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A Literature Review on Fetus Brain Motion Correction in MRI

arXiv 2401.16782 ↗First posted 30 Jan 2024eess.IV cs.LGcited by 0

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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.

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References · 14

  1. 2017Attention Is All You Needcited by 60,361
  2. 2020Denoising Diffusion Probabilistic Modelscited by 21,076
  3. 2020Score-Based Generative Modeling through Stochastic Differential Equationscited by 9,122
  4. 2016Image-to-Image Translation with Conditional Adversarial Networkscited by 4,527
  5. 2019Generative Modeling by Estimating Gradients of the Data Distributioncited by 4,101
  6. 2022Measurement-conditioned Denoising Diffusion Probabilistic Model for Under-sampled Medical Image Reconstructioncited by 65
  7. 2018Real-time Deep Pose Estimation with Geodesic Loss for Image-to-Template Rigid Registrationcited by 40
  8. 2022SVoRT: Iterative Transformer for Slice-to-Volume Registration in Fetal Brain MRIcited by 26
  9. 2017Slice-to-volume medical image registration: a surveycited by 21
  10. 20173D Reconstruction in Canonical Co-ordinate Space from Arbitrarily Oriented 2D Imagescited by 15
  11. 2017Predicting Slice-to-Volume Transformation in Presence of Arbitrary Subject Motioncited by 10
  12. 2019Deep Predictive Motion Tracking in Magnetic Resonance Imaging: Application to Fetal Imagingcited by 10
2 more
  1. 2022Motion Correction and Volumetric Reconstruction for Fetal Functional Magnetic Resonance Imaging Datacited by 4
  2. 2018MoCoNet: Motion Correction in 3D MPRAGE images using a Convolutional Neural Network approachcited by 3

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  1. 2024FetalDiffusion: Pose-Controllable 3D Fetal MRI Synthesis with Conditional Diffusion Model
  2. 2023Self-supervised Fetal MRI 3D Reconstruction Based on Radiation Diffusion Generation Model
  3. 2022Accelerated Motion Correction with Deep Generative Diffusion Models
  4. 2024A Generative Diffusion Model to Solve Inverse Problems for Robust in-NICU Neonatal MRI
  5. 2025Pathological MRI Segmentation by Synthetic Pathological Data Generation in Fetuses and Neonates
  6. 2025DIMA: DIffusing Motion Artifacts for unsupervised correction in brain MRI images
  7. 2024HAITCH: A Framework for Distortion and Motion Correction in Fetal Multi-Shell Diffusion-Weighted MRI
  8. 2024Generative Diffusion Model Bootstraps Zero-shot Classification of Fetal Ultrasound Images In Underrepresented African Populations