Automated SSIM Regression for Detection and Quantification of Motion Artefacts in Brain MR Images

• URL: http://arxiv.org/abs/2206.06725v1
• Date: Tue, 14 Jun 2022 10:16:54 GMT
• Title: Automated SSIM Regression for Detection and Quantification of Motion Artefacts in Brain MR Images
• Authors: Alessandro Sciarra, Soumick Chatterjee, Max D\"unnwald, Giuseppe Placidi, Andreas N\"urnberger, Oliver Speck and Steffen Oeltze-Jafra
• Abstract summary: Motion artefacts in magnetic resonance brain images are a crucial issue. The assessment of MR image quality is fundamental before proceeding with the clinical diagnosis. An automated image quality assessment based on the structural similarity index (SSIM) regression has been proposed here.
• Score: 54.739076152240024
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Motion artefacts in magnetic resonance brain images are a crucial issue. The assessment of MR image quality is fundamental before proceeding with the clinical diagnosis. If the motion artefacts alter a correct delineation of structure and substructures of the brain, lesions, tumours and so on, the patients need to be re-scanned. Otherwise, neuro-radiologists could report an inaccurate or incorrect diagnosis. The first step right after scanning a patient is the "\textit{image quality assessment}" in order to decide if the acquired images are diagnostically acceptable. An automated image quality assessment based on the structural similarity index (SSIM) regression through a residual neural network has been proposed here, with the possibility to perform also the classification in different groups - by subdividing with SSIM ranges. 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. 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 obtained results show that the proposed method could be a tool in supporting neuro-radiologists and radiographers in evaluating the image quality before the diagnosis.

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