# A Literature Review on Fetus Brain Motion Correction in MRI

arXiv: https://arxiv.org/abs/2401.16782 · HTML: https://mlsanity.com/paper/2401.16782/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 40 references that are in the map.
- First posted on arXiv 30 Jan 2024.

## Builds on (most cited first, 14 of 14)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [Score-Based Generative Modeling through Stochastic Differential Equations](https://mlsanity.com/paper/2011.13456/) (2020)
- [Image-to-Image Translation with Conditional Adversarial Networks](https://mlsanity.com/paper/1611.07004/) (2016)
- [Generative Modeling by Estimating Gradients of the Data Distribution](https://mlsanity.com/paper/1907.05600/) (2019)
- [Measurement-conditioned Denoising Diffusion Probabilistic Model for Under-sampled Medical Image Reconstruction](https://mlsanity.com/paper/2203.03623/) (2022)
- [Real-time Deep Pose Estimation with Geodesic Loss for Image-to-Template Rigid Registration](https://mlsanity.com/paper/1803.05982/) (2018)
- [SVoRT: Iterative Transformer for Slice-to-Volume Registration in Fetal Brain MRI](https://mlsanity.com/paper/2206.10802/) (2022)
- [Slice-to-volume medical image registration: a survey](https://mlsanity.com/paper/1702.01636/) (2017)
- [3D Reconstruction in Canonical Co-ordinate Space from Arbitrarily Oriented 2D Images](https://mlsanity.com/paper/1709.06341/) (2017)
- [Predicting Slice-to-Volume Transformation in Presence of Arbitrary Subject Motion](https://mlsanity.com/paper/1702.08891/) (2017)
- [Deep Predictive Motion Tracking in Magnetic Resonance Imaging: Application to Fetal Imaging](https://mlsanity.com/paper/1909.11625/) (2019)
- [Motion Correction and Volumetric Reconstruction for Fetal Functional Magnetic Resonance Imaging Data](https://mlsanity.com/paper/2202.05863/) (2022)
- [MoCoNet: Motion Correction in 3D MPRAGE images using a Convolutional Neural Network approach](https://mlsanity.com/paper/1807.10831/) (2018)

## Abstract (arXiv)

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.