  Motion artefacts in magnetic resonance brain images can have a strong impact
on diagnostic confidence. The assessment of MR image quality is fundamental
before proceeding with the clinical diagnosis. Motion artefacts can alter the
delineation of structures such as the brain, lesions or tumours and may require
a repeat scan. Otherwise, an inaccurate (e.g. correct pathology but wrong
severity) or incorrect diagnosis (e.g. wrong pathology) may occur.
"\textit{Image quality assessment}" as a fast, automated step right after
scanning can assist in deciding if the acquired images are diagnostically
sufficient. An automated image quality assessment based on the structural
similarity index (SSIM) regression through a residual neural network is
proposed in this work. Additionally, a classification into different groups -
by subdividing with SSIM ranges - is evaluated. Importantly, this method
predicts SSIM values of an input image in the absence of a reference ground
truth image. The networks were able to detect motion artefacts, and the best
performance for the regression and classification task has always been achieved
with ResNet-18 with contrast augmentation. The mean and standard deviation of
residuals' distribution were $\mu=-0.0009$ and $\sigma=0.0139$, respectively.
Whilst for the classification task in 3, 5 and 10 classes, the best accuracies
were 97, 95 and 89\%, respectively. The results show that the proposed method
could be a tool for supporting neuro-radiologists and radiographers in
evaluating image quality quickly.

Image and Video Processing;Computer Vision and Pattern Recognition;Machine Learning;Medical Physics