Deep Learning using K-space Based Data Augmentation for Automated Cardiac MR Motion Artefact Detection
This addresses the need for automated quality assessment in large-scale medical imaging studies like the UK Biobank, reducing manual effort, but it is incremental as it builds on existing methods with a novel augmentation approach.
The paper tackles the problem of automatically detecting motion-related artefacts in cardiac MR images, achieving high accuracy, precision, and recall on a dataset of 3465 images from the UK Biobank, with detection in less than 1ms.
Quality assessment of medical images is essential for complete automation of image processing pipelines. For large population studies such as the UK Biobank, artefacts such as those caused by heart motion are problematic and manual identification is tedious and time-consuming. Therefore, there is an urgent need for automatic image quality assessment techniques. In this paper, we propose a method to automatically detect the presence of motion-related artefacts in cardiac magnetic resonance (CMR) images. As this is a highly imbalanced classification problem (due to the high number of good quality images compared to the low number of images with motion artefacts), we propose a novel k-space based training data augmentation approach in order to address this problem. Our method is based on 3D spatio-temporal Convolutional Neural Networks, and is able to detect 2D+time short axis images with motion artefacts in less than 1ms. We test our algorithm on a subset of the UK Biobank dataset consisting of 3465 CMR images and achieve not only high accuracy in detection of motion artefacts, but also high precision and recall. We compare our approach to a range of state-of-the-art quality assessment methods.