Abstract
Cardiovascular magnetic resonance (CMR) provides comprehensive cardiac assessment but remains under utilized 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.
Links to Paper and Supplementary Materials
Main Paper (Open Access Version): https://papers.miccai.org/miccai-2026-sat/paper/STACOM2026_018.pdf
SharedIt Link: Not yet available
SpringerLink (DOI): Not yet available
Supplementary Material: Not Submitted
Link to Open Review
Open Review Page: https://openreview.net/profile?id=~Tim_Leiner3
BibTex
@InProceedings{DemOme_ORIONCMR_MICCAISAT2026,
author = { Demirel, Omer Burak AND Horst, Kelly K. AND Perazzolo, Alessio AND Bruno, Elisa AND Kaya, Kenan AND Ouyang, Rongzhen AND Ahmed, Enas AND Smink, Jouke AND Waddle, Spencer L. AND Chao, Tzu Cheng AND Wang, Dinghui AND Langer, Steve G. AND Kline, Timothy L. AND Korfiatis, Panagiotis AND Browne, Jacinta AND Isgum, Ivana AND Leiner, Tim},
title = { { ORION-CMR: On-scanner Reporting with Integrated Foundation Model for End-to-End Cardiac MRI Analysis and Interpretation } },
booktitle = {Medical Image Computing and Computer Assisted Intervention -- MICCAI 2026 Workshops and Challenges},
year = {2026},
publisher = {Springer Nature Switzerland},
volume = {LNCS 17267},
month = {pending},
page = {pending}
}