Abstract #4902

A Model-Based Method for Estimation of Myelin Water Fractions

Yudu Li^1,2, Rong Guo^1,2, Yibo Zhao^1,2, Yang Chen^1,3, Bryan Clifford^1,2, Tianyao Wang⁴, Chenyan Wang^1,5, Yiping Du⁶, Yao Li^6,7, and Zhi-Pei Liang^1,2

¹Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, IL, United States, ²Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Urbana, IL, United States, ³Department of Mathematics & Statistics, Xi’an Jiaotong University, Xi'an, China, ⁴Department of Radiology, The Fifth People's Hospital of Shanghai, Shanghai, China, ⁵Institute for Medical Imaging Technology (IMIT), School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China, ⁶School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China, ⁷Med-X Research Institute, Shanghai Jiao Tong University, Shanghai, China

Quantitative mapping of myelin water fractions (MWF) can substantially improve our understanding of the progression of several demyelination white matter diseases such as multiple sclerosis. While MWF can be determined from both T2-weighted and T2*-weighted imaging data, it is much faster to collect T2*-weighted imaging data. However, estimation of MWF from T2*-weighted imaging data using a multi-exponential component model is an ill-conditioned problem whose solutions are often very sensitive to noise and modeling errors. In this work, we address this problem using a new model-based method. This method is characterized by: a) absorbing the spectral priors using the Bayesian-based statistical framework, and b) absorbing the spatial priors using a reproducing kernel based model. Both simulation and experimental results show the proposed method significantly outperforms the conventional parameter estimation methods currently used for MWF estimation.

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