A diffusion-matched principal component analysis (DM-PCA) based two-channel denoising procedure for high-resolution diffusion-weighted MRI

Abstract

Over 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.

Evidence in support of chromosomal sex influencing plasma based metabolome vs APOE genotype influencing brain metabolome profile in humanized APOE male and female mice

“…Raw, low-resolution, dMRI images were motion and eddy-current corrected using FSL's eddy-correct [62] and denoised using a diffusion-matched principal component analysis technique [63]. Subsequently, the three low resolution datasets were reconstructed using in-house super-resolution reconstruction software, written in Julia [64], to generate 150 μm isotropic dMRI data.…”

Principal component analysis for fast and model-free denoising of multi b-value diffusion-weighted MR images

“…In particular, (Pai et al 2011 ) used PCA prior to removing noisy outliers when calculating maximum intensity projection in cardiac DWI. Furthermore, (Chen et al 2018 ) recently utilized diffusion-matched PCA on the magnitude and phase DW data to denoise high-resolution brain diffusion tensor imaging. Spinner et al ( 2018 ) introduced k-b PCA in IVIM images, which enables higher parallel imaging without adding additional noise.…”

“…The approaches cited above in which PCA was applied for DWI mostly focus on brain imaging (Manjón et al 2013 , Chen et al 2018 , Spinner et al 2018 ), meet very specific denoising needs (reduce noise for MIP (Pai et al 2011 ), enable higher parallel imaging (Spinner et al 2018 )) and are evaluated with regard to their effect on diffusion modelling. We set out to develop a generalizable and simple approach for improving image quality for diagnostic purposes that could be easily applied in any anatomy.…”

Brain multimodal co-alterations related to delay discounting: a multimodal MRI fusion analysis in persons with and without cocaine use disorder

“…The T1w images were pre-processed using standard methods [ 62 , 63 ], and then participant-level maps of GMV were created using voxel-based morphometry [ 64 , 65 ]. The DWI data were denoised [ 66 ], motion and eddy-corrected using DTIPrep [ 67 ], and then preprocessed using standard FSL tools [ 61 ]. FA was calculated within all white matter voxels.…”