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A feature-based convolutional neural network for reconstruction of interventional MRI
Blanca Zufiria1,2, Suhao Qiu1, Kang Yan1
1Institute for Medical Imaging Technology, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China.
NMR in Biomedicine
|December 20, 2019
Summary
Deep learning reconstructs interventional MRI images for neurosurgery. A novel feature-based CNN (FbCNN) achieves real-time visualization, improving electrode placement accuracy in deep brain stimulation.
Area of Science:
- Medical Imaging
- Machine Learning
- Neurosurgery
Background:
- Real-time interventional MRI (I-MRI) is crucial for visualizing surgical tools during neurosurgery.
- Accurate electrode placement in deep brain stimulation relies on real-time imaging.
- Challenges exist in reconstructing I-MRI images due to high undersampling and speed requirements.
Purpose of the Study:
- To develop a deep learning method for reconstructing interventional MRI images.
- To enable real-time visualization for improved accuracy in MR-guided neurosurgery.
- To address the challenges of high undersampling rates and fast reconstruction in I-MRI.
Main Methods:
- Proposed a feature-based convolutional neural network (FbCNN) for image reconstruction.
- Utilized golden-angle radially sampled data.
- Employed a two-stage approach: interventional feature reconstruction and feature refinement/postprocessing.
Main Results:
- FbCNN successfully reconstructed interventional features and final images using only five radial spokes.
- Achieved a reconstruction time of approximately 500 ms per frame.
- Demonstrated a high acceleration factor of approximately 80.
Conclusions:
- FbCNN outperforms traditional and other deep learning methods for I-MRI reconstruction.
- The method shows potential for real-time application in MR-guided neurosurgery.
- Enables accurate visualization for procedures like deep brain stimulation electrode placement.
