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Abstract #3350

Non-rigid Respiratory Motion Estimation of Coronary MR Angiography using Unsupervised Fully Convolutional Neural Network

Haikun Qi1, Gastao Cruz1, Thomas Kuestner1, Niccolo Fuin1, René Botnar1, and Claudia Prieto1
1School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom

Non-rigid motion corrected coronary MR angiography (CMRA) in combination with 2D image-based navigators has been proposed to account for the complex respiratory-induced motion of the heart in undersampled acquisitions. However, this framework requires the efficient and accurate estimation of non-rigid bin-to-bin motion from undersampled respiratory-resolved images. In this study, we aim to investigate the feasibility of using an unsupervised fully convolutional network to estimate non-rigid motion from undersampled respiratory-resolved CMRA. The performance of the proposed approach was evaluated on 5-fold accelerated free-breathing CMRA and validated against a widely used conventional non-rigid registration method.

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