Demonstrates k-space simulation of respiratory motion artifacts for cardiac MRI quality assessment, highlighting improved deep learning classification under data scarcity.
To tackle data scarcity for training a deep-learning algorithm for cardiac MR image quality assessment, we develop a k-space method for simulating respiratory motion artifacts with different levels of severity on artifact-free publicly available cardiac MRI data. The benefit of such simulated data is investigated, demonstrating the usefulness of training a feature extractor with the simulated artifacts for image quality classification. Our proposed method achieved the test accuracy of 0.625 and Cohen's Kappa of 0.473 (n=120 images), ranking third in task one for the CMRxMotion challenge of MICCAI 2022.
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Amirrajab et al. (2024) studied this question.
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