Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026 · Cape Town, South Africa
662-04-015 ISMRM Abstract

Self-Supervised Deep Learning Model for Estimating Microstructural Parameters in Breast Cancer

Primary: Diffusion - Microstructure
Secondary: Analysis Methods - Image Synthesis and Translation
662-04-015 · Artificial Intelligence-Based Image Reconstruction in the Body · Thursday, 14 May, 2:35 PM–3:30 PM · Digital Posters Row C
Keywords: Diffusion MRI Breast cancer Deep learning IMPULSED Neural Network
Accepted
Juanhua Zhang1, Lei Wu2, Yuan Lian1, Fan Liu1, Peijiang Ma1, Kaihan Yang1, Haihua Bao2, Diwei Shi3,4, Hua Guo 1
1Tsinghua University, Beijing, China
2Qinghai University Affiliated Hospital, Xining, China
3Research Institute of Tsinghua University in Shenzhen, China
4Tsinghua Shenzhen International Graduate School, Tsinghua University, Beijing, China
Presenting Author: Hua Guo

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References

1. Xu J, Jiang X, Li H, et al. Magnetic resonance imaging of mean cell size in human breast tumors. Magnetic Resonance in Medicine. 2020;83(6):2002-2014. doi:10.1002/mrm.28056 [doi]
2. Jiang X, Li H, Xie J, Zhao P, Gore JC, Xu J. Quantification of cell size using temporal diffusion spectroscopy. Magnetic Resonance in Medicine. 2016;75(3):1076-1085. doi:10.1002/mrm.25684 [doi]
3. MR cell size imaging with temporal diffusion spectroscopy. Magnetic Resonance Imaging. 2021;77:109-123. doi:10.1016/j.mri.2020.12.010 [doi]
4. Probing neural tissues at small scales: Recent progress of oscillating gradient spin echo (OGSE) neuroimaging in humans. Journal of Neuroscience Methods. 2021;349:109024. doi:10.1016/j.jneumeth.2020.109024 [doi]
5. Liu F, Wu L, Luo X, et al. Evaluating the Diagnostic Performance of MR Cytometry Imaging in Differentiating Benign and Malignant Breast Tumors. Journal of Magnetic Resonance Imaging. 2025;62(2):521-533. doi:10.1002/jmri.29757 [doi]
6. Wu D, Jiang K, Li H, et al. Time-Dependent Diffusion MRI for Quantitative Microstructural Mapping of Prostate Cancer. Radiology. 2022;303(3):578-587. doi:10.1148/radiol.211180 [doi]
7. Jiang X, Xu J, Gore JC. Mapping hepatocyte size in vivo using temporal diffusion spectroscopy MRI. Magnetic Resonance in Medicine. 2020;84(5):2671-2683. doi:10.1002/mrm.28299 [doi]
8. Golkov V, Dosovitskiy A, Sperl JI, et al. q-Space Deep Learning: Twelve-Fold Shorter and Model-Free Diffusion MRI Scans. IEEE Transactions on Medical Imaging. 2016;35(5):1344-1351. doi:10.1109/TMI.2016.2551324 [doi]
9. Li Z, Li Z, Bilgic B, et al. DIMOND: DIffusion Model OptimizatioN with Deep Learning. Advanced Science. 2024;11(24):2307965. doi:10.1002/advs.202307965 [doi]
10. Ronneberger O, Fischer P, Brox T. U-Net: Convolutional Networks for Biomedical Image Segmentation. In: Navab N, Hornegger J, Wells WM, Frangi AF, eds. Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015. Cham: Springer International Publishing; 2015:234-241. doi:10.1007/978-3-319-24574-4_28 [doi]
11. Van AT, Holdsworth SJ, Bammer R. In vivo investigation of restricted diffusion in the human brain with optimized oscillating diffusion gradient encoding. Magnetic Resonance in Medicine. 2014;71(1):83-94. doi:10.1002/mrm.24632 [doi]
12. Wang Z, Bovik AC, Sheikh HR, Simoncelli EP. Image quality assessment: from error visibility to structural similarity. IEEE Transactions on Image Processing. 2004;13(4):600-612. doi:10.1109/TIP.2003.819861 [doi]

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http://echo.ismrm.org/p/ISMRM2026/662-04-015