Improved Diffusion-Weighted Hyperpolarized 129Xe Lung MRI with Patch-Based Higher-Order, Singular Value Decomposition Denoising

Abstract

Rationale and Objectives

Hyperpolarized xenon (¹²⁹Xe) MRI is a noninvasive method to assess pulmonary structure and function. To measure lung microstructure, diffusion-weighted imaging—commonly the apparent diffusion coefficient (ADC)—can be employed to map changes in alveolar-airspace size resulting from normal aging and pulmonary disease. However, low signal-to-noise ratio (SNR) decreases ADC measurement certainty, and biases ADC to spuriously low values. Further, these challenges are most severe in regions of the lung where alveolar simplification or emphysematous remodeling generate abnormally high ADCs. Here, we apply Global Local Higher Order Singular Value Decomposition (GLHOSVD) denoising to enhance image SNR, thereby reducing uncertainty and bias in diffusion measurements.

Materials and Methods

GLHOSVD denoising was employed in simulated images and gas phantoms with known diffusion coefficients to validate its effectiveness and optimize parameters for analysis of diffusion-weighted ¹²⁹Xe MRI. GLHOSVD was applied to data from 120 subjects (34 control, 39 cystic fibrosis (CF), 27 lymphangioleiomyomatosis (LAM), and 20 asthma). Image SNR, ADC, and distributed diffusivity coefficient (DDC) were compared before and after denoising using Wilcoxon signed-rank analysis for all images.

Results

Denoising significantly increased SNR in simulated, phantom, and in-vivo images, showing a greater than 2-fold increase (p < 0.001) across diffusion-weighted images. Although mean ADC and DDC remained unchanged (p > 0.05), ADC and DDC standard deviation decreased significantly in denoised images (p < 0.001).

Conclusion

When applied to diffusion-weighted ¹²⁹Xe images, GLHOSVD improved image quality and allowed airspace size to be quantified in high-diffusion regions of the lungs that were previously inaccessible to measurement due to prohibitively low SNR, thus providing insights into disease pathology.