Keywords: Image Reconstruction, BrainRobust motion correction of fetal brain MRI slices is crucial for fetal brain volume reconstruction. However, conventional methods can only handle a limited range of motion. Hence, a deep learning model based on prior geometric constraints is proposed to predict the motion of 2D slices. It consists of a global and a relative motion estimation network. Sharing features between two networks make the model to learn more unique feature representations for global motion correction. Moreover, we present a control point-based approach to simulate complex fetal motion trajectories. The experimental results demonstrate that the proposed method is effective and efficient.
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