Year: 2016
Type: conference-paper
Abstract: Noise is a major issue influencing quantitative analysis in diffusion MRI. The effects of noise can be reduced by repeated acquisitions, but this leads to long acquisition times that can be unrealistic in clinical settings. For this reason, post-acquisition denoising methods have been widely used to improve SNR. Among existing methods, non-local means (NLM) has been shown to produce good image quality with edge preservation. However, currently the application of NLM to diffusion MRI has been mostly focused on the spatial space (i.e., the x -space), despite the fact that diffusion data live in a combined space consisting of the x -space and the q -space (i.e., the space of wavevectors). In this paper, we propose to extend NLM to both x -space and q -space. We show how patch-matching, as required in NLM, can be performed concurrently in x - q space with the help of azimuthal equidistant projection and rotation invariant features. Extensive experiments on both synthetic and real data confirm that the proposed x - q space NLM (XQ-NLM) outperforms the classic NLM. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
Authors: Geng Chen, Yafeng Wu, Dinggang Shen, Pew-Thian Yap
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $5,392
Awards: Infant Brain Measurement and Super-Resolution Atlas Construction, Quantifying Brain Abnormality by Multimodality Neuroimage Analysis, Longitudinal Mapping of Human Brain Development in the First Years of Life, Fast, Robust Analysis of Large Population Data, Development of Robust Brain Measurement Tools Informed by Ultrahigh Field 7T MRI, Robust White Matter Morphometry with Small Databases
OpenAlex ID: W2523850218