Temporal Interpolation of Abdominal MRIs Acquired During Free-Breathing

We propose a convolutional neural network (CNN) based solution for temporal image interpolation in navigated 2D multi-slice dynamic MRI acquisitions. Such acquisitions can achieve high contrast time-resolved volumetric images without the need for breath-holding, which makes them essential for quantifying breathing induced motion for MR guided therapies. Reducing the number of navigator slices needed in these acquisitions would allow increasing through-plane resolution and reducing overall acquisition time. The proposed CNN achieves this by interpolating between successive navigator slices. The method is an end-to-end learning based approach and avoids the determination of the motion field between the input images. We evaluate the method on a dataset of abdominal MRI sequences acquired from 14 subjects during free-breathing, which exhibit pseudo-periodic motion and sliding motion interfaces. Compared to an interpolation-by-registration approach, the method achieves higher interpolation accuracy on average, quantified in terms of intensity RMSE and residual motion errors. Further, we analyze the differences between the two methods, showing the CNN’s advantages in peak inhale and exhale positions.