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.
Highlights
We verify the effectiveness of our enhancement method by comparing the Perivascular spaces (PVSs) segmentation results achieved from (1) the original 7 T MR images, (2) the denoised images, and (3) our enhanced and denoised images
The segmentation performance is evaluated by the Dice similarity coefficient (DSC), sensitivity (SN), and positive prediction value (PPV) as defined below: DSC =
Our predicted PVSs are very similar to the ground-truth labels with the best PVS segmentation results, indicating that our proposed image enhancement and denoising method is helpful for PVS segmentation
Summary
The nonlocal filtering methods have been further extended to address the higher-dimensional problems (BM4D)[27, 28] in video and 3D medical image data Both types of these nonlocal methods and BM3D methods are effective on image denoising, they fail to enhance the PVSs in 7 T MR images since PVSs are tiny structure in the 7 T MR images and the matched nonlocal image blocks are highly similar to each other. The experimental results show that our proposed enhancement and denoising method can significantly improve image quality and potentially help with PVS segmentation
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