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Three-Dimensional Phase Resolved Functional Lung Magnetic Resonance Imaging

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Jointly Learned 3D Non-Cartesian Sampling With Wave Encoding and Reconstruction for Neurovascular Phase Contrast MRI.

Chenwei Tang^1,2, Brock W Jolicoeur^2,3, James Rice^2,4

¹Department of Medical Physics, University of Wisconsin, Madison, Wisconsin, USA.

Magnetic Resonance in Medicine

|December 8, 2025

Summary

This summary is machine-generated.

Accelerated 3D phase contrast (PC) MRI using learned wave encoding and model-based reconstruction (MoDL) significantly improves image quality and provides accurate flow measurements. This novel approach enables faster scans with comparable or better results than traditional methods.

Keywords:

deep learning non‐cartesian sampling optimization phase contrast wave encoding

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Area of Science:

Medical Imaging
Magnetic Resonance Imaging
Fluid Dynamics

Background:

Accelerating Magnetic Resonance Imaging (MRI) scans is crucial for reducing motion artifacts and improving patient comfort.
3D Phase Contrast (PC) MRI is essential for quantifying blood flow, but traditional methods are time-consuming.
Wave encoding offers a promising strategy for accelerating MRI acquisition.

Purpose of the Study:

To develop and validate an accelerated 3D PC MRI technique using jointly learned wave encoding and model-based reconstruction (MoDL).
To compare the performance of the novel accelerated technique against conventional methods in terms of speed and accuracy.

Main Methods:

A simulation framework and pseudo-fully sampled neurovascular 4D flow data were used to learn optimal wave encoding parameters and a MoDL network.
Prospective 3D PC MRI scans were acquired in a flow phantom and 12 healthy participants using the learned wave encoding and a standard MoDL reconstruction.
Acquisitions included the accelerated wave-encoded scan (2.25 min), a time-matched non-wave-encoded scan, a time-matched 3D radial scan, and a reference 3D radial scan (5.65 min).

Main Results:

Learned wave scans in a phantom showed accurate flow rates and high correlation with reference scans (R² = 0.99).
In vivo, wave-encoded scans exhibited reduced aliasing and blurring, with improved small vessel conspicuity compared to non-wave-encoded and time-matched radial scans.
Quantitative flow analysis demonstrated similar or improved accuracy (coefficient of variation and intraclass correlation) for the accelerated wave scans compared to the reference scan.

Conclusions:

Jointly learned wave encoding and MoDL reconstruction represent a feasible method for accelerating 3D PC MRI.
The developed technique significantly enhances image quality and ensures accurate hemodynamic measurements.
This acceleration holds potential for routine clinical application in neurovascular flow assessment.