Cite this abstract
http://echo.ismrm.org/p/ISMRM2025/4240
4240
ISMRM Abstract
Electromagnetic Interference Removal in Low-Field MRI via EDITER, Structured Low-Rank Modeling, and Denoising Diffusion Model
Accepted
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 2025 ISMRM and ISMRT Annual Meeting and Exhibition are available to registered attendees. This content becomes freely available to the public on 15 May 2027.
To request or purchase access, contact the ISMRM Central Office at support@ismrm.org.
Log inReferences
1. Wald LL, McDaniel PC, Witzel T, Stockmann JP, Cooley CZ. Low-cost and portable MRI. Journal of Magnetic Resonance Imaging. 2020;52(3):686-696. doi:10.1002/jmri.26942 2. Marques JP, Simonis FFJ, Webb AG. Low-field MRI: An MR physics perspective. Journal of Magnetic Resonance Imaging. 2019;49(6):1528-1542. doi:10.1002/jmri.26637 3. Geethanath S, Vaughan Jr. JT. Accessible magnetic resonance imaging: A review. Journal of Magnetic Resonance Imaging. 2019;49(7):e65-e77. doi:10.1002/jmri.26638 4. O’Reilly T, Teeuwisse WM, de Gans D, Koolstra K, Webb AG. In vivo 3D brain and extremity MRI at 50 mT using a permanent magnet Halbach array. Magnetic Resonance in Medicine. 2021;85(1):495-505. doi:10.1002/mrm.28396 5. Cooley CZ, McDaniel PC, Stockmann JP, et al. A portable scanner for magnetic resonance imaging of the brain. Nat Biomed Eng. 2021;5(3):229-239. doi:10.1038/s41551-020-00641-5 6. Srinivas SA, Cauley SF, Stockmann JP, et al. External Dynamic InTerference Estimation and Removal (EDITER) for low field MRI. Magnetic Resonance in Medicine. 2022;87(2):614-628. doi:10.1002/mrm.28992 7. Zhao Y, Ding Y, Lau V, et al. Whole-body magnetic resonance imaging at 0.05 Tesla. Science. 2024;384(6696):eadm7168. doi:10.1126/science.adm7168 8. Koonjoo N, Zhu B, Bagnall GC, Bhutto D, Rosen MS. Boosting the signal-to-noise of low-field MRI with deep learning image reconstruction. Sci Rep. 2021;11(1):8248. doi:10.1038/s41598-021-87482-7 9. Ayde R, Senft T, Salameh N, Sarracanie M. Deep learning for fast low-field MRI acquisitions. Sci Rep. 2022;12(1):11394. doi:10.1038/s41598-022-14039-7 10. de Leeuw den Bouter ML, Ippolito G, O’Reilly TPA, Remis RF, van Gijzen MB, Webb AG. Deep learning-based single image super-resolution for low-field MR brain images. Sci Rep. 2022;12(1):6362. doi:10.1038/s41598-022-10298-6 11. Lau V, Xiao L, Zhao Y, et al. Pushing the limits of low-cost ultra-low-field MRI by dual-acquisition deep learning 3D superresolution. Magnetic Resonance in Medicine. 2023;90(2):400-416. doi:10.1002/mrm.29642 12. Lin H, Figini M, D’Arco F, et al. Low-field magnetic resonance image enhancement via stochastic image quality transfer. Medical Image Analysis. 2023;87:102807. doi:10.1016/j.media.2023.102807 13. Haldar JP. Low-Rank Modeling of Local $k$-Space Neighborhoods (LORAKS) for Constrained MRI. IEEE Transactions on Medical Imaging. 2014;33(3):668-681. doi:10.1109/TMI.2013.2293974 14. Kim TH, Setsompop K, Haldar JP. LORAKS makes better SENSE: Phase-constrained partial fourier SENSE reconstruction without phase calibration. Magnetic Resonance in Medicine. 2017;77(3):1021-1035. doi:10.1002/mrm.26182 15. Wang Y, Yu J, Zhang J. Zero-Shot Image Restoration Using Denoising Diffusion Null-Space Model. In: International Conference on Learning Representations (ICLR). [doi]
2. Jalal A, Arvinte M, Daras G, Price E, Dimakis AG, Tamir J. Robust Compressed Sensing MRI with Deep Generative Priors. In: Advances in Neural Information Processing Systems (NeurIPS).
3. Chung H, Ye JC. Score-based diffusion models for accelerated MRI. Medical Image Analysis. 2022;80:102479. doi:10.1016/j.media.2022.102479 18. Chung H, Lee S, Ye JC. Decomposed Diffusion Sampler for Accelerating Large-Scale Inverse Problems. International Conference on Learning Representations (ICLR). [doi]
4. Dabov K, Foi A, Katkovnik V, Egiazarian K. Image Denoising by Sparse 3-D Transform-Domain Collaborative Filtering. IEEE Transactions on Image Processing. 2007;16(8):2080-2095. doi:10.1109/TIP.2007.901238 [doi]