Abstract #2454

Improved parameter estimation for non-Gaussian IVIM using an unbiased vector non-local means

Lyu Jian^1,2, Xinyuan Zhang^1,2,3, Yingjie Mei⁴, and Li Guo^1,2,5

¹School of Biomedical Engineering, Southern Medical University, Guangzhou, China, ²Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou, China, ³Guangdong-Hong Kong-Macao Greater Bay Area Center for Brain Science and Brain-Inspired Intelligence, Guangzhou, China, ⁴Philips Healthcare, Guangzhou, China, ⁵Department of MRI, The First People’s Hospital of Foshan (Affiliated Foshan Hospital of Sun Yat-sen University), Foshan, China

Non-Gaussian intravoxel incoherent motion (NG-IVIM) has been proposed to simultaneously quantify the perfusion and non-Gaussian diffusion properties in tissues. However, accurate parameter estimation for NG-IVIM is usually challenged by noise. The noncentral χ-distribution noise would introduce bias in the estimated NG-IVIM parameters. In addition, severe noise easily causes the estimated parameter values have large variance. To improve the accuracy and precision of parameter estimation for NG-IVIM, we propose to use an unbiased vector non-local means (UVNLM) filter to denoise and correct the noise bias before NG-IVIM model fitting.

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