Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026 · Cape Town, South Africa
365-06-012 ISMRM Abstract

Scanner-Adaptive Coil-Level Denoising for Diffusion MRI Using Explicit Noise Priors

Primary: Acquisition & Reconstruction - AI methods
Secondary: Diffusion - Diffusion Reconstruction
365-06-012 · Diffusion MRI Reconstruction Methods · Monday, 11 May, 5:05 PM–6:00 PM · Digital Posters Row F
Keywords: Diffusion MRI Deep learning Denoising Coil-adaptive Noise Prior
Accepted
Linbo Tang1,2, Qiang Liu2,3, Lipeng Ning2, Yogesh Rathi 2
1John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, United States of America
2Department of Psychiatry, Brigham and Women's Hospital and Harvard Medical School, Boston, United States of America
3College of Engineering, Northeastern University, Boston, United States of America
Presenting Author: Yogesh Rathi

Synopsis

Motivation:
Goals:
Approach:
Results:
Full abstract & presentation

The full text, figures, and any recorded presentation for this abstract are not shown here. Log in if you are a member or registered attendee with access.

Full abstracts, figures, and presentations for Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition are available to registered attendees. This content becomes freely available to the public roughly two years after the meeting.

To request or purchase access, contact the ISMRM Central Office at info@ismrm.org.

Log in

References

1. 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]
2. Henriques RN, Jespersen SN, Shemesh N. Hybrid PCA denoising-improving PCA denoising in the presence of spatial correlations. In: Proceedings of the 30’th Scientific Meeting of the International Society for Magnetic Resonance in Medicine. 2022:84. doi: 10.1016/j.neuroimage.2020.117539 [doi]
3. Moeller S, Pisharady PK, Ramanna S, et al. NOise reduction with DIstribution Corrected (NORDIC) PCA in dMRI with complex-valued parameter-free locally low-rank processing. Neuroimage. 2021;226:117539. doi: 10.1016/j.neuroimage.2020.117539 [doi]
4. Fadnavis S, Batson J, Garyfallidis E. Patch2Self: Denoising Diffusion MRI with Self-Supervised Learning. Adv Neural Inf Process Syst. 2020;33:16293-16303. doi: 10.48550/arXiv.2011.01355 [doi]
5. Zamir SW, Arora A, Khan S, Hayat M, Khan FS, Yang MH. Restormer: Efficient transformer for high-resolution image restoration. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2022:5728-5739. doi: 10.1109/CVPR52688.2022.00564 [doi]
6. Chen L, Chu X, Zhang X, Sun J. Simple baselines for image restoration. In: European Conference on Computer Vision. 2022:17-33. doi: 10.1007/978-3-031-20071-7_2 [doi]
7. Liu Q, Ning L, Shaik IA, et al. Reduced cross-scanner variability using vendor-agnostic sequences for single-shell diffusion MRI. Magn Reson Med. 2024;92(1):246-256. doi: 10.1002/mrm.30062 [doi]
8. Perez E, Strub F, De Vries H, Dumoulin V, Courville A. Film: Visual reasoning with a general conditioning layer. In: Proceedings of the AAAI Conference on Artificial Intelligence. Vol 32. 2018. doi: 10.1609/aaai.v32i1.11671 [doi]
9. Esin YE. Coil Sensitivity Map Calculation Using Biot-Savart Law at 3 Tesla and Parallel Imaging in MRI. Middle East Technical University (Turkey); 2017.
10. Jeny Rajan, Dirk Poot, Jaber Juntu, and Jan Sijbers, “Noise measurement from magnitude mri using local estimates of variance and skewness,” Physics in medicine & biology, vol. 55, no. 16, pp. N441, 2010. doi: 10.1088/0031-9155/55/16/N02 [doi]

Cite this abstract

http://echo.ismrm.org/p/ISMRM2026/365-06-012