Meeting Banner
Abstract #3520

Comparing the LPCA and MPPCA denoising approaches for diffusion MRI using simulated human data

Qiuting Wen1, Mark S. Graham2, Sourajit M. Mustafi1, Ivana Drobnjak2, Hui Zhang2, and Yu-Chien Wu1

1Indiana University, Indianapolis, IN, United States, 2University of College London

In this study, we investigate two denoising methods for diffusion MRI: the local PCA approach and Marchenko-Pastur (MP) PCA approach. Ground-truth diffusion-weighted images of the human brain are developed and used for noise simulation. Two diffusion-weighting b-values and two noise levels are generated as input data for both denoisers. Metrics of diffusion tenor imaging (DTI) and neurite orientation distribution and density (NODDI) are computed after denoising and compared between denoise methods.

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

Join Here