Abstract #1903

An Active Learning platform for automatic MR image quality assessment

Thomas Küstner^1,2, Martin Schwartz^1,2, Annika Kaupp², Petros Martirosian¹, Sergios Gatidis¹, Nina F. Schwenzer¹, Fritz Schick¹, Holger Schmidt¹, and Bin Yang²

¹University Hospital Tübingen, Tübingen, Germany, ²Institute of Signal Processing and System Theory, University of Stuttgart, Stuttgart, Germany

Acquired images are usually analyzed by a human observer (HO) according to a certain diagnostic question. Flexible algorithm parametrization and the enormous amount of data created per patient make this task time-demanding and expensive. Furthermore, definition of objective quality criterion can be very challenging, especially in the context of a missing reference image. In order to support the HO in assessing image quality, we propose a non-reference MR image quality assessment system based on a machine-learning approach with an Active Learning loop to reduce the amount of necessary labeled training data. Labeling is performed via an easy accessible website.

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