Retrospective Motion Correction of MR Images using Prior-Assisted Deep Learning
This work aims to improve the quality of MRI scans for radiologists by reducing motion artifacts, which is an incremental improvement to existing deep learning methods.
The paper addresses the problem of motion artifacts in MRI by enhancing existing deep learning models with additional image prior information. The proposed approach demonstrated promising results in improving motion correction.
In MRI, motion artefacts are among the most common types of artefacts. They can degrade images and render them unusable for accurate diagnosis. Traditional methods, such as prospective or retrospective motion correction, have been proposed to avoid or alleviate motion artefacts. Recently, several other methods based on deep learning approaches have been proposed to solve this problem. This work proposes to enhance the performance of existing deep learning models by the inclusion of additional information present as image priors. The proposed approach has shown promising results and will be further investigated for clinical validity.