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Related Experiment Video

Updated: Jun 22, 2025

Author Spotlight: Standardization and Best Practices for Advancing Lung Imaging Using 129Xe MRI
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Improved Diffusion-Weighted Hyperpolarized 129Xe Lung MRI with Patch-Based Higher-Order, Singular Value Decomposition

Stephanie A Soderlund1, Abdullah S Bdaiwi2, Joseph W Plummer1

  • 1Center for Pulmonary Imaging Research, Division of Pulmonary Medicine, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio 45229, USA; Department of Biomedical Engineering, University of Cincinnati, Cincinnati, Ohio 45221, Cincinnati, Ohio 45229, USA.

Academic Radiology
|July 3, 2024
PubMed
Summary

Global Local Higher Order Singular Value Decomposition (GLHOSVD) denoising enhances signal-to-noise ratio in hyperpolarized xenon MRI, improving lung microstructure measurements. This method reduces uncertainty and bias in apparent diffusion coefficient (ADC) calculations, especially in diseased lung regions.

Keywords:
DenoisingDiffusion-weighted MRIHyperpolarized XenonPulmonary

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Area of Science:

  • Medical Imaging
  • Pulmonary Medicine
  • Image Processing

Background:

  • Hyperpolarized xenon (129Xe) MRI is a noninvasive tool for assessing lung structure and function.
  • Diffusion-weighted imaging, using the apparent diffusion coefficient (ADC), maps alveolar-airspace size changes.
  • Low signal-to-noise ratio (SNR) in diffusion-weighted 129Xe MRI introduces uncertainty and bias in ADC measurements, particularly in regions with high diffusion.

Purpose of the Study:

  • To apply Global Local Higher Order Singular Value Decomposition (GLHOSVD) denoising to enhance SNR in diffusion-weighted 129Xe MRI.
  • To reduce uncertainty and bias in ADC measurements.
  • To enable quantification of lung microstructure in previously inaccessible high-diffusion regions.

Main Methods:

  • GLHOSVD denoising was validated using simulated images and gas phantoms.
  • The method was applied to diffusion-weighted 129Xe MRI data from 120 subjects (controls, cystic fibrosis, LAM, asthma).
  • Image SNR, ADC, and distributed diffusivity coefficient (DDC) were compared before and after denoising.

Main Results:

  • GLHOSVD significantly increased SNR (over 2-fold, p < 0.001) in simulated, phantom, and in-vivo images.
  • While mean ADC and DDC remained unchanged (p > 0.05), their standard deviations significantly decreased after denoising (p < 0.001).
  • Denoising improved image quality and enabled measurement in high-diffusion lung regions.

Conclusions:

  • GLHOSVD denoising effectively enhances SNR in diffusion-weighted 129Xe MRI.
  • This technique reduces uncertainty and bias in ADC and DDC measurements.
  • GLHOSVD allows for improved quantification of lung microstructure, offering new insights into pulmonary disease pathology.