  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 2 to 4x, 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.
