Abstract #2061
Evaluating the match of image quality metrics with radiological assessment in a dataset with and without motion artifacts
Hannah Eichhorn1, Simon Chemnitz-Thomsen1,2, Evangelos Vouros1,3, Nitesh Shekhrajka4, Robert Frost5,6, André van der Kouwe5,7, and Melanie Ganz1,8
1Neurobiology Research Unit, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark, 2Department of Mathematical Sciences, University of Copenhagen, Copenhagen, Denmark, 3Niels Bohr Institute, University of Copenhagen, Copenhagen, Denmark, 4Department of Radiology, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark, 5Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, United States, 6Department of Radiology, Harvard Medial School, Boston, MA, United States, 7Department of Radiology, Harvard Medical School, Boston, MA, United States, 8Department of Computer Science, University of Copenhagen, Copenhagen, Denmark
Synopsis
Currently no image quality metric, used for evaluating the performance of artifact correction or image reconstruction methods, is sensitive to all possible image artifacts. This complicates the choice of a proper quantitative quality measure. To provide assistance with this choice, we investigated the correlation of commonly used metrics with radiological evaluation on a dataset acquired with and without motion. The full-reference metrics SSIM and PSNR correlated most strongly with observer scores. Among the reference-free metrics Image Entropy, Average Edge Strength and Tenengrad measure showed a consistent correlation for all investigated sequences.
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