ArticleMagnetic resonance in medicine2019
Evaluation of principal component analysis image denoising on multi-exponential MRI relaxometry.
Article in Magnetic resonance in medicine, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers.
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34 citing papers in PubMed, 88 citations in OpenAlex.
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- Microstructure-Informed Myelin Mapping (MIMM) from routine multi-echo gradient echo data using multiscale physics modeling of iron and myelin effects and QSM.Magnetic resonance in medicine · 2025Article
- Denoising Improves Cross-Scanner and Cross-Protocol Test-Retest Reproducibility of Diffusion Tensor and Kurtosis Imaging.Human brain mapping · 2025Article
- High-resolution quantitative TImaging neuroscience (Cambridge, Mass.) · 2025Article
- Spectral principal axis system (SPAS) and tuning of tensor-valued encoding for microscopic anisotropy and time-dependent diffusion in the rat brain.Imaging neuroscience (Cambridge, Mass.) · 2025Article
- High-resolution perfusion imaging in rodents using pCASL at 9.4 T.NMR in biomedicine · 2025Article
- Interplay between MRI-based axon diameter and myelination estimates in macaque and human brain.Imaging neuroscience (Cambridge, Mass.) · 2025Article
- 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
- In vivo disentanglement of diffusion frequency-dependence, tensor shape, and relaxation using multidimensional MRI.Human brain mapping · 2024Article
- Improved myelin water imaging using BImaging neuroscience (Cambridge, Mass.) · 2024Article
- A comprehensive protocol for quantitative magnetic resonance imaging of the brain at 3 Tesla.PloS one · 2024Article
- Locally low-rank denoising in transform domains.bioRxiv : the preprint server for biology · 2023Article
- The CALIPR framework for highly accelerated myelin water imaging with improved precision and sensitivity.Science advances · 2023Article
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Authors and funding
7 authors at 5 institutions in 3 countries.
Funding
Abstract
purposeMulti-exponential relaxometry is a powerful tool for characterizing tissue, but generally requires high image signal-to-noise ratio (SNR). This work evaluates the use of principal-component-analysis (PCA) denoising to mitigate these SNR demands and improve the precision of relaxometry measures.
methodsPCA denoising was evaluated using both simulated and experimental MRI data. Bi-exponential transverse relaxation signals were simulated for a wide range of acquisition and sample parameters, and experimental data were acquired from three excised and fixed mouse brains. In both cases, standard relaxometry analysis was performed on both original and denoised image data, and resulting estimated signal parameters were compared.
resultsDenoising reduced the root-mean-square-error of parameters estimated from multi-exponential relaxometry by factors of ≈3×, for typical acquisition and sample parameters. Denoised images and subsequent parameter maps showed little or no signs of spatial artifact or loss of resolution.
conclusionExperimental studies and simulations demonstrate that PCA denoising of MRI relaxometry data is an effective method of improving parameter precision without sacrificing image resolution. This simple yet important processing step thus paves the way for broader applicability of multi-exponential MRI relaxometry.
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