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2 April 2024 GAN-based motion artifact correction of 3D MR volumes using an image-to-image translation algorithm
Vishnu Vardhan Reddy Kanamata Reddy, Chandan Ganesh Bangalore Yogananda, Nghi C. D. Truong, Ananth J. Madhuranthakam, Joseph A. Maldjian, Baowei Fei
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Proceedings Volume 12930, Medical Imaging 2024: Clinical and Biomedical Imaging; 1293024 (2024) https://doi.org/10.1117/12.3007743
Event: SPIE Medical Imaging, 2024, San Diego, California, United States
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Abstract
The quality of brain MRI volumes is often compromised by motion artifacts arising from intricate respiratory patterns and involuntary head movements, manifesting as blurring and ghosting that markedly degrade imaging quality. In this study, we introduce an innovative approach employing a 3D deep learning framework to restore brain MR volumes afflicted by motion artifacts. The framework integrates a densely connected 3D U-net architecture augmented by generative adversarial network (GAN)-informed training with a novel volumetric reconstruction loss function tailored to 3D GAN to enhance the quality of the volumes. Our methodology is substantiated through comprehensive experimentation involving a diverse set of motion artifact-affected MR volumes. The generated high-quality MR volumes have similar volumetric signatures comparable to motion-free MR volumes after motion correction. This underscores the significant potential of harnessing this 3D deep learning system to aid in the rectification of motion artifacts in brain MR volumes, highlighting a promising avenue for advanced clinical applications.
(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.
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Vishnu Vardhan Reddy Kanamata Reddy, Chandan Ganesh Bangalore Yogananda, Nghi C. D. Truong, Ananth J. Madhuranthakam, Joseph A. Maldjian, and Baowei Fei "GAN-based motion artifact correction of 3D MR volumes using an image-to-image translation algorithm", Proc. SPIE 12930, Medical Imaging 2024: Clinical and Biomedical Imaging, 1293024 (2 April 2024); https://doi.org/10.1117/12.3007743
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KEYWORDS
Motion models
Magnetic resonance imaging
3D modeling
Image processing
Deep learning
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