GEOMETRIC CONSTRAINED DEEP LEARNING FOR MOTION CORRECTION OF FETAL BRAIN MR IMAGES.

Robust motion correction of fetal brain MRI slices is crucial for 3D brain volume reconstruction. However, conventional methods can only handle a limited range of motion. Hence, a deep learning model based on geometric constraints is proposed in order to predict the arbitrary motion of fetal brain MRI slices in a standard anatomical space, which consists of a global motion estimation network and a relative motion estimation network. In particular, the relative motion estimation network is used to estimate the relative motion between two adjacent slices, which is exploited as a geometric constraint. Then, sharing features between two networks make the model to learn more unique feature representations for global motion correction, and a weight-learnable strategy is employed to balance the contributions of two networks. With this design, the proposed method can estimate more complicated and large motions. Moreover, to build a large simulated fetal brain stack dataset with realistic appearance for successfully training a robust motion correction model, we introduced a control point-based method to simulate fetal motion trajectories at different gestational ages, between stacks and within 2D slices. The experimental results on a large number of fetal brain stacks demonstrate the state-of-the-art performance of our method.