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Related Experiment Video

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Author Spotlight: Optimized Lung MRI Protocol with Computationally Efficient Reconstruction Methods

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Undersampling artifact reduction for free-breathing 3D stack-of-radial MRI based on a deep adversarial learning

Chang Gao¹, Vahid Ghodrati¹, Shu-Fu Shih²

¹Department of Radiological Sciences, University of California Los Angeles, Los Angeles, CA, United States; Inter-Departmental Graduate Program of Physics and Biology in Medicine, University of California Los Angeles, Los Angeles, CA, United States.

Magnetic Resonance Imaging

|October 21, 2022

Summary

A novel 3D generative adversarial network (GAN) effectively reduces streaking artifacts in undersampled stack-of-radial MRI scans, preserving crucial image sharpness for abdominal imaging.

Keywords:

Abdominal MRI Artifact suppression Deep adversarial network Deep learning Radial acquisition

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Area of Science:

Medical Imaging
Artificial Intelligence
Radiology

Background:

Stack-of-radial MRI enables free-breathing abdominal scans but suffers from long acquisition times.
Undersampling accelerates scans but introduces streaking artifacts and degrades image quality.

Purpose of the Study:

To develop and evaluate a deep learning network for reducing streaking artifacts in undersampled stack-of-radial MRI.
To assess the network's ability to preserve perceptual image sharpness.

Main Methods:

A 3D generative adversarial network (GAN) was developed using adversarial, mean-squared-error, and structural similarity index loss functions.
Training data was augmented by gating to five respiratory states to mitigate motion artifacts.
The GAN's performance was evaluated on 3-5x undersampled data from two institutions, comparing it against a 3D U-Net.

Main Results:

The 3D GAN achieved significantly higher SSIM (0.841 vs. 0.798) compared to the 3D U-Net.
Region of interest analysis confirmed effective streak removal without altering tissue characteristics.
Radiologist scores indicated a significant improvement in artifact reduction (1.6 points on a 4-point scale) with no compromise in sharpness.

Conclusions:

The developed 3D GAN effectively removes streaking artifacts from undersampled stack-of-radial abdominal MRI.
This deep learning approach successfully preserves perceptual image details, enhancing diagnostic quality.