Background/Objectives: Cardiac magnetic resonance (CMR) imaging is a key tool for diagnosing cardiovascular disease, but its analysis remains time-consuming and dependent on expert interpretation, which can limit throughput and reproducibility. To address these challenges, we aim to develop an automated solution that streamlines CMR post-processing, enabling consistent, rapid, and quantitative assessment of cardiac structures and myocardial pathology. Methods: We introduce SPOT-Cardio, an AI-powered imaging analysis toolbox based on a 2D breath-held late gadolinium enhancement (LGE) imaging technology: SPOT. This acquisition combines BR- and BL-LGE images in a single scan, allowing simultaneous capture of high-contrast scar information and detailed cardiac anatomy. Using the resulting CMR images, deep learning models (based on 2D U-Net or MedFormer) were trained to segment cardiac structures and myocardial scars. The trained models and associated image-processing algorithms were then integrated into the open-source medInria platform and specifically within its cardiac-focused MUSICardio application. Results: SPOT-Cardio enables automatic segmentation of cardiac structures and myocardial scars, performs landmark-based regional localization, and extracts key biomarkers such as scar volume, extent, and transmurality. The resulting quantitative measures are presented in standardized bullseye plots accompanied by detailed clinical reports. Conclusions: With a one-click workflow and intuitive visualization, SPOT-Cardio reduces manual workload and supports more accessible and consistent cardiovascular assessment. By integrating advanced image acquisition with AI-based automation, it provides a practical and efficient solution for streamlined and quantitative CMR analysis.
Naide et al. (2025) studied this question.