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Magn Reson Med. 2019 Feb 5. doi: 10.1002/mrm.27658. [Epub ahead of print]

Evaluation of principal component analysis image denoising on multi-exponential MRI relaxometry.

Author information

1
Department of Biomedical Engineering, Vanderbilt University, Nashville, Tennessee.
2
Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, Tennessee.
3
Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, Tennessee.
4
Department of Electrical Engineering, Vanderbilt University, Nashville, Tennessee.
5
Center of Functionally Integrative Neuroscience, Aarhus University Hospital, Aarhus, Denmark.
6
Department of Physics and Astronomy, Aarhus University, Aarhus, Denmark.
7
Champalimaud Centre for the Unknown, Lisbon, Portugal.

Abstract

PURPOSE:

Multi-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.

METHODS:

PCA 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.

RESULTS:

Denoising 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.

CONCLUSION:

Experimental 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.

KEYWORDS:

MRI; denoising; multi-exponential; myelin; principal component analysis; relaxometry

PMID:
30720206
DOI:
10.1002/mrm.27658

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