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Retrospective correction of motion-affected MR images using deep learning frameworks.
Thomas Küstner1,2,3, Karim Armanious1,2, Jiahuan Yang1
1Department for Signal Processing and System Theory, University of Stuttgart, Stuttgart, Germany.
Magnetic Resonance in Medicine
|May 14, 2019
Summary
Deep learning effectively corrects MRI motion artifacts retrospectively without prior knowledge. Generative adversarial networks produce near-realistic, motion-free images, enhancing diagnostic accuracy.
Area of Science:
- Medical Imaging
- Artificial Intelligence
- Machine Learning
Background:
- Motion artifacts in Magnetic Resonance Imaging (MRI) significantly degrade image quality and diagnostic accuracy.
- Existing motion correction methods often require prospective application with prior motion knowledge, posing challenges for retrospective correction.
- Retrospective motion correction without prior knowledge remains a significant challenge in MRI.
Purpose of the Study:
- To investigate the feasibility of deep learning frameworks for retrospective, reference-free motion correction in MRI.
- To compare the performance of variational autoencoders and generative adversarial networks for image-to-image translation in motion artifact correction.
- To evaluate the influence of motion type and optimal network architecture on correction efficacy.
Main Methods:
- Proposed deep learning models, specifically variational autoencoders and generative adversarial networks, for retrospective motion correction.
- Trained models on pairs of motion-free and motion-affected MRI images for image-to-image translation.
- Conducted blinded subjective image quality assessments and quantitative image similarity analyses to evaluate performance.
Main Results:
- Generative adversarial network (GAN)-based motion correction demonstrated feasibility, producing near-realistic, motion-free images.
- Subjective assessments confirmed the quality of GAN-corrected images.
- Quantitative metrics showed high performance: normalized root mean squared error <0.08, structural similarity index >0.8, and normalized mutual information >0.9.
Conclusions:
- Deep learning enables feasible retrospective restoration of motion artifacts in MRI, yielding near-realistic, motion-free images.
- While promising, the image translation process may alter or obscure anatomical details.
- Further studies are required to evaluate the clinical applicability of this deep learning-based motion correction technique.
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