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Enhancement of Perivascular Spaces in 7 T MR Image using Haar Transform of Non-local Cubes and Block-matching Filtering

Title
Enhancement of Perivascular Spaces in 7 T MR Image using Haar Transform of Non-local Cubes and Block-matching Filtering
Authors
Hou, YingkunPark, Sang HyunWang, QianZhang, JunZong, XiaopengLin, WeiliShen, Dinggang
DGIST Authors
Park, Sang Hyun
Issue Date
2017
Citation
Scientific Reports, 7(1)
Type
Article
Article Type
Article
ISSN
2045-2322
Abstract
Perivascular spaces (PVSs) in brain have a close relationship with typical neurological diseases. The quantitative studies of PVSs are meaningful but usually difficult, due to their thin and weak signals and also background noise in the 7 T brain magnetic resonance images (MRI). To clearly distinguish the PVSs in the 7 T MRI, we propose a novel PVS enhancement method based on the Haar transform of non-local cubes. Specifically, we extract a certain number of cubes from a small neighbor to form a cube group, and then perform Haar transform on each cube group. The Haar transform coefficients are processed using a nonlinear function to amplify the weak signals relevant to the PVSs and to suppress the noise. The enhanced image is reconstructed using the inverse Haar transform of the processed coefficients. Finally, we perform a block-matching 4D filtering on the enhanced image to further remove any remaining noise, and thus obtain an enhanced and denoised 7 T MRI for PVS segmentation. We apply two existing methods to complete PVS segmentation, i.e., (1) vesselness-Thresholding and (2) random forest classification. The experimental results show that the PVS segmentation performances can be significantly improved by using the enhanced and denoised 7 T MRI. © 2017 The Author(s).
URI
http://hdl.handle.net/20.500.11750/4454
DOI
10.1038/s41598-017-09336-5
Publisher
Nature Publishing Group
Related Researcher
Files:
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Collection:
Robotics EngineeringETC1. Journal Articles


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