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.

该论文提供了对 MRI 中胎儿运动校正的最新进展的全面回顾，涵盖了各种解决这些挑战的现代方法和技术进步，包括传统的 3D 胎儿 MRI 校正方法（如切片到体积注册），基于深度学习的技术（如卷积神经网络，长短期记忆网络，变形器，生成对抗网络）和最新的扩散模型。通过这次文献综述得出的见解反映出对胎儿 MRI 研究中技术细节和实际影响的深入了解，为该领域的潜在解决方案和未来改进提供了合理的观点。