Reference-less SSIM Regression for Detection and Quantification of Motion Artefacts in Brain MRIs

Alessandro Sciarra, Soumick Chatterjee, Max Dünnwald, Giuseppe Placidi, Andreas Nürnberger, Oliver Speck, Steffen Oeltze-Jafra

Keywords: Motion artefacts, MRI, ResNet, Image quality assessment

TL;DR: This research presents an image quality assessment technique with the help of reference-free SSIM regression and demonstrates its applicability in the presence of motion artefacts

Abstract: Motion artefacts in magnetic resonance images can critically affect diagnosis and the quantification of image degradation due to their presence is required. Usually, image quality assessment is carried out by experts such as radiographers, radiologists and researchers. However, subjective evaluation requires time and is strongly dependent on the experience of the rater. In this work, an automated image quality assessment based on the structural similarity index regression through ResNet models is presented. The results show that the trained models are able to regress the SSIM values with high level of accuracy. When the predicted SSIM values were grouped into 10 classes and compared against the ground-truth motion classes, the best weighted accuracy of 89±2% was observed with RN-18 model, trained with contrast augmentation.

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Paper Type: recently published or submitted journal contributions

Primary Subject Area: Application: Radiology

Secondary Subject Area: Detection and Diagnosis

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