Temporal Interpolation via Motion Field Prediction
This work addresses a domain-specific problem in medical imaging by providing an incremental improvement to reduce acquisition time and computational cost in 4D MR reconstruction.
The paper tackles the problem of prolonged acquisition sessions in navigated 2D multi-slice dynamic MR imaging by proposing a CNN-based method for temporal interpolation of navigator slices via motion field prediction, which reduces the number of navigator acquisitions without degrading specificity and halves the number of registrations required during 4D reconstruction, substantially cutting reconstruction time.
Navigated 2D multi-slice dynamic Magnetic Resonance (MR) imaging enables high contrast 4D MR imaging during free breathing and provides in-vivo observations for treatment planning and guidance. Navigator slices are vital for retrospective stacking of 2D data slices in this method. However, they also prolong the acquisition sessions. Temporal interpolation of navigator slices an be used to reduce the number of navigator acquisitions without degrading specificity in stacking. In this work, we propose a convolutional neural network (CNN) based method for temporal interpolation via motion field prediction. The proposed formulation incorporates the prior knowledge that a motion field underlies changes in the image intensities over time. Previous approaches that interpolate directly in the intensity space are prone to produce blurry images or even remove structures in the images. Our method avoids such problems and faithfully preserves the information in the image. Further, an important advantage of our formulation is that it provides an unsupervised estimation of bi-directional motion fields. We show that these motion fields can be used to halve the number of registrations required during 4D reconstruction, thus substantially reducing the reconstruction time.