ORION-CMR: On-scanner Reporting with Integrated Foundation Model for End-to-End Cardiac MRI Analysis and Interpretation

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

arXiv · 2609.23950

ORION-CMR: On-scanner Reporting with Integrated Foundation Model for End-to-End Cardiac MRI Analysis and Interpretation

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

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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.

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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. 2026-09-20. ORION-CMR: On-scanner Reporting with Integrated Foundation Model for End-to-End Cardiac MRI Analysis and Interpretation. https://arxiv.org/abs/2609.23950

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