OmniMRI: A Unified Vision--Language Foundation Model for Generalist MRI Interpretation

• URL: http://arxiv.org/abs/2508.17524v1
• Date: Sun, 24 Aug 2025 21:11:28 GMT
• Title: OmniMRI: A Unified Vision--Language Foundation Model for Generalist MRI Interpretation
• Authors: Xingxin He, Aurora Rofena, Ruimin Feng, Haozhe Liao, Zhaoye Zhou, Albert Jang, Fang Liu,
• Abstract summary: We introduce OmniMRI, a unified vision-language foundation model designed to generalize across the entire MRI workflow.<n> OmniMRI is trained on a large-scale, heterogeneous corpus curated from 60 public datasets.<n>Results demonstrate OmniMRI's ability to perform diverse tasks within a single architecture.
• Score: 5.3427577036717
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Magnetic Resonance Imaging (MRI) is indispensable in clinical practice but remains constrained by fragmented, multi-stage workflows encompassing acquisition, reconstruction, segmentation, detection, diagnosis, and reporting. While deep learning has achieved progress in individual tasks, existing approaches are often anatomy- or application-specific and lack generalizability across diverse clinical settings. Moreover, current pipelines rarely integrate imaging data with complementary language information that radiologists rely on in routine practice. Here, we introduce OmniMRI, a unified vision-language foundation model designed to generalize across the entire MRI workflow. OmniMRI is trained on a large-scale, heterogeneous corpus curated from 60 public datasets, over 220,000 MRI volumes and 19 million MRI slices, incorporating image-only data, paired vision-text data, and instruction-response data. Its multi-stage training paradigm, comprising self-supervised vision pretraining, vision-language alignment, multimodal pretraining, and multi-task instruction tuning, progressively equips the model with transferable visual representations, cross-modal reasoning, and robust instruction-following capabilities. Qualitative results demonstrate OmniMRI's ability to perform diverse tasks within a single architecture, including MRI reconstruction, anatomical and pathological segmentation, abnormality detection, diagnostic suggestion, and radiology report generation. These findings highlight OmniMRI's potential to consolidate fragmented pipelines into a scalable, generalist framework, paving the way toward foundation models that unify imaging and clinical language for comprehensive, end-to-end MRI interpretation.

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