Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026 · Cape Town, South Africa
461-04-008 ISMRM Abstract

Breast Diffusion-Weighted Multi-Shot EPI with Model-Based Deep Learning Reconstruction

Primary: Body - Breast
Secondary: Diffusion - Diffusion Reconstruction
461-04-008 · Quantitative MRI Across the Body · Tuesday, 12 May, 2:35 PM–3:30 PM · Digital Posters Row B
Keywords: Breast cancer AI-Accelerated MRI Multi-shot EPI DWI Breast dwi Deep learning-based reconstruction
Accepted
Mart WJ van Straten 1,2, Chinmay Rao3, Martijn Nagtegaal1, Marcel Breeuwer2,4, Matthias van Osch1, Peter Börnert1,5, Yiming Dong1
1C.J. Gorter MRI Center, Department of Radiology, Leiden University Medical Center, Leiden, Netherlands
2Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, Netherlands
3Division of Image Processing, Department of Radiology, Leiden University Medical Center, Leiden, Netherlands
4Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, Netherlands
5Philips Innovative Technologies, Hamburg, Germany
Presenting Author: Mart WJ van Straten

Synopsis

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References

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2. R.M. Mann, N. Cho, and L. Moy. “Breast MRI: State of the Art”. In: Radiology 292.3 (2019), pp. 520–536. doi:10.1148/radiol.2019182947 [doi]
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4. W. Wu and K.L. Miller. “Image formation in diffusion MRI: A review of recent technical developments”. In: Journal of Magnetic Resonance Imaging 46.3 (2017), pp. 646–662. doi:10.1002/jmri.25664 [doi]
5. H.K. Jeong, J.C. Gore, and A.W. Anderson. “High-resolution human diffusion tensor imaging using 2-D navigated multishot SENSE EPI at 7 T”. In: Magnetic Resonance in Medicine 69.3 (2013), pp. 793–802. doi:10.1002/mrm.24320 [doi]
6. H.K. Aggarwal, M.P. Mani, and M. Jacob. “MoDL: Model-Based Deep Learning Architecture for Inverse Problems”. In: IEEE Transactions on Medical Imaging 38.2 (2019), pp. 394–405. doi:10.1109/TMI.2018.2865356 [doi]
7. Y .Hu et al. “RUN-UP: Accelerated multishot diffusion-weighted MRI reconstruction using an unrolled network with U-Net as priors”. In: Magnetic Resonance in Medicine 85.2 (2021), pp. 709–720. doi:10.1002/mrm.28446 [doi]
8. H.K. Aggarwal, M.P. Mani, and M. Jacob. “MoDL-MUSSELS: Model-Based Deep Learning for Multishot Sensitivity-Encoded Diffusion MRI”. In: IEEE Transactions on Medical Imaging 39.4 (2020), pp. 1268–1277. doi:10.1109/TMI.2019.2946501 [doi]
9. M.L. Terpstra et al. “A symmetric loss function for magnetic resonance imaging reconstruction and image registration with deep learning”. In: Medical Image Analysis 80 (2022), p. 102509. doi:10.1016/j.media.2022.102509 [doi]
10. Y. Hu et al. “Motion-robust reconstruction of multishot diffusion-weighted images without phase estimation through locally low-rank regularization”. In: Magnetic Resonance in Medicine 81.2 (2019), pp. 1181–1190. doi:10.1002/mrm.27488 [doi]

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http://echo.ismrm.org/p/ISMRM2026/461-04-008