Prenatal MRI of fetal brain is vulnerable to unpredictable fetal motion and maternal movement. The conventional registration-based motion correction methods sometimes fail in excessive motion. In this work, we proposed a learning-based scheme to estimate fetal brain motion using a deep recursive framework, which replicated the iterative slice-to-volume registration and 3D volumetric reconstruction process. The network outperformed the previous learning-based methods and with good computational efficiency compared to traditional method. It also achieved high super-resolution reconstruction accuracy on simulated motion-corrupted slices, and therefore, is promising for fetal brain MRI analysis.
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