Abstract #2061

Evaluating the match of image quality metrics with radiological assessment in a dataset with and without motion artifacts

Hannah Eichhorn¹, Simon Chemnitz-Thomsen^1,2, Evangelos Vouros^1,3, Nitesh Shekhrajka⁴, Robert Frost^5,6, André van der Kouwe^5,7, and Melanie Ganz^1,8

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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