Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026 · Cape Town, South Africa
662-04-004 ISMRM Abstract

Self-Supervised Reconstruction and Denoising for High-Resolution Distortion-Free Prostate DWI

Primary: Acquisition & Reconstruction - Image Reconstruction: AI
Secondary: Body - Prostate
662-04-004 · Artificial Intelligence-Based Image Reconstruction in the Body · Thursday, 14 May, 2:35 PM–3:30 PM · Digital Posters Row C
Keywords: Diffusion Acquisition Prostate Deep learning reconstruction Body MRI Diffusion-weighted imaging (DWI)
Accepted
Jingjia Chen 1,2, Haoyang Pei1,2,3, Kun Zhou4, Qiuting Wen5, Mahesh B Keerthivasan6, Angela Tong1,2, Hersh Chandarana1,2, Li Feng1,2
1Center for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, New York University Grossman School of Medicine, New York, United States of America
2Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, United States of America
3Department of Electrical and Computer Engineering, New York University Tandon School of Engineering, New York, United States of America
4Magnetic resonance, Siemens Shenzhen Magnetic Resonance Ltd., Shenzhen, China
5Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, United States of America
6Siemens Medical Solutions, Boston, United States of America
Presenting Author: Jingjia Chen

Synopsis

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References

1. Key Statistics for Prostate Cancer | Prostate Cancer Facts. American Cancer Society. January 6, 2025. https://www.cancer.org/cancer/types/prostate-cancer/about/key-statistics
2. NCCN Clinical Practice Guide- Lines in Oncology: Prostate Cancer. National Comprehensive Cancer Network.; 2023. Accessed April 23, 2025. https://www.nccn.org/guidelines/guidelines-detail?category=1&id=1459
3. Fazekas T, Shim SR, Basile G, et al. Magnetic Resonance Imaging in Prostate Cancer Screening: A Systematic Review and Meta-Analysis. JAMA Oncology. 2024;10(6):745-754. doi:10.1001/jamaoncol.2024.0734 [doi]
4. Fennessy FM, Maier SE. Quantitative diffusion MRI in prostate cancer: Image quality, what we can measure and how it improves clinical assessment. European Journal of Radiology. 2023;167:111066. doi:10.1016/j.ejrad.2023.111066 [doi]
5. Jingjia Chen, Zhou K, Bruno M, et al. Distortion-Free Diffusion-Weighted Imaging of the Prostate Using TGSE-Based Golden-Angle PROPELLER Acquisition and Deep Learning Denoising. In: Proceedings of the 2025 Annual Meeting of ISMRM. 0664. Wiley; 2025.
6. Zhou K, Liu W, Cheng S. Non-CPMG PROPELLER diffusion imaging: comparison of phase insensitive preparation with split acquisition. In: Proc. Intl. Soc. Mag. Reson. Med. 26 (2018). 5320. https://cds.ismrm.org/protected/18MProceedings/PDFfiles/5320.html
7. Alsop DC. Phase insensitive preparation of single-shot RARE: Application to diffusion imaging in humans. Magnetic Resonance in Medicine. 1997;38(4):527-533. doi:10.1002/mrm.1910380404 [doi]
8. Lemberskiy G, Chandarana H, Bruno M, et al. Feasibility of Accelerated Prostate Diffusion-Weighted Imaging on 0.55 T MRI Enabled With Random Matrix Theory Denoising. Investigative Radiology. 2023;58(10):720. doi:10.1097/RLI.0000000000000979 [doi]
9. Veraart J, Novikov DS, Christiaens D, Ades-aron B, Sijbers J, Fieremans E. Denoising of diffusion MRI using random matrix theory. NeuroImage. 2016;142:394-406. doi:10.1016/j.neuroimage.2016.08.016 [doi]
10. Pipe JG, Farthing VG, Forbes KP. Multishot diffusion-weighted FSE using PROPELLER MRI. Magnetic Resonance in Medicine. 2002;47(1):42-52. doi:10.1002/mrm.10014 [doi]
11. Lehtinen J, Munkberg J, Hasselgren J, et al. Noise2Noise: Learning Image Restoration without Clean Data. arXiv. Preprint posted online October 29, 2018. doi:10.48550/arXiv.1803.04189 [doi]
12. Chen J, Pei H, Maier C, et al. Self-Supervised Joint Reconstruction and Denoising of T2-Weighted PROPELLER MRI of the Lungs at 0.55T. Published online 2025. https://arxiv.org/abs/2507.14308

Cite this abstract

http://echo.ismrm.org/p/ISMRM2026/662-04-004