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 Rosen^4,5,6, Neha Koonjoo^4,5, Bo Zhu^4,5,6, 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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