Abstract #1093

Improved Myelin/Axonal/Extracellular Water Separation Using Adaptive Probabilistic Subspaces and T1W-Translated Spatial Priors

Ruihao Liu^1,2, Yudu Li^1,3,4, Yue Guan^5,6, Ziwen Ke^1,2,6, Shuoyun Feng², Weijun Tang⁷, Yao Li^2,5,6, Yiping P. Du², and Zhi-Pei Liang^1,8

¹Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, IL, United States, ²School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China, ³Department of Bioengineering, University of Illinois at Urbana-Champaign, Urbana, IL, United States, ⁴National Center for Supercomputing Applications, University of Illinois at Urbana-Champaign, Urbana, IL, United States, ⁵National Engineering Research Center of Advanced Magnetic Resonance Technologies for Diagnosis and Therapy, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China, ⁶Institute of Medical Robotics, Shanghai Jiao Tong University, Shanghai, China, ⁷Huashan Hospital, Fudan University, Shanghai, China, ⁸Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Urbana, IL, United States

Synopsis

Keywords: Quantitative Imaging, Quantitative Imaging

Motivation: Separating myelin, axonal, and extracellular water components from brain gradient-echo imaging data is desirable for characterizing many brain diseases but involves solving a challenging ill-conditioned signal decomposition problem.

Goal(s): To develop an effective method to solve the underlying ill-conditioned problem.

Approach: We solved the signal decomposition using an adaptive probabilistic subspace model incorporating spatial constraints. This method effectively compensates for practical perturbations by adapting subspace bases to individual imaging dataset and stabilizes decomposition by integrating spatiospectral priors from companion T1W images via deep translation.

Results: Simulations and experimental results showed significantly improved maps of myelin/axonal/extracellular water over existing methods.

Impact: This method may improve the practical utility of myelin/axonal/extracellular water fraction mapping. The integration of deep translation priors and adaptive spectral priors provides a promising framework for solving other ill-conditioned inverse problems.

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