Meeting Banner
Abstract #2454

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

Lyu Jian1,2, Xinyuan Zhang1,2,3, Yingjie Mei4, and Li Guo1,2,5
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.

This abstract and the presentation materials are available to members only; a login is required.

Join Here