ArticleImaging neuroscience (Cambridge, Mass.)2023
Efficient PCA denoising of spatially correlated redundant MRI data.
Article in Imaging neuroscience (Cambridge, Mass.), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed.
- Offline Reconstruction of Diffusion MRI Acquisitions for Comparison Between Complex PCA-Based and AI-Based Denoising.Magnetic resonance in medicine · 2026Article
- Axon Diameter Mapping From Myelin Water Diffusion MRI.IEEE transactions on medical imaging · 2026Article
- Thermal noise lowers the accuracy of rotationally invariant harmonics of diffusion MRI data and their robustness to experimental variations.Magnetic resonance in medicine · 2026Article
- White matter microstructure is differentially impacted by cerebral amyloid angiopathy, neurofibrillary tangles, and neuritic plaque co-pathology.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2025Article
- Improved DTI-Based Skeletal Muscle Architecture Estimation via Diffusion Weighted Image Denoising and First Eigenvector Map Smoothing.NMR in biomedicine · 2025Article
- Robust-tedana: An automated denoising pipeline for multi-echo fMRI data.bioRxiv : the preprint server for biology · 2025Article
- Considerations and recommendations from the ISMRM diffusion study group for preclinical diffusion MRI: Part 1: In vivo small-animal imaging.Magnetic resonance in medicine · 2025Review
- Romer-EPTI: Rotating-view motion-robust super-resolution EPTI for SNR-efficient distortion-free in-vivo mesoscale diffusion MRI and microstructure imaging.Magnetic resonance in medicine · 2025Article
- Deuterium metabolic imaging phenotypes mouse glioblastoma heterogeneity through glucose turnover kinetics.eLife · 2025Article
- Denoising Improves Cross-Scanner and Cross-Protocol Test-Retest Reproducibility of Diffusion Tensor and Kurtosis Imaging.Human brain mapping · 2025Article
- High-resolution perfusion imaging in rodents using pCASL at 9.4 T.NMR in biomedicine · 2025Article
- Human gray matter microstructure mapped using neurite exchange imaging (NEXI) on a clinical scanner.Imaging neuroscience (Cambridge, Mass.) · 2025Article
- Interplay between MRI-based axon diameter and myelination estimates in macaque and human brain.Imaging neuroscience (Cambridge, Mass.) · 2025Article
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- Self-supervised learning for denoising of multidimensional MRI data.Magnetic resonance in medicine · 2024Article
- NOise Reduction with DIstribution Corrected (NORDIC) principal component analysis improves brain activity detection across rodent and human functional MRI contexts.Imaging neuroscience (Cambridge, Mass.) · 2024Article
- Romer-EPTI: rotating-view motion-robust super-resolution EPTI for SNR-efficient distortion-free in-vivo mesoscale dMRI and microstructure imaging.bioRxiv : the preprint server for biology · 2024Article
- Quantifying human gray matter microstructure using neurite exchange imaging (NEXI) and 300 mT/m gradients.Imaging neuroscience (Cambridge, Mass.) · 2024Article
- Enhancement of intra-cardiac flow-field data using adaptive Kernel filtering.Scientific reports · 2023Article
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Authors and funding
6 authors.
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Abstract
Marčenko-Pastur PCA (MPPCA) denoising is emerging as an effective means for noise suppression in MR imaging (MRI) acquisitions with redundant dimensions. However, MPPCA performance can be severely compromised by spatially correlated noise-an issue typically affecting most modern MRI acquisitions-almost to the point of returning the original images with little or no noise removal. In this study, we explore different threshold criteria for principal component analysis (PCA) component classification that enable efficient and robust denoising of MRI data even when noise exhibits high spatial correlations, especially in cases where data are acquired with Partial Fourier and when only magnitude data are available. We show that efficient denoising can be achieved by incorporating a-priori information about the noise variance into PCA denoising thresholding. Based on this, two denoising strategies developed here are: 1) General PCA (GPCA) denoising that uses a-priori noise variance estimates without assuming specific noise distributions; and 2) Threshold PCA (TPCA) denoising which removes noise components with a threshold computed from a-priori estimated noise variance to determine the upper bound of the Marčenko-Pastur (MP) distribution. These strategies were tested in simulations with known ground truth and applied for denoising diffusion MRI data acquired using pre-clinical (16.4T) and clinical (3T) MRI scanners. In synthetic phantoms, MPPCA denoising failed to denoise spatially correlated data, while GPCA and TPCA better classified components as dominated by signal/noise. In cases where the noise variance was not accurately estimated (as can be the case in many practical scenarios), TPCA still provides excellent denoising performance. Our experiments in pre-clinical diffusion data with highly corrupted by spatial correlated noise revealed that both GPCA and TPCA robustly denoised the data while MPPCA denoising failed. In
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