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
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
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.
Fig.
4. f, D*, Dapp, Kapp maps and their corresponding
error maps of the proposed UVNLM-NLS method. The error maps show the absolute
difference between the reference parameters and the estimated parameters.
Fig. 1. RMSE comparisons
of f, D*, Dapp, Kapp
maps estimated with the conventional NLS, the PCA-NLS, the proposed UVNLM-NLS
method.