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

## TL;DR

This study demonstrates that PCA denoising significantly improves the accuracy of multi-exponential MRI relaxometry measurements, enabling better tissue characterization without compromising image resolution.

## Contribution

The paper evaluates PCA denoising for MRI relaxometry, showing it reduces parameter estimation error and enhances measurement precision in both simulated and experimental data.

## Key findings

- Denoising reduces parameter RMSE by 2-4x.
- Images and parameter maps retain resolution with minimal artifacts.
- PCA denoising broadens the applicability of relaxometry techniques.

## 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 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.

---
Source: https://tomesphere.com/paper/1903.08140