Magnetic Resonance Histopathology (MRH): a direct, noninvasive, and low-cost diagnostic for quantitative microarchitecture imaging.
Motivation: To provide a non-invasive, rapid, low-cost diagnostic imaging method to quantitatively characterize microscopic tissue textures currently unresolvable by diagnostic imaging. Goal(s): Quantitative assessment of microscopic textures in the 10 to 1500 micrometer range with voxel dimensions similar to DWI. Either individual or multiple voxels providing multi-slice images in clinically relevant acquisition times. Approach: Excite a voxel and interrogate with a k-encode matching the spatial frequency being probed. Single voxel or multiple using a "read gradient" to separate multiple voxels. Results: MRH has demonstrated 39 μm resolution preclinically and 100 μm clinically in phantoms probing 6000 VOI in 20 seconds. Prostate scans confirm functionality. Impact: MRH provides a currently inaccessible quantitative measure of sentinel tissue microscopic characteristics enabling more informed diagnosis, reducing the need for excisional observation.
Authors
- Timothy W. James (ORCID: https://orcid.org/0000-0003-2633-5645)
- Kun Lu (ORCID: https://orcid.org/0000-0001-9526-1614)
- Kyle Williams (ORCID: https://orcid.org/0000-0003-4333-9111)
- Russell E. Jacobs (ORCID: https://orcid.org/0000-0002-1382-8486)
- Ian D. Parker
- Kristin M. James
- Ruslan Garipov
- Naomi S. Sta Maria (ORCID: https://orcid.org/0000-0001-9710-2908)
- Arnold Julian Vinoj Benjamin (ORCID: https://orcid.org/0000-0003-2063-8258)
- Dallas Turley
- Fei Han (ORCID: https://orcid.org/0000-0002-2720-392X)
- J. Samantha (ORCID: https://orcid.org/0000-0003-3127-455X)
- Gérard Crelier
Publication Details
- Journal
- Proceedings on CD-ROM - International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition/Proceedings of the International Society for Magnetic Resonance in Medicine, Scientific Meeting and Exhibition
- Published
- 2025-09-16
- DOI
- https://doi.org/10.58530/2025/3198
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 0.00