ISMRM25 - Towards Foundation Models in MRI

ISMRM & ISMRT Annual Meeting & Exhibition • 10-15 May 2025 • Honolulu, Hawai'i

Towards Foundation Models in MRI

Oral

AI & Machine Learning

Tuesday, 13 May 2025

310 (Lili-u Theater)

15:45 - 17:45

Moderators: Anthony Gatti & Nancy Pham

Session Number: O-12

No CME/CE Credit

15:45		Introduction Anthony Gatti
15:57		0616. Disease-Specific Brain Function Representation Generation for Diagnosis Using Large Language Models PDF Poster View Presentation Video M. Liu, L. Zhang, Q. Wang Shanghai Jiao Tong University, Shanghai, China Impact: Our findings indicate that general and disease-specific brain function representations guided with LLM improve diagnostic accuracy. Additionally, the framework’s adaptability across different diseases positions it as a versatile tool in neuroimaging research, with potential applications in studying various disorders.
16:09		0617. Privacy Preserving Performance Analysis of the AI Model Deployed on the MRI Scanner with Multimodal Vision-Language Feedback View Presentation Video P. M. Goud, M. G. Reddy, C. Bhushan, D. Shanbhag GE HealthCare, Bengaluru, India Impact: We report a privacy preserving mechanism for monitoring segmentation model performance in terms of simple text logging, rather than quantitative numbers which might require re-interpretation to deduce the performance of the AI model.
16:21		0618. Comparison of Radiologists and Multimodal Large Language Models Responses to Radiology ImageQuest Q. Wu, Q. Wu, J. Xue, D. Shen, M. Wang Henan Provincial People's Hospital, Zhengzhou, China Impact: Multimodal LLMs show promise in radiology education and practice, while further research is needed to validate their impact on real clinical applications
16:33		0619. Supporting MRI Technicians: An LLM-Based Troubleshoot Companion for Operational Assistance View Presentation Video L. Pfaff, B. Geissler, U. Klenke, F. Wagner, R. Schneider, T. Wuerfl, A. Maier Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany Impact: This work enhances MRI troubleshooting by introducing a context-aware support tool based on LLMs, improving problem-solving efficiency for technicians. It highlights the potential of RAG systems in healthcare to replace traditional keyword-based search methods with more intelligent solutions.
16:45		0621. Enabling a one touch MR patient setup using RIS Interpretation and 3D Camera View Presentation Video D. Anand, S. Gannavarapu, s. Rajamani, M. Patil, S. KS, D. Shanbhag GE Healthcare, Bangalore, India Impact: The trained Coil detection and RIS interpretation model aids in interpreting the scan intent thereby enabling proper positioning of patient, coil and automatic landmarking, saving time and avoiding repeat scans.
16:57		0622. Segment-Any-Muscle: Towards an Open-Source, Contrast-Agnostic Computer-Vision Muscle Segmentation Model for MRI and CT View Presentation Video E. Wesselink, J. Elliott, M. McKay, E. Martino, N. Caplan, S. Mackey, J. Cohen-Adad, S. Bédard, B. Leener, E. Naga Karthik, C. Law, M. Fortin, C. Vleggeert – Lankamp, A. Ieva, B. Kim, M. Hancock, A. Pool - Goudzwaard, P. Pevenage, K. A. Weber II Stanford University, Palo Alto, United States Impact: This contrast-agnostic computer-vision model can automatically and accurately assess muscle health from both MRI and CT. We are expanding this to all muscles to support multiple clinical and research applications linking muscle health to overall health and disease.
17:09		0623. Enhancing organ segmentation performance in foundation models via ensemble learning View Presentation Video Q. Li, Y. Zhang, Y. Li, Y. Zhang, L. Sun, M. Sun, Q. Li, Z. Wang, M. Liu, X. Hu, S. Wang, C. Wang Fudan University, Shanghai, China Impact: This study integrates the ensemble learning technique for the first time to enhance the performance of foundation models, potentially reducing costs in time and resources. More importantly, it provides an effective approach for improving foundation model performance in future applications.
17:21		0624. TotalSpineSeg: Robust Spine Segmentation and Labeling Across Multiple MRI Contrasts PDF Poster View Presentation Video Y. Warszawer, N. Molinier, J. Valosek, E. Shirbint, P-l Benveniste, T. Granberg, R. Ouellette, C. Tsagkas, V. Callot, F. Mohamed, J. Bednarik, K. O'Grady, A. Achiron, J. Cohen-Adad Sheba Medical Center, Ramat Gan, Israel Impact: TotalSpineSeg could enhance clinical workflows by providing automatic vertebrae segmentation, improving the diagnosis of various spinal pathologies and supporting informed clinical decision-making. It is available on GitHub (https://github.com/neuropoly/totalspineseg) and in Spinal Cord Toolbox v.6.5¹⁴.
17:33		0620. WITHDRAWN

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