an ML derived probabilistic graphical model (PGM) based on a naïve Bayes framework. Both models were compared against each other and against the CAPRA risk score. The models’ input variables were selected based on statistical analysis and domain expertise including a literature review and expert input. A decision tree was derived from the PGM to translate its probabilistic reasoning into a transparent classifier. Results: The five key input variables were as follows: binarized CAPRA score, maximal intraprostatic PSMA uptake intensity (SUVmax), presence of bone metastases, nodal involvement at common iliac bifurcation, and seminal vesicle infiltration. The PGM achieved superior predictive performance with a balanced accuracy of 0.73, sensitivity of 0.60, and specificity of 0.86, substantially outperforming both the LR (balanced accuracy: 0.50, sensitivity: 0.00, specificity: 1.00) and CAPRA (balanced accuracy: 0.59, sensitivity: 0.20, specificity: 0.99). The decision tree provided an explainable classifier with CAPRA as a primary branch node, followed by SUVmax and specific PET-detected tumor sites. Conclusions: Integrating ⁶⁸Ga-PSMA-11 imaging biomarkers with clinical parameters, such as CAPRA, significantly improves models to predict progression in patients with high-risk PCa undergoing primary treatment. The PGM offers superior balanced accuracy and enables risk stratification that may guide personalized treatment decisions.

The development of computed tomography (CT) image analysis methods, including deep learning methods, requires large and well-annotated datasets. In addition to being widely used in medicine, X-ray CT (XCT) is also important in industrial metrology, quality control, and radiation safety, where real scans are costly to acquire and expert-validated annotations are often required for supervised modelling. Despite this need, flexible tools for generating synthetic, fully annotated CT data for industrial and radiological applications remain limited. In particular, there is a need for solutions that enable simultaneous control over object geometry, material properties, noise levels, and CT-like artifacts, allowing their impact on segmentation, reconstruction, and volumetric measurement methods to be systematically investigated. This paper presents CanIonSynth - an open-source platform for generating synthetic CT data for aerosol cans and ionization chambers. The tool creates 3D volumes, segmentation masks and CT reconstructions. The framework allows users to control parameters of generated objects, such as geometry, material type, fill level, voxel resolution, noise, and image reconstruction settings, through a configuration file. Additionally, the tool supports commonly used CT imaging data formats, including NIfTI, DICOM and DICOM-SEG.