Yudu Li1,2, Jiahui Xiong1,2, Rong Guo1,2, Yibo Zhao1,2, Yao Li3,4, and Zhi-Pei Liang1,2
1Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Urbana, IL, United States, 2Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, IL, United States, 3School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China, 4Med-X Research Institute, Shanghai, China
1Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Urbana, IL, United States, 2Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, IL, United States, 3School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China, 4Med-X Research Institute, Shanghai, China
This
work addresses the problem of robust MWF estimation. A new model was proposed, which is capable of compensating
practical signal errors; a Bayesian-based method was developed for robust
parameter estimation, which incorporates parameter distributions and low-rank
signal structures.
Figure 2: In vivo results obtained
from one healthy subject, including the estimated MWF maps by different
schemes, T1W images, and B0 field maps. As can be seen, the
MWF maps from the proposed method are of much higher quality than those
produced by the other methods, especially in the regions where B0
inhomogeneities are large (e.g. the region marked by blue arrows).
Figure 4: Representative results
obtained from multiple healthy subjects. The MWF maps produced by the proposed
method are consistent among different subjects.
