  This paper provides a comprehensive review of the latest advancements in
fetal motion correction in MRI. We delve into various contemporary
methodologies and technological advancements aimed at overcoming these
challenges. It includes traditional 3D fetal MRI correction methods like Slice
to Volume Registration (SVR), deep learning-based techniques such as
Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM) Networks,
Transformers, Generative Adversarial Networks (GANs) and most recent
advancements of Diffusion Models. The insights derived from this literature
review reflect a thorough understanding of both the technical intricacies and
practical implications of fetal motion in MRI studies, offering a reasoned
perspective on potential solutions and future improvements in this field.
