Year: 2022
Type: preprint
Abstract: Diffusion-weighted (DW) magnetic resonance spectroscopy (MRS) suffers from a lower signal to noise ratio (SNR) compared to conventional MRS owing to the addition of diffusion attenuation. This technique can therefore strongly benefit from noise reduction strategies. In the present work, the Marchenko-Pastur principal component analysis (MP-PCA) denoising is tested on Monte Carlo simulations and on in vivo DW-MRS data acquired at 9.4T in the rat brain. We provide a descriptive study of the effects observed following different MP-PCA denoising strategies (denoising the entire matrix versus using a sliding window), in terms of apparent SNR, rank selection, noise correlation within and across b-values and quantification of metabolite concentrations and fitted diffusion coefficients. MP-PCA denoising yielded an increased apparent SNR, a more accurate B0 drift correction between shots, and similar estimates of metabolite concentrations and diffusivities compared to the raw data. No spectral residuals on individual shots were observed but correlations in the noise level across shells were introduced, an effect which was mitigated using a sliding window, but which should be carefully considered.
Source: arXiv (Cornell University)
Language: English
Subfield: Radiology, Nuclear Medicine and Imaging
Field: Medicine
Domain: Health Sciences
Open Access status: green
Awards: Translational Non-Invasive Metabolic Studies towards Novel Treatments of Chronic Hepatic Encephalopathy in Developing Brain, from 3D Organotypic Brain Cell Cultures to the In vivo Rat and Human Brain, Enhanced MR Spectroscopic mapping of brain regional changes in type C hepatic encephalopathy of juvenile rats to develop novel combinatorial treatments, INtegrating Magnetic Resonance SPectroscopy and Multimodal Imaging for Research and Education in MEDicine, In vivo brain microstructure mapping for clinical neuroimaging, G8811794584
OpenAlex ID: W4223547468