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Motion Modelling and Analysis Group

School of Biomedical Engineering and Imaging Sciences,

King's College London


Motion-Corrected MR Image Reconstruction

(Funded by EPSRC grant EP/P001009/1)

Motion, such as that caused by breathing or the heart beating, can cause artefacts in reconstructed MR images. In this work, we developed techniques to detect these artefacts and even correct for them during the reconstruction process. This was made possible by exploiting deep learning methods. In our initial work (Oksuz et al, Proc MICCAI MLMIR) we trained a variant of the AUTOMAP reconstruction network to perform motion artefact correction. Subsequently, we developed separate artefact detection and correction networks in a cascaded architecture and traned them in an end-to-end fashion (Oksuz et al, Proc MICCAI).

Motion artefact detection and correction in cine MR reconstruction
Detection and correction of MR artefacts in cine MR reconstruction.


In later work, we extended the motion artefact detection and correction network to also perform image segmentation. This resulted in a deep learning based solution that could both reconstruct and segment cine MR data, whilst accounting for the possible presence of corruption in the data due to motion and mistriggering. The model had state-of-the-art performance in the presence of corrupted data as well as when presented with uncorrupted data.

Motion artefact detection, correction and segmentation of cine MR
Motion artefact detection, correction and segmentation of cine MR.