Cardiac MRI scanner system generates instant detailed heart reports
ORION-CMR: On-scanner Reporting with Integrated Foundation Model for End-to-End Cardiac MRI Analysis and Interpretation
Computer Vision and Pattern RecognitionMachine Learning
Summary
Cardiac MRI scans give detailed information about the heart but are hard to interpret and take a long time. The authors created ORION-CMR, a system that quickly and automatically analyzes heart MRI images and produces detailed reports directly on the scanner. It can identify different heart diseases and assess heart function with accuracy close to that of expert doctors. This system significantly speeds up the process, making advanced heart imaging more accessible in clinical settings.
What this means in practice
- •For clinical radiology teams: Automatically analyze cardiac MRI scans and generate interpretation reports on the scanner to speed up diagnosis and reduce manual workload.
- •For medical imaging device developers: Integrate end-to-end AI models that perform sequence classification, disease detection, and report generation into MRI scanners for real-time cardiac assessment.$Commercial implications: Enables development of enhanced cardiac MRI scanners with built-in AI diagnostics for hospitals and imaging centers.
Authors
Omer Burak Demirel, Kelly K. Horst, Alessio Perazzolo, Elisa Bruno, Kenan Kaya, Rongzhen Ouyang, Enas Ahmed, Jouke Smink, Spencer L. Waddle, Zainudeen Kallumpurath, Tzu Cheng Chao, Dinghui Wang, Steve G. Langer, Timothy L. Kline, Panagiotis Korfiatis, Jacinta Browne, Ivana Isgum, Tim Leiner
Abstract
Cardiovascular magnetic resonance (CMR) provides comprehensive cardiac assessment but remains underutilized because of the complexity of acquisition, post-processing, and interpretation. Existing artificial intelligence (AI) methods address isolated tasks, limiting clinical integration. We present ORION-CMR (On-scanner Reporting with Integrated fOunda-tioN Model), the first clinically evaluated scanner-native end-to-end CMR foundation model. Pretrained on 12,896,733 CMR images from 9,258 studies, ORION-CMR performs sequence classification, ventricular function assessment, late gadolinium enhancement (LGE) detection, binary and multiclass disease classification, and local large language model-based report generation in approximately 90 seconds. The framework. was evaluated on public benchmarks and clinically validated in a multi-vendor cohort of 68 subjects with normal examinations, congenital heart disease, dilated cardiomyopathy, and myocardial infarction. ORION-CMR outperformed supervised baselines and the previously published CMR foundation model (CMR-FM), achieving state-of-the-art performance for LGE classification and scar segmentation. Clinical evaluation achieved an AUC of 0.96 for normal-versus abnormal classification and 0.88 for multiclass disease classification, while generated reports demonstrated 81.4% agreement with expert interpretation. These results demonstrate the feasibility of real-time scanner-native AI-assisted CMR analysis and automated report generation.