1760 ISMRM Abstract

Denoising intrinsic MRI repetitions using self-supervised iterative residual learning

1760 · Self-Supervised AI/ML Techniques · Monday, 6 May, 1:45 PM–2:45 PM · Exhibition Hall (Hall 403)

Keywords: Analysis/Processing Data Processing Diffusion Tensor Imaging Magnetic resonance imaging Self-supervised learning Transfer learning

Accepted

Zihan Li ¹, Berkin Bilgic^2,3, Ziyu Li⁴, Kui Ying⁵, David H Salat^2,3, Jonathan R Polimeni^2,3, Hongen Liao¹, Susie Huang^2,3, Qiyuan Tian¹

¹Department of Biomedical Engineering, Tsinghua University, Beijing, China

²Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston, United States of America

³Harvard Medical School, Boston, United States of America

⁴Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom

⁵Department of Engineering Physics, Tsinghua University, Beijing, China

Presenting Author: Zihan Li

Full abstract & presentation

The full text, figures, and any recorded presentation for this abstract are not shown here.

This content is freely available and no longer restricted to registered attendees. Log in or create a free account to view it.

References

1. Zhang K, Zuo W, Chen Y, Meng D, Zhang L. Beyond a gaussian denoiser: Residual learning of deep cnn for image denoising. IEEE transactions on image processing . 2017;26(7):3142-3155.

2. Tian Q, Zaretskaya N, Fan Q, et al. Improved cortical surface reconstruction using sub-millimeter resolution MPRAGE by image denoising. NeuroImage . 2021;233:117946.

3. Li Z, Tian Q, Ngamsombat C, et al. High ‐ fidelity fast volumetric brain MRI using synergistic wave ‐ controlled aliasing in parallel imaging and a hybrid denoising generative adversarial network (HDnGAN). Medical physics . 2021; 4. Lehtinen J, Munkberg J, Hasselgren J, et al. Noise2Noise: Learning Image Restoration without Clean Data. The International Conference on Machine Learning . 2018:2965-2974.

4. Ledig C, Theis L, Huszár F, et al. Photo-realistic single image super-resolution using a generative adversarial network. Proceedings of the IEEE conference on computer vision and pattern recognition . 2017:4681-4690.

5. Li Z, Bilgic B, Li Z, Ying K, Polimeni J, Huang S, Tian Q. Noise2Average: deep learning based denoising without high-SNR training data using iterative residual learning. Proceedings of the 31st Annual Meeting of the International Society for Magnetic Resonance in Medicine (ISMRM) 2022 . 2022; 7. Smith SM, Jenkinson M, Woolrich MW, et al. Advances in functional and structural MR image analysis and implementation as FSL. Neuroimage . 2004;23:S208-S219.

6. Jenkinson M, Beckmann CF, Behrens TEJ, Woolrich MW, Smith SM. FSL. NeuroImage . 8/15/ 2012;62(2):782-790.

7. Andersson JL, Skare S, Ashburner J. How to correct susceptibility distortions in spin-echo echo-planar images: application to diffusion tensor imaging. Neuroimage . 2003;20(2):870-888.

8. Andersson JL, Sotiropoulos SN. An integrated approach to correction for off-resonance effects and subject movement in diffusion MR imaging. NeuroImage . 2016;125:1063-1078.

9. van der Kouwe AJ, Benner T, Salat DH, Fischl B. Brain morphometry with multiecho MPRAGE. NeuroImage . 2008;40(2):559-569.

10. Fan Q, Witzel T, Nummenmaa A, et al. MGH–USC Human Connectome Project datasets with ultra-high b-value diffusion MRI. NeuroImage . 2016;124:1108-1114.

11. Maggioni M, Katkovnik V, Egiazarian K, Foi A. Nonlocal transform-domain filter for volumetric data denoising and reconstruction. IEEE transactions on image processing . 2012;22(1):119-133.

12. Maggioni M, Foi A. Nonlocal transform-domain denoising of volumetric data with groupwise adaptive variance estimation. Computational Imaging X . 2012;8296:82960O 15. Manjón JV, Coupé P, Martí‐Bonmatí L, Collins DL, Robles M. Adaptive non‐local means denoising of MR images with spatially varying noise levels. Journal of Magnetic Resonance Imaging. 2010;31(1):192-203.

Cite this abstract

http://echo.ismrm.org/p/ISMRM2024/1760