A diffusion-matched principal component analysis (DM-PCA) based two-channel denoising procedure for high-resolution diffusion-weighted MRI
AffiliationUniv Arizona, Dept Biomed Engn
Univ Arizona, Dept Med Imaging
Univ Arizona, Dept Elect & Comp Engn
Univ Arizona, Inst BIO5
Univ Arizona, Evelyn F McKnight Brain Inst
MetadataShow full item record
PublisherPUBLIC LIBRARY SCIENCE
CitationChen N-k, Chang H-C, Bilgin A, Bernstein A, Trouard TP (2018) A diffusion-matched principal component analysis (DM-PCA) based two-channel denoising procedure for high-resolution diffusion-weighted MRI. PLoS ONE 13(4): e0195952. https://doi.org/10.1371/journal.pone.0195952
Rights© 2018 Chen et al. This is an open access article distributed under the terms of the Creative Commons Attribution License.
Collection InformationThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at firstname.lastname@example.org.
AbstractOver the past several years, significant efforts have been made to improve the spatial resolution of diffusion-weighted imaging (DWI), aiming at better detecting subtle lesions and more reliably resolving white-matter fiber tracts. A major concern with high-resolution DWI is the limited signal-to-noise ratio (SNR), which may significantly offset the advantages of high spatial resolution. Although the SNR of DWI data can be improved by denoising in post-processing, existing denoising procedures may potentially reduce the anatomic resolvability of high-resolution imaging data. Additionally, non-Gaussian noise induced signal bias in low-SNR DWI data may not always be corrected with existing denoising approaches. Here we report an improved denoising procedure, termed diffusion-matched principal component analysis (DM-PCA), which comprises 1) identifying a group of (not necessarily neighboring) voxels that demonstrate very similar magnitude signal variation patterns along the diffusion dimension, 2) correcting low-frequency phase variations in complex-valued DWI data, 3) performing PCA along the diffusion dimension for real-and imaginary-components (in two separate channels) of phase-corrected DWI voxels with matched diffusion properties, 4) suppressing the noisy PCA components in real-and imaginary-components, separately, of phase-corrected DWI data, and 5) combining real-and imaginary-components of denoised DWI data. Our data show that the new two-channel (i.e., for real-and imaginary-components) DM-PCA denoising procedure performs reliably without noticeably compromising anatomic resolvability. Non-Gaussian noise induced signal bias could also be reduced with the new denoising method. The DM-PCA based denoising procedure should prove highly valuable for high-resolution DWI studies in research and clinical uses.
NoteOpen access journal.
VersionFinal published version
SponsorsNIH [R01 NS 074045, R21 EB 018419]
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