Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026 · Cape Town, South Africa
668-03-004 ISMRM Abstract

Rapid Physics-Consistent QTI Model Fitting using a Hierarchical Encoder-Decoder Network

Primary: Diffusion - Microstructure
Secondary: Diffusion - Diffusion Analysis & Visualization
668-03-004 · Quantitative MRI Software: Tools and Applications · Thursday, 14 May, 1:40 PM–2:35 PM · Digital Posters Row I
Keywords: Diffusion Analysis and Visualization Deep learning Q-space trajectory imaging Diffusion Microstructure
Accepted
Wenwen Sun1, Mahsa Rajabi2, Mathews Jacob2, Merry Mani1,3
1Department of Biomedical Engineering, University of Virginia, Charlottesville, United States of America
2Department of Electrical and Computer Engineering, University of Virginia, Charlottesville, United States of America
3Department of Radiology and Medical Imaging, University of Virginia, Charlottesville, United States of America
Presenting Author: Chu-Yu Lee

Synopsis

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References

1. Westin CF, Knutsson H, Pasternak O, Szczepankiewicz F, Özarslan E, van Westen D, Mattisson C, Bogren M, O'Donnell LJ, Kubicki M, Topgaard D, Nilsson M. Q-space trajectory imaging for multidimensional diffusion MRI of the human brain. NeuroImage. 2016;135:345-362.
2. Nilsson M, Szczepankiewicz F, Brabec J, Taylor M, Westin CF, Golby A, van Westen D, Sundgren PC. Tensor-valued diffusion MRI in under 3 minutes: an initial survey of microscopic anisotropy and tissue heterogeneity in intracranial tumors. Magn Reson Med. 2020;83(2):608-620.
3. Herberthson M, Boito D, Haije TD, Feragen A, Westin CF, Özarslan E. Q-space trajectory imaging with positivity constraints (QTI+). NeuroImage. 2021;238:118198.
4. Golkov V, Dosovitskiy A, Sperl JI, Menzel MI, Czisch M, Sämann P, Brox T, Cremers D. q-Space deep learning: twelve-fold shorter and model-free diffusion MRI scans. IEEE Trans Med Imaging. 2016;35(5):1344-1351.
5. Tian Q, Bilgic B, Fan Q, Liao C, Ngamsombat C, Hu Y, Witzel T, Setsompop K, Polimeni JR, Huang SY. DeepDTI: High-fidelity six-direction diffusion tensor imaging using deep learning. NeuroImage. 2020;219:117017.
6. Li Z, Gong T, Lin Z, He H, Tong Q, Li C, Sun Y, Yu F, Zhong J. Fast and robust diffusion kurtosis parametric mapping using a three-dimensional convolutional neural network. IEEE Access. 2019;7:71398-71411.
7. Karimi D, Vasung L, Jaimes C, Machado-Rivas F, Warfield SK, Gholipour A. Learning to estimate the fiber orientation distribution function from diffusion-weighted MRI. NeuroImage. 2021;239:118316.
8. Gyori NG, Clark CA, Alexander DC, Kaden E. Training data distribution significantly impacts the estimation of tissue microstructure with machine learning. Magn Reson Med. 2022;87(2):932-947.
9. Rizor EJ, Babenko V, Dundon NM, et al. Hormone Health Study (HHS) [dataset]. OpenNeuro; 2024. doi:10.18112
10. Chen G, Hong Y, Huynh KM, Yap PT. Deep learning prediction of diffusion MRI data with microstructure-sensitive loss functions. Med Image Anal. 2023;85:102742.
11. Gibbons EK, Hodgson KK, Chaudhari AS, Richards LG, Majersik JJ, Adluru G, DiBella EVR. Simultaneous NODDI and GFA parameter map generation from subsampled q-space imaging using deep learning. Magnetic Resonance in Medicine. 2019;81:2399-2411.

Cite this abstract

http://echo.ismrm.org/p/ISMRM2026/668-03-004