  Accurately estimating and correcting the motion artifacts are crucial for 3D
image reconstruction of the abdominal and in-utero magnetic resonance imaging
(MRI). The state-of-art methods are based on slice-to-volume registration (SVR)
where multiple 2D image stacks are acquired in three orthogonal orientations.
In this work, we present a novel reconstruction pipeline that only needs one
orientation of 2D MRI scans and can reconstruct the full high-resolution image
without masking or registration steps. The framework consists of two main
stages: the respiratory motion estimation using a self-supervised recurrent
neural network, which learns the respiratory signals that are naturally
embedded in the asymmetry relationship of the neighborhood slices and cluster
them according to a respiratory state. Then, we train a 3D deconvolutional
network for super-resolution (SR) reconstruction of the sparsely selected 2D
images using integrated reconstruction and total variation loss. We evaluate
the classification accuracy on 5 simulated images and compare our results with
the SVR method in adult abdominal and in-utero MRI scans. The results show that
the proposed pipeline can accurately estimate the respiratory state and
reconstruct 4D SR volumes with better or similar performance to the 3D SVR
pipeline with less than 20\% sparsely selected slices. The method has great
potential to transform the 4D abdominal and in-utero MRI in clinical practice.
