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Abstract #4770

Crowdsourced Quality Metrics for Image Reconstruction using Machine Learned Ranking

Kevin M Johnson1,2, Laura Eisenmenger3, Patrick Turski2, and Leonardo Rivera-Rivera1

1Medical Physics, University of Wisconsin - Madison, Madison, WI, United States, 2Radiology, University of Wisconsin - Madison, Madison, WI, United States, 3Radiology, University of California - San Francisco, San Francisco, CA, United States

In this work, we investigate a scheme for crowd sourcing image quality using machine learned metrics from user rankings of corrupted images. Using an HTML application, experienced observers ranked pairs of corrupted images with respect to image quality. A convolution neural network (CNN) was then trained to produce a quality score that was higher in the preferred images. The trained CNN was found to be more sensitive to artifacts from image blurring and wavelet compression than mean square error. Finally, preliminary use in training a machine learned image reconstruction is demonstrated.

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