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An image interpolation approach for acquisition time reduction in navigator-based 4D MRI
Neerav Karani1, Lin Zhang1, Christine Tanner1
1Biomedical Image Computing Group, ETH Zurich, Switzerland.
Medical Image Analysis
|March 3, 2019
Summary
This study introduces a novel convolutional neural network (CNN) for temporal interpolation of navigator slices in 4D MR imaging. This method reduces scan times by predicting motion fields, preserving image quality and potentially speeding up reconstruction.
Area of Science:
- Medical Imaging
- Artificial Intelligence in Medicine
- Magnetic Resonance Imaging
Background:
- Navigated 2D multi-slice dynamic Magnetic Resonance (MR) imaging facilitates high-contrast 4D MR imaging during free breathing, crucial for treatment planning.
- Navigator slices are essential for retrospective stacking of 2D data but extend acquisition times.
- Temporal interpolation of navigator slices offers a solution to reduce acquisition duration without compromising stacking specificity.
Purpose of the Study:
- To propose a novel convolutional neural network (CNN) based method for temporal interpolation of navigator slices in 4D MR imaging.
- To leverage motion field prediction as an intermediate step to improve interpolation accuracy and preserve image information.
- To enable unsupervised estimation of bi-directional motion fields for potential acceleration of 4D reconstruction.
Main Methods:
- Development of a CNN-based approach for temporal interpolation, incorporating motion field prediction.
- Utilizing the underlying motion field to guide image intensity interpolation, avoiding issues like blurring or structure removal.
- Unsupervised estimation of bi-directional motion fields as part of the interpolation process.
Main Results:
- The proposed CNN method faithfully preserves image information, avoiding the artifacts seen in direct intensity space interpolation.
- The method provides unsupervised estimation of bi-directional motion fields.
- 4D reconstruction quality is preserved compared to using true navigators, despite reduced navigator acquisition.
Conclusions:
- The CNN-based temporal interpolation with motion field prediction is an effective method for reducing navigator acquisition in 4D MR imaging.
- This approach preserves image quality and offers potential for significant reduction in 4D reconstruction time.
- The unsupervised motion field estimation provides additional benefits for image registration and reconstruction acceleration.
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