Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026 · Cape Town, South Africa
663-02-004 ISMRM Abstract

A Zero-Shot SSL Framework for Joint Image Reconstruction and Biomarker Estimation in 2D/3D Under-Sampled MUSE-DTI

Primary: Acquisition & Reconstruction - AI methods
Secondary: Diffusion - DWI/DTI/DKI
663-02-004 · Diffusion: DWI/DTI/DKI · Thursday, 14 May, 9:25 AM–10:20 AM · Digital Posters Row D
Keywords: Deep learning reconstruction Multi-shot DWI MUSE Self-supervised learning Diffusion Tensor Imaging (DTI)
Accepted
Chenglang Yuan1, Shihui Chen1,2, Liyuan Liang1,2, Xiaorui Xu3, HAILIN XIONG1, Yi Li 1, Tianbaige Liu1, QITING WU1, Wing Yat Cheung1, Sai Kam Hui4,5, Qi DOU6, Hing-Chiu Chang1,2
1Department of Biomedical Engineering, The Chinese University of Hong Kong, Hong Kong, China
2Multi-Scale Medical Robotics Center, Hong Kong, China
3Department of Diagnostic Radiology, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China
4CU Lab of AI in Radiology (CLAIR), Department of Imaging and Interventional Radiology, The Chinese University of Hong Kong, Hong Kong, China
5Department of Psychiatry, The Chinese University of Hong Kong, Hong Kong, China
6Department of Computer Science and Engineering, The Chinese University of Hong Kong, Hong Kong, China
Presenting Author: Yi Li

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References

1. Chen, Nan-kuei, et al. "A robust multi-shot scan strategy for high-resolution diffusion weighted MRI enabled by multiplexed sensitivity-encoding (MUSE)." Neuroimage 72 (2013): 41-47. https://doi.org/10.1016/j.neuroimage.2013.01.038 [doi]
2. Chang, Hing-Chiu, et al. "Human brain diffusion tensor imaging at submillimeter isotropic resolution on a 3 Tesla clinical MRI scanner." Neuroimage 118 (2015): 667-675. https://doi.org/10.1016/j.neuroimage.2015.06.016 [doi]
3. Zbontar, Jure, et al. "fastMRI: An open dataset and benchmarks for accelerated MRI." arXiv preprint arXiv:1811.08839 (2018). https://doi.org/10.48550/arXiv.1811.08839 [doi]
4. Sriram, Anuroop, et al. "End-to-end variational networks for accelerated MRI reconstruction." International conference on medical image computing and computer-assisted intervention. Cham: Springer International Publishing, 2020. https://doi.org/10.1007/978-3-030-59713-9_7 [doi]
5. Yuan, Chenglang, et al. "Deep learning-based joint image reconstruction and biomarker estimation for highly-accelerated multi-shot intravoxel incoherent motion DWI." ISMRM & ISMRT Annual Meeting & Exhibition (2025).
6. Yaman, Burhaneddin, et al. "Self‐supervised learning of physics‐guided reconstruction neural networks without fully sampled reference data." Magnetic resonance in medicine 84.6 (2020): 3172-3191.  https://doi.org/10.1002/mrm.28378 [doi]
7. Yaman, Burhaneddin. "Zero-Shot Self-Supervised Learning for MRI Reconstruction." International Conference on Learning Representations. 2022. https://doi.org/10.48550/arXiv.2102.07737 [doi]
8. Li, Zihan, et al. "DIMOND: DIffusion model OptimizatioN with deep learning." Advanced Science 11.24 (2024): 2307965. https://doi.org/10.1002/advs.202307965 [doi]
9. Ronneberger, Olaf, Philipp Fischer, and Thomas Brox. "U-net: Convolutional networks for biomedical image segmentation." International Conference on Medical image computing and computer-assisted intervention. Cham: Springer international publishing, 2015. https://doi.org/10.1007/978-3-319-24574-4_28 [doi]
10. Garyfallidis, Eleftherios, et al. "Dipy, a library for the analysis of diffusion MRI data." Frontiers in neuroinformatics 8 (2014): 8. https://doi.org/10.3389/fninf.2014.00008 [doi]

Cite this abstract

http://echo.ismrm.org/p/ISMRM2026/663-02-004