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Abstract #2951

Respiratory Motion Artifact Simulation for DL application in Cardiac MR Image Quality Assessment

Sina Amirrajab1, Yasmina Al Khalil1, Josien Pluim1, Marcel Breeuwer1,2, and Cian M. Scannell1
1Biomedical Engineering, Eindhoven University of Technology, Eindhoven, Netherlands, 2MR R&D - Clinical Science, Philips Healthcare, Eindhoven, Netherlands

Synopsis

Keywords: Machine Learning/Artificial Intelligence, Artifacts, Respirator Artifact Simulation

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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Keywords