Our objective is to develop a general, nonlinear gradient system model that can accurately predict gradient distortions using convolutional networks. A set of training gradient waveforms were measured on a small animal imaging system and used to train a temporal convolutional network to predict the gradient waveforms produced by the imaging system. The trained network was able to accurately predict nonlinear distortions produced by the gradient system. Network prediction of gradient waveforms was incorporated into the image reconstruction pipeline and provided improvements in image quality and diffusion parameter mapping compared to both the nominal gradient waveform and the gradient impulse response function. Temporal convolutional networks can more accurately model gradient system behavior than existing linear methods and may be used to retrospectively correct gradient errors.

The integration of artificial intelligence (AI) into radiology has significantly improved diagnostic accuracy and workflow efficiency. Multimodal large language models (MLLMs), which combine natural language processing (NLP) and computer vision techniques, hold the potential to further revolutionize medical image analysis. Despite these advances, their widespread clinical adoption of MLLMs remains limited by challenges such as data quality, interpretability, ethical and regulatory compliance- including adherence to frameworks like the General Data Protection Regulation (GDPR) - computational demands, and generalizability across diverse patient populations. Addressing these interconnected challenges presents opportunities to enhance MLLM performance and reliability. Priorities for future research include improving model transparency, safeguarding data privacy through federated learning, optimizing multimodal fusion strategies, and establishing standardized evaluation frameworks. By overcoming these barriers, MLLMs can become essential tools in radiology, supporting clinical decision-making, and improving patient outcomes.

Understanding the complex neural activity dynamics is crucial for the development of the field of neuroscience. Although current functional MRI classification approaches tend to be based on static functional connectivity or cannot capture spatio-temporal relationships comprehensively, we present a new framework that leverages dynamic graph creation and spatiotemporal attention mechanisms for Autism Spectrum Disorder(ASD) diagnosis. The approach used in this research dynamically infers functional brain connectivity in each time interval using transformer-based attention mechanisms, enabling the model to selectively focus on crucial brain regions and time segments. By constructing time-varying graphs that are then processed with Graph Convolutional Networks (GCNs) and transformers, our method successfully captures both localized interactions and global temporal dependencies. Evaluated on the subset of ABIDE dataset, our model achieves 63.2 accuracy and 60.0 AUC, outperforming static graph-based approaches (e.g., GCN:51.8). This validates the efficacy of joint modeling of dynamic connectivity and spatio-temporal context for fMRI classification. The core novelty arises from (1) attention-driven dynamic graph creation that learns temporal brain region interactions and (2) hierarchical spatio-temporal feature fusion through GCNtransformer fusion.

Diffusion models have recently gained immense interest for their generative capabilities, specifically the high quality and diversity of the synthesized data. However, examples of their applications in 3D medical imaging are still scarce, especially in cardiology. Generating diverse realistic cardiac anatomies is crucial for applications such as in silico trials, electromechanical computer simulations, or data augmentations for machine learning models. In this work, we investigate the application of Latent Diffusion Models (LDMs) for generating 3D meshes of human cardiac anatomies. To this end, we propose a novel LDM architecture -- MeshLDM. We apply the proposed model on a dataset of 3D meshes of left ventricular cardiac anatomies from patients with acute myocardial infarction and evaluate its performance in terms of both qualitative and quantitative clinical and 3D mesh reconstruction metrics. The proposed MeshLDM successfully captures characteristics of the cardiac shapes at end-diastolic (relaxation) and end-systolic (contraction) cardiac phases, generating meshes with a 2.4% difference in population mean compared to the gold standard.

To develop PerAIDE, an AI-driven system for automated analysis of pulmonary perfusion blood volume (PBV) using dual-energy computed tomography pulmonary angiography (DE-CTPA) in patients with chronic pulmonary thromboembolism (CPE). In this prospective observational study, 32 patients with chronic thromboembolic pulmonary disease (CTEPD) and 151 patients with chronic thromboembolic pulmonary hypertension (CTEPH) were enrolled between January 2022 and July 2024. PerAIDE was developed to automatically quantify three distinct perfusion patterns-normal, reduced, and defective-on DE-CTPA images. Two radiologists independently assessed PBV scores. Follow-up imaging was conducted 3 months after balloon pulmonary angioplasty (BPA). PerAIDE demonstrated high agreement with the radiologists (intraclass correlation coefficient = 0.778) and reduced analysis time significantly (31 ± 3 s vs. 15 ± 4 min, p < 0.001). CTEPH patients had greater perfusion defects than CTEPD (0.35 vs. 0.29, p < 0.001), while reduced perfusion was more prevalent in CTEPD (0.36 vs. 0.30, p < 0.001). Perfusion defects correlated positively with pulmonary vascular resistance (ρ = 0.534) and mean pulmonary artery pressure (ρ = 0.482), and negatively with oxygenation index (ρ = -0.441). PerAIDE effectively differentiated CTEPH from CTEPD (AUC = 0.809, 95% CI: 0.745-0.863). At the 3-month post-BPA, a significant reduction in perfusion defects was observed (0.36 vs. 0.33, p < 0.01). CTEPD and CTEPH exhibit distinct perfusion phenotypes on DE-CTPA. PerAIDE reliably quantifies perfusion abnormalities and correlates strongly with clinical and hemodynamic markers of CPE severity. ClinicalTrials.gov, NCT06526468. Registered 28 August 2024- Retrospectively registered, https://clinicaltrials.gov/study/NCT06526468?cond=NCT06526468&rank=1 . PerAIDE is a dual-energy computed tomography pulmonary angiography (DE-CTPA) AI-driven system that rapidly and accurately assesses perfusion blood volume in patients with chronic pulmonary thromboembolism, effectively distinguishing between CTEPD and CTEPH phenotypes and correlating with disease severity and therapeutic response. Right heart catheterization for definitive diagnosis of chronic pulmonary thromboembolism (CPE) is invasive. PerAIDE-based perfusion defects correlated with disease severity to aid CPE-treatment assessment. CTEPH demonstrates severe perfusion defects, while CTEPD displays predominantly reduced perfusion. PerAIDE employs a U-Net-based adaptive threshold method, which achieves alignment with and faster processing relative to manual evaluation.

Detecting clinically significant prostate cancer (csPCa) remains a top priority in delivering high-quality care, yet consensus on an optimal diagnostic pathway is constantly evolving. In this study, we present an innovative diagnostic approach, leveraging a machine learning model tailored to the emerging role of prostate micro-ultrasound (micro-US) in the setting of csPCa diagnosis. We queried our prospective database for patients who underwent Micro-US for a clinical suspicious of prostate cancer. CsPCa was defined as any Gleason group grade > 1. Primary outcome was the development of a diagnostic pathway which implements clinical and radiological findings using machine learning algorithm. The dataset was divided into training (70%) and testing subsets. Boruta algorithms was used for variable selection, then based on the importance coefficients multivariable logistic regression model (MLR) was fitted to predict csPCA. Classification and Regression Tree (CART) model was fitted to create the decision tree. Accuracy of the model was tested using receiver characteristic curve (ROC) analysis using estimated area under the curve (AUC). Overall, 1422 patients were analysed. Multivariable LR revealed PRI-MUS score ≥ 3 (OR 4.37, p < 0.001), PI-RADS score ≥ 3 (OR 2.01, p < 0.001), PSA density ≥ 0.15 (OR 2.44, p < 0.001), DRE (OR 1.93, p < 0.001), anterior lesions (OR 1.49, p = 0.004), prostate cancer familiarity (OR 1.54, p = 0.005) and increasing age (OR 1.031, p < 0.001) as the best predictors for csPCa, demonstrating an AUC in the validation cohort of 83%, 78% sensitivity, 72.1% specificity and 81% negative predictive value. CART analysis revealed elevated PRIMUS score as the main node to stratify our cohort. By integrating clinical features, serum biomarkers, and imaging findings, we have developed a point of care model that accurately predicts the presence of csPCa. Our findings support a paradigm shift towards adopting MicroUS as a first level diagnostic tool for csPCa detection, potentially optimizing clinical decision making. This approach could improve the identification of patients at higher risk for csPca and guide the selection of the most appropriate diagnostic exams. External validation is essential to confirm these results.

Artificial intelligence (AI) achieves high-quality annotations of radiological images, yet often lacks the robustness required in clinical practice. Interactive annotation starts with an AI-generated delineation, allowing radiologists to refine it with feedback, potentially improving precision and reliability. These techniques have been explored in two-dimensional desktop environments, but are not validated by radiologists or integrated with immersive visualization technologies. We used a Virtual Reality (VR) system to determine whether (1) the annotation quality improves when radiologists can edit the AI annotation and (2) whether the extra work done by editing is worthwhile. We evaluated the clinical feasibility of an interactive VR approach to annotate mandibular and mental foramina on segmented 3D mandibular models. Three experienced dentomaxillofacial radiologists reviewed AI-generated annotations and, when needed, refined them at the voxel level in 3D space through click-based interactions until clinical standards were met. Our results indicate that integrating expert feedback within an immersive VR environment enhances annotation accuracy, improves clinical usability, and offers valuable insights for developing medical image analysis systems incorporating radiologist input. This study is the first to compare the quality of original and interactive AI annotation and to use radiologists' opinions as the measure. More research is needed for generalization.

Diffusion MRI (dMRI) is a valuable tool to map brain microstructure and connectivity by analyzing water molecule diffusion in tissue. However, acquiring dMRI data requires to capture multiple 3D brain volumes in a short time, often leading to trade-offs in image quality. One challenging artifact is susceptibility-induced distortion, which introduces significant geometric and intensity deformations. Traditional correction methods, such as topup, rely on having access to blip-up and blip-down image pairs, limiting their applicability to retrospective data acquired with a single phase encoding direction. In this work, we propose a deep learning-based approach to correct susceptibility distortions using only a single acquisition (either blip-up or blip-down), eliminating the need for paired acquisitions. Experimental results show that our method achieves performance comparable to topup, demonstrating its potential as an efficient and practical alternative for susceptibility distortion correction in dMRI.

BackgroundIron overload promotes atherosclerosis in mice and causes vascular dysfunction in humans with Hemochromatosis. However, data are controversial on whether systemic iron availability within physiological limits affects the pathogenesis of atherosclerosis. We, therefore, performed an individual participant data (IPD) meta-analysis and studied the association between serum iron biomarkers with common carotid intima-media thickness (CC-IMT); in addition, since sex influences iron metabolism and vascular aging, we studied if there are sex-specific differences. MethodsWe pooled the IPD and analysed the data on adults (age[&ge;]18y) by orthogonal approaches: machine learning (ML) and a single-stage meta-analysis. For ML, we tuned a gradient-boosted tree regression model (XGBoost) and subsequently, we interpreted the features using variable importance. For the single-stage metaanalysis, we examined the association between iron biomarkers and CC-IMT using spline-based linear mixed models, accounting for sex interactions and study-specific effects. To confirm robustness, we repeated analyses on imputed data using multivariable regression adjusted for key covariates identified through machine learning. Further, subgroup analyses were performed in children and adolescents (age<18y). In addition, to evaluate causality, we used UK Biobank data to examine associations between the hemochromatosis (HFE) genotypes (C282Y/H63D) and mean CC-IMT in [~]42,500 participants with carotid ultrasound data, using sex-stratified linear regression (adjusted for age, assessment centre, and genetic principal components). ResultsWe included IPD from 21 studies (N=10,807). The application of the ML model showed moderate predictive performance and identified iron biomarkers (transferrin, ferritin, transferrin saturation, and iron) as key features for IMT prediction. Multivariable analyses showed non-linear sex-specific relationships for ferritin and transferrin with CC-IMT: ferritin showed a significant positive association, and transferrin showed negative associations at specific ranges, both only among females. No significant associations were found between CC-IMT in those with HFE genotypes in either sex in the UK Biobank. ConclusionOur observational data show that iron biomarkers - ferritin and transferrin are non-linearly associated with CC-IMT specifically in females, while a significant causal association between the HFE genotype and CC-IMT could not be demonstrated in the UK Biobank data. We conclude that the observational associations may not only be explained by causal effects of iron on the arterial wall thickness, but also in part be driven by residual confounding factors such as inflammation. Other: No financial support was received for this meta-analysis. The protocol for this study is registered in the PROSPERO database (CRD42020155429; https://www.crd.york.ac.uk/).

Chest X-ray imaging is crucial for diagnosing pulmonary and cardiac diseases, yet its interpretation demands extensive clinical experience and suffers from inter-observer variability. While deep learning models offer high diagnostic accuracy, their black-box nature hinders clinical adoption in high-stakes medical settings. To address this, we propose X-Ray-CoT (Chest X-Ray Chain-of-Thought), a novel framework leveraging Vision-Language Large Models (LVLMs) for intelligent chest X-ray diagnosis and interpretable report generation. X-Ray-CoT simulates human radiologists' "chain-of-thought" by first extracting multi-modal features and visual concepts, then employing an LLM-based component with a structured Chain-of-Thought prompting strategy to reason and produce detailed natural language diagnostic reports. Evaluated on the CORDA dataset, X-Ray-CoT achieves competitive quantitative performance, with a Balanced Accuracy of 80.52% and F1 score of 78.65% for disease diagnosis, slightly surpassing existing black-box models. Crucially, it uniquely generates high-quality, explainable reports, as validated by preliminary human evaluations. Our ablation studies confirm the integral role of each proposed component, highlighting the necessity of multi-modal fusion and CoT reasoning for robust and transparent medical AI. This work represents a significant step towards trustworthy and clinically actionable AI systems in medical imaging.