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Abstract #4697

Comparison of Quality Assessment Methods for Deep-Learning-Based MR Image Reconstruction

Mohammadhassan Izady Yazdanabady 1,2, Kyoko Fujimoto3, Benjamin Paul Berman3, Matthew S Rosen4,5,6, Neha Koonjoo4,5, Bo Zhu4,5,6, Christian George Graff3, and Aria Pezeshk3

1School of Computing, Informatics, and Decision Systems Engineering, Arizona State Univerisity, Tempe, AZ, United States, 2Department of Neurosurgery, Barrow Neurological Institute, St. Joseph’s Hospital and Medical Center, Phoenix, AZ, United States, 3Center for Devices and Radiological Health, US Food and Drug Administration, Silver Spring, MD, United States, 4A. A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, United States, 5Harvard Medical School, Boston, MA, United States, 6Department of Physics, Harvard University, Cambridge, MA, United States

The proper methodology to perform rigorous quantitative task-based assessment of image quality for deep learning based MR reconstruction methods has not been devised yet. In this study we reconstructed T1-weighted brain images using neural networks trained with five different datasets, and explored the consistency and relationship between rankings of image quality using three different assessment metrics and FreeSurfer-based quantitative analysis. Our study indicates that assessment of image quality for a data-driven reconstruction algorithm may require several types of analysis including using different image quality assessment metrics and their agreement with clinically relevant tasks.

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