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Abstract #4396

Improved estimation of diffusion tensors by noise-corrected curve fitting with adaptive neighborhood regularization

Li Guo1,2,3, Xinyuan Zhang2,3, Changqing Wang4, Jian Lyu2,3, Yingjie Mei5, Ruiliang Lu1, Mingyong Gao1, and Yanqiu Feng2,3
1Department of MRI, The First People’s Hospital of Foshan (Affiliated Foshan Hospital of Sun Yat-sen University), Foshan, China, 2School of Biomedical Engineering, Southern Medical University, Guangzhou, China, 3Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou, China, 4School of Biomedical Engineering, Anhui Medical University, Hefei, China, 5Philips Healthcare, Guangzhou, China

The noncentral Chi noise in magnitude image may significantly affect the reliability of quantitative analysis in diffusion-weighted (DW) magnetic resonance imaging (MRI), especially at high b-value and/or higher order modeling of diffusion signal such as diffusion kurtosis imaging (DKI). We developed a novel first-moment noise-corrected curve fitting model with adaptive neighborhood regularization (MN1CM-ANR) algorithm for DKI. By fitting the signal to its first-moment (i.e. the expectation of the signal), MN1CM-ANR can effectively compensate the bias due to the noncentral Chi noise. In addition, by exploiting the neighboring pixels to regularize the curve fitting, MN1CM-ANR can reduce the measurement variance.

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