Principal component analysis denoising improves sensitivity of MR diffusion to detect white matter injury in neuroHIV

Abstract

Background and Purpose: Diffusion-weighted imaging is able to capture important information about cerebral white matter (WM) structure. However, diffusion data can suffer from MRI and biological noise that degrades the quality of the images and makes finding important features difficult. We investigated how effectively local and nonlocal denoising increased the sensitivity to detect differences in cerebral WM in neuroHIV. Methods:We utilized principal component analysis (PCA) denoising to detect WM differences using fractional anisotropy. Local and nonlocal PCA denoising paradigms were implemented that varied in search area and number of components. We examined different-sized WM tracts that consistently show differences between people living with Human Immunodeficiency Virus (HIV) (PWH) and HIV-negative individuals (corpus callosum, forceps minor, and right uncinate fasciculus), and size-matched tracts not typically associated with HIV-related differences (spinothalamic, right medial lemniscus, and left occipitopontine). We first conducted a full sample comparison of WM differences between groups, and then randomly reduced the sample to the point where we still found differences in WM. Results:Nonlocal PCA denoising allowed us to detect differences after a sample reduction of 35% in the forceps minor, 17% in the right uncinate fasciculus, and 6% in the corpus callosum.Conclusions: PCA denoising had a beneficial effect on detecting significant differences in PWH after sample size reduction. The smaller forceps minor tract and right uncinate fasciculus showed greater sensitivity to PCA denoising than the larger corpus callosum.These results show the importance of identifying the most effective PCA denoising strategy when investigating WM in PWH.

Research on singular value decomposition denoising algorithm based on a genetic algorithm and FFT and its application

“…In this paper, we conducted spectral data simulations using the HITRAN database and plotted the absorption spectral line transmission of O2 from 13145 cm -1 to 13150 cm -1 at 1 atm and 296 K, which covered two absorption peaks with central wave numbers of 13146.58350 cm -1 and 13148.15051 cm -1 and line intensities of 8.571× 10 -24 cm/molecule and 6.748× 10 -24 cm/molecule. As shown in Figure 1, random Gaussian white noise is added to the simulated transmission spectrum using Python software, and the signal-to-noise ratio of the noise-containing simulated spectral signal is 8.12 dB according to equation (8), where the number of sampling points is 777. To show the complete convergence characteristics of the GA module, the singular value energies corresponding to different m are calculated, as shown in Figure 3(Upper panel).…”

“…In pursuit of improving the signal-to-noise ratio and achieving signal-to-noise separation, many scholars at home and abroad have conducted a lot of research and formed many signal processing theories, including S-G filter [4], wavelet transform denoising [5,6] and principal component analysis (PCA) [7,8] different denoising algorithms have their limitations, such as the window size and polynomial order of the S-G filter, which are judged empirically, the wavelet transform has different adaptive capabilities due to the diversity of wavelet bases, hence a soft threshold or a hard threshold is reasonably required to obtain the desired denoising effect. Singular value decomposition (SVD) can effectively analyze nonlinear and nonsmooth signals with the advantages of no delay and small phase shift and has been widely used in signal denoising [9], image reconstruction [10], image feature extraction [11], and other areas.…”