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Related Experiment Video

Updated: May 28, 2026

Three-Dimensional Phase Resolved Functional Lung Magnetic Resonance Imaging

Three-Dimensional Phase Resolved Functional Lung Magnetic Resonance Imaging

Published on: June 21, 2024

Fast MR elastography via deep learning-based phase interpolation: A technical feasibility study.

Yoshito Ishihara¹, Tomokazu Numano¹, Daiki Ito¹

¹Department of Radiological Sciences, Graduate School of Human Health Sciences, Tokyo Metropolitan University, 7-2-10, Higashiogu, Arakawa-ku, Tokyo 116-8551, Japan.

Magnetic Resonance Imaging

|May 26, 2026

Summary

This summary is machine-generated.

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A novel deep learning method reduces required phase acquisitions in MR elastography (MRE) by up to 50%. This technique maintains clinical accuracy, potentially shortening scan times and improving image quality for MRE procedures.

Area of Science:

Medical Imaging
Artificial Intelligence
Biophysics

Background:

Clinical MR elastography (MRE) requires four vibration phase images, increasing acquisition time and risk of slice misalignment.
Inadequate breath-holding can compromise MRE data quality and accuracy.

Purpose of the Study:

To evaluate the technical feasibility of a deep learning-based method for reducing MRE phase acquisitions.
To assess the potential of interpolating missing vibration phase images using spatiotemporal wave periodicity.

Main Methods:

Developed two deep learning models: a 3-to-1 (25% reduction) and a 2-to-2 (50% reduction) phase acquisition model.
Validated models using phantom experiments and in vivo liver MRE in 13 healthy volunteers.
Evaluated wave images using SSIM/PSNR and elastograms using Bland-Altman analysis/ICC.

Keywords:

Acceleration Deep learning Elastogram MR elastography U-net Wave image

Related Experiment Videos

Last Updated: May 28, 2026

Three-Dimensional Phase Resolved Functional Lung Magnetic Resonance Imaging

Three-Dimensional Phase Resolved Functional Lung Magnetic Resonance Imaging

Published on: June 21, 2024

Main Results:

Both models generated high-quality wave images with excellent SSIM and PSNR.
Shear stiffness measurements showed high agreement and excellent ICC (>0.90) in phantoms and in vivo.
In vivo measurements showed no significant differences compared to the conventional 4-phase method (p > 0.05).

Conclusions:

The proposed deep learning method is technically feasible for reducing MRE phase acquisitions.
This approach can potentially reduce acquisitions by up to 50% while maintaining clinically acceptable accuracy.
The method offers a promising alternative to conventional MRE, improving efficiency and potentially reducing motion artifacts.