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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