Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026 · Cape Town, South Africa
606-03-002 ISMRM Abstract

Enhanced Ultra Low-Field Diffusion Tensor Imaging with Direction-Dependent Bias Correction and Spatio-Angular Superresolution

Primary: Diffusion - DWI/DTI/DKI
Secondary: Physics & Engineering - Low-Field MRI
606-03-002 · Low Field MRI Applications · Thursday, 14 May, 4:00 PM–5:50 PM · Auditorium 2
Keywords: Software Tools Super-resolution Ultra-Low-Field MRI Diffusion Tensor Imaging (DTI) Bias field correction
Accepted
Mark Olchanyi 1,2,3, Annabel Sorby-Adams3,4, John Kirsch4, Brian Edlow3,4, Ava Farnan3, Matthew S Rosen4, Emery Brown1,2,4, W. Taylor Kimberly3,4, Juan E Iglesias5,6
1Picower Institute, Massachusetts Institute of Technology, Cambridge, United States of America
2Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America
3Massachusetts General Hospital, Boston, United States of America
4Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Charlestown, United States of America
5Computer Science and Artificial Intelligence Lab, Massachusetts Institute of Technology, Cambridge, United States of America
6Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Charlestown, United States of America
Presenting Author: Mark Olchanyi

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References

1. Y. Liu et al., “A low-cost and shielding-free ultra-low-field brain MRI scanner,” Nat Commun, vol. 12, no. 1, p. 7238, Dec. 2021, doi: 10.1038/s41467-021-27317-1. [doi]
2. K. N. Sheth et al., “Assessment of Brain Injury Using Portable, Low-Field Magnetic Resonance Imaging at the Bedside of Critically Ill Patients,” JAMA Neurol, vol. 78, no. 1, p. 41, Jan. 2021, doi: 10.1001/jamaneurol.2020.3263. [doi]
3. C. Z. Cooley et al., “A portable scanner for magnetic resonance imaging of the brain,” Nat Biomed Eng, vol. 5, no. 3, pp. 229–239, Nov. 2020, doi: 10.1038/s41551-020-00641-5. [doi]
4. A. J. Sorby-Adams et al., “Portable, low-field magnetic resonance imaging for evaluation of Alzheimer’s disease,” Nat Commun, vol. 15, no. 1, p. 10488, Dec. 2024, doi: 10.1038/s41467-024-54972-x. [doi]
5. W. T. Kimberly et al., “Brain imaging with portable low-field MRI,” Nature Reviews Bioengineering, vol. 1, no. 9, pp. 617–630, Jul. 2023, doi: 10.1038/s44222-023-00086-w. [doi]
6. J. Gholam et al., “Diffusion Tensor MRI and Spherical-Deconvolution-Based Tractography on an Ultra-Low Field Portable MRI System,” ArXiv, Jun. 2025.
7. A. Yendiki et al., “Automated probabilistic reconstruction of white-matter pathways in health and disease using an atlas of the underlying anatomy,” Front Neuroinform, vol. 5, p. 23, Oct. 2011, doi: 10.3389/fninf.2011.00023. [doi]
8. J. E. Iglesias, K. Van Leemput, P. Golland, and A. Yendiki, “Joint Inference on Structural and Diffusion MRI for Sequence-Adaptive Bayesian Segmentation of Thalamic Nuclei with Probabilistic Atlases,” in Inf Process Med Imaging, 2019, pp. 767–779. doi: 10.1007/978-3-030-20351-1_60. [doi]

Cite this abstract

http://echo.ismrm.org/p/ISMRM2026/606-03-002