Abstract #1914

A noise correction model incorporating weighted neighborhood information for liver R2* mapping

Changqing Wang^1,2,3, Xinyuan Zhang², Yanying Ma⁴, Xiaoyun Liu¹, Diego Hernando³, Scott B. Reeder^3,5,6,7,8, Wufan Chen^1,2, and Yanqiu Feng²

¹School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu, China, People's Republic of, ²School of Biomedical Engineering and Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou, China, People's Republic of, ³Radiology, University of Wisconsin-Madison, Madison, WI, United States, ⁴School of Mathematical Sciences, University of Electronic Science and Technology of China, Chengdu, China, People's Republic of, ⁵Medical Physics, University of Wisconsin-Madison, Madison, WI, United States, ⁶Biomedical Engineering, University of Wisconsin-Madison, Madison, WI, United States, ⁷Medicine, University of Wisconsin-Madison, Madison, WI, United States, ⁸Emergency Medicine, University of Wisconsin-Madison, Madison, WI, United States

R2* mapping has the potential to provide rapid and accurate quantification of liver iron overload. However, conventional voxelwise liver R2* mapping methods are challenging when using echo images with low signal-noise ratio (SNR). The purpose of this work was to improve liver R2* mapping by a noise correction model incorporating weighted neighborhood information. Simulation and in vivo results demonstrate that the proposed method produces more accurate R2* maps with high spatial resolution compared to two recently proposed R2* mapping methods.

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