MRI k-Space Motion Artefact Augmentation: Model Robustness and Task-Specific Uncertainty

Patient movement during the acquisition of magnetic resonance images (MRI) can cause unwanted image artefacts. These artefacts may affect the quality of diagnosis by clinicians and cause errors in automated image analysis. In this work, we present a method for generating realistic motion artefacts from artefact-free data to be used in deep learning frameworks to increase training appearance variability and ultimately make machine learning algorithms such as convolutional neural networks (CNNs) robust to the presence of motion artefacts. We model patient movement as a sequence of randomly-generated, 'de-meaned', rigid 3D affine transforms which, by resampling artefact-free volumes, are then combined in k-space to generate realistic motion artefacts. We show that by augmenting the training of semantic segmentation CNNs with artefacted data, we can train models that generalise better and perform more reliably in the presence of artefacted data, with negligible cost to their performance on artefact-free data. We show that the performance of models trained using artefacted data on segmentation tasks on real-world test-retest image pairs is more robust. Finally, we demonstrate that measures of uncertainty obtained from motion augmented models reflect the presence of artefacts and can thus provide relevant information to ensure the safe usage of deep learning extracted biomarkers in clinics.