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Deep Predictive Motion Tracking in Magnetic Resonance Imaging: Application to Fetal Imaging
IEEE Transactions on Medical Imaging
|August 4, 2020
Summary
A new deep learning method accurately tracks fetal motion in real-time during MRI scans. This innovation improves image quality and reduces scan times, making fetal magnetic resonance imaging (MRI) more efficient and tolerable.
Area of Science:
- Medical Imaging
- Artificial Intelligence
- Biomedical Engineering
Background:
- Fetal magnetic resonance imaging (MRI) is hindered by significant fetal motion, necessitating manual monitoring and repeated scans.
- Current methods for managing fetal motion are operator-dependent, inefficient, and prolong scan times, impacting patient comfort and resource utilization.
Purpose of the Study:
- To develop a real-time, image-based deep learning method for automatic fetal motion tracking and navigation in MRI.
- To overcome the limitations of manual monitoring and improve the efficiency and quality of fetal MRI.
Main Methods:
- A recurrent neural network with spatial and temporal encoder-decoders was developed to infer fetal motion parameters directly from acquired MRI slices.
- The network was trained and validated on diverse datasets, including varying fetal ages and motion trajectories.
Main Results:
- The proposed deep learning method achieved real-time performance, outperforming alternative estimation and prediction techniques.
- Average errors were 3.5 degrees for motion estimation and 8 degrees for motion prediction tasks.
Conclusions:
- The developed real-time deep predictive motion tracking technique offers a robust solution for assessing fetal movements during MRI.
- This method can enhance fetal MRI by guiding slice acquisitions and enabling advanced navigation systems, improving diagnostic accuracy and patient experience.
