Abstract #4396

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

Li Guo^1,2,3, Xinyuan Zhang^2,3, Changqing Wang⁴, Jian Lyu^2,3, Yingjie Mei⁵, Ruiliang Lu¹, Mingyong Gao¹, and Yanqiu Feng^2,3

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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