ISMRM25 - AI-Enhanced Imaging: Redefining Clarity & Precision

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

AI-Enhanced Imaging: Redefining Clarity & Precision

Oral

AI & Machine Learning

Wednesday, 14 May 2025

311

08:15 - 10:15

Moderators: Jongho Lee & Fang Liu

Session Number: O-16

No CME/CE Credit

08:15		0743. Self-gated self-supervised ADMM unrolling enables mesoscale high-resolution motion-robust diffusion-weighted imaging View Presentation Video Z. Tan, P. Liebig, A. Hofmann, M. Jaroszewicz, Y. Jiang, V. Gulani, F. Laun, F. Knoll University of Michigan, Ann Arbor, United States Impact: Our proposed ADMM unrolling enables whole brain DWI of 21 volumes at 0.7 mm isotropic resolution and 10 minutes scan, and shows higher signal-to-noise ratio (SNR), clearer tissue delineation, and improved motion robustness, which make it plausible for clinical translation.
08:27		0744. An Explainable AI-based Motion Detection approach for MR images without requirement of motion annotated ground truth data View Presentation Video S. Banerjee, D. Shanbhag, S. Chatterjee GE HealthCare, Bengaluru, India Impact: Reliable motion alert for MRI scans shall enable technologists to re-scan the subjects while in the scanning room. This will help in reducing the patient recalls due to motion artifact and hence reduce burden on the healthcare system.
08:39		0745. Unified Motion Correction Model for Multi-modal MRI View Presentation Video H. Xiong, F. Li, J. Cai, Q. Wang School of Biomedical Engineering & State Key Laboratory of Advanced Medical Materials and Devices, ShanghaiTech University, Shanghai, China, Shanghai, China Impact: This framework enhances multi-modal MRI by effectively correcting motion artifacts, leading to improved image quality and diagnostic confidence. It holds potential for widespread clinical adoption, benefiting patient care and advancing research involving diverse MRI.
08:51		0746. DeepEddy: high-quality fast eddy current and bulk motion correction using deep learning-based image synthesis and co-registration View Presentation Video J. Zhang, F. Lange, J. Andersson, J. Zheng, Y. Jing, H. Yang, M. Liu, Z. Li, W. Wu, Q. Tian, Z. Li University of Oxford, Oxford, United Kingdom Impact: DeepEddy enables eddy current and bulk motion correction for diffusion data with any number of diffusion directions, showing the promise to benefit clinical applications where scan time is extremely limited.
09:03		0747. Registration-Guided Cardiac Functional Assessment from Limited Annotations in a Single Breath-hold Cine View Presentation Video A. Ghoul, P. Cassal Paulson, K. Hammernik, P. Krumm, D. Rueckert, S. Gatidis, T. Küstner University Hospital of Tübingen, Tuebingen, Germany Impact: Our framework enables automated cardiac function assessment, even for highly accelerated single breath-hold scans. We improve CMR accessibility for studies with limited subjects and sparse manual annotations. Results indicate reliable motion estimation, ventricular function measures and myocardial strain analysis.
09:15		0748. Deep Learning Super-Resolution reconstruction for fast cardiac MRI protocol：A Comparative Study with Conventional cardiac MR View Presentation Video Y. Hua, H. Lu, X. Yue, F. Du, N. Zhang, H. Jin, M. Zeng Zhongshan hospital of Fudan University, Shanghai, China Impact: This study demonstrates that CSAI-CMR improves image quality and significantly reduces scan time, enhancing patient comfort and clinical efficiency, it supports advancing cardiac MRI toward more precise, efficient, and patient-friendly practices, potentially increasing its clinical adoption.
09:27		0749. An Ambient Denoising Score Matching Based Self-supervised Denoising Approach for Multicontrast Low-Field MRI View Presentation Video J. Tu, Y. Shi, F. Lam University of Illinois Urbana-Champaign, Champaign, United States Impact: Our method represents a new approach for self-supervised multicontrast MRI denoising. It may offer better trade-offs in SNR, resolution, and speed to benefit many low-field applications.
09:39		0750. NUCLIDE: A Novel Unsupervised Clustering-based Image Denoising and Enhancement for 4D Dynamic PET/MRI Data View Presentation Video H. Yousefi, M. Hamdi, R. Laforest, M. Brier, T. Benzinger, Y. Chen, H. An Washington University in St.Louis, Creve Coeur, United States Impact: Our method enables robust, unsupervised denoising for PET/MRI, preserving critical TACs and structural information. This method has applications in clinical settings and is adaptable to multimodal imaging.
09:51		0751. Denoising ASL Images Using Distribution Remapping-Based Deep Learning View Presentation Video Z. Xu, R. Guo, Z. Ke, Y. Li, Y. Zhao, W. Jin, Z. Meng, Y. Li, Z-P Liang University of Illinois, Urbana Champaign, Urbana, United States Impact: Our proposed method addresses the issue of limited target ASL training datasets for deep learning-based ASL denoising and demonstrates excellent denoising performance. It can be generalized for the practical utility in both research and clinical applications.
10:03		0752. Ultra-fast High-Resolution Multi-Contrast Qualitative and Quantitative MRI of the Entire Brain in 3 minutes View Presentation Video B. Alyuz, S. Qiu, H-L Lee, C. Gao, S. Madhusoodhanan, N. Sicotte, P. Sati, Y. Xie, D. Li Cedars-Sinai Medical Center, Los Angeles, United States Impact: The proposed approach enables high-resolution multi-contrast MRI and quantitative mapping of the entire brain in 3 minutes. It can improve and facilitate diagnosis and monitoring of neurological diseases like MS by making detailed brain imaging feasible in time-sensitive clinical settings.

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