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

06/14/2022
by   Alessandro Sciarra, et al.
0

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 "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 μ=-0.0009 and σ=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.

READ FULL TEXT

page 1

page 2

page 3

page 5

page 6

page 8

research
06/25/2018

A Machine-learning framework for automatic reference-free quality assessment in MRI

Magnetic resonance (MR) imaging offers a wide variety of imaging techniq...
research
08/15/2018

Deep Learning using K-space Based Data Augmentation for Automated Cardiac MR Motion Artefact Detection

Quality assessment of medical images is essential for complete automatio...
research
09/20/2022

Detecting respiratory motion artefacts for cardiovascular MRIs to ensure high-quality segmentation

While machine learning approaches perform well on their training domain,...
research
08/13/2017

Image Quality Assessment Guided Deep Neural Networks Training

For many computer vision problems, the deep neural networks are trained ...
research
08/08/2019

A Multimodal Deep Network for the Reconstruction of T2W MR Images

Multiple sclerosis is one of the most common chronic neurological diseas...
research
09/10/2022

Explainable Image Quality Assessments in Teledermatological Photography

Image quality is a crucial factor in the success of teledermatological c...

Please sign up or login with your details

Forgot password? Click here to reset