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.
Highlights
Diffusion weighted imaging (DWI), through characterizing properties of proton diffusion, can assess microstructural changes of brain tissues resulting from neurological diseases [1, 2].DWI data that are appropriately sampled in q-space can be fit to a tensor or other models to characterize the structure of white-matter
The external capsule and extreme capsules are resolvable in the fractional anisotropy (FA) map calculated from the original DWI data (SNR ~ = 25) both before and after denoising
It can be seen that the mean DWI map reveals only coarse hippocampal structure (Fig 6F), while the colorcoded FA map more clearly defines the dentate gyrus, fibers that connect hippocampus and entorhinal cortex to other brain areas, and CA1, CA2 and CA3 of the hippocampus
Summary
Diffusion weighted imaging (DWI), through characterizing properties of proton diffusion, can assess microstructural changes of brain tissues resulting from neurological diseases [1, 2]. DWI data that are appropriately sampled in q-space can be fit to a tensor or other models to characterize the structure of white-matter. MRI procedures that can correct for shot-to-shot phase inconsistencies are the keys in enabling high-resolution DWI [12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42]. The advantages of high-resolution DWI have been demonstrated in a series of recent reports [18,43,44,45]
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