  Magnetic resonance (MR) imaging offers a wide variety of imaging techniques.
A large amount of data is created per examination which needs to be checked for
sufficient quality in order to derive a meaningful diagnosis. This is a manual
process and therefore time- and cost-intensive. Any imaging artifacts
originating from scanner hardware, signal processing or induced by the patient
may reduce the image quality and complicate the diagnosis or any image
post-processing. Therefore, the assessment or the ensurance of sufficient image
quality in an automated manner is of high interest. Usually no reference image
is available or difficult to define. Therefore, classical reference-based
approaches are not applicable. Model observers mimicking the human observers
(HO) can assist in this task. Thus, we propose a new machine-learning-based
reference-free MR image quality assessment framework which is trained on
HO-derived labels to assess MR image quality immediately after each
acquisition. We include the concept of active learning and present an efficient
blinded reading platform to reduce the effort in the HO labeling procedure.
Derived image features and the applied classifiers (support-vector-machine,
deep neural network) are investigated for a cohort of 250 patients. The MR
image quality assessment framework can achieve a high test accuracy of 93.7$\%$
for estimating quality classes on a 5-point Likert-scale. The proposed MR image
quality assessment framework is able to provide an accurate and efficient
quality estimation which can be used as a prospective quality assurance
including automatic acquisition adaptation or guided MR scanner operation,
and/or as a retrospective quality assessment including support of diagnostic
decisions or quality control in cohort studies.
