DDoS-UNet: Incorporating Temporal Information Using Dynamic Dual-Channel UNet for Enhancing Super-Resolution of Dynamic MRI

Magnetic resonance imaging (MRI) provides high spatial resolution and excellent soft-tissue contrast without using harmful ionising radiation. Dynamic MRI is an essential tool for interventions to visualise movements or changes of the target organ. However, such MRI acquisitions with high temporal resolution suffer from limited spatial resolution - also known as the spatio-temporal trade-off of dynamic MRI. Several approaches, including deep learning based super-resolution approaches by treating each timepoint as individual volumes. This research addresses this issue by creating a deep learning model which attempts to learn both spatial and temporal relationships. A modified 3D UNet model, DDoS-UNet, is proposed - which takes the low-resolution volume of the current timepoint along with a prior image volume. Initially, the network is supplied with a static high-resolution planning scan as the prior image along with the low-resolution input to super-resolve the first timepoint. Then it continues step-wise by using the super-resolved timepoints as the prior image while super-resolving the subsequent timepoints. The model performance was tested with 3D dynamic data that was undersampled to different in-plane levels and achieved an average SSIM value of
$0.951\pm 0.017$
while reconstructing only 4% of the k-space - which could result in a theoretical acceleration factor of 25. The proposed approach can be used to reduce the required scan-time while achieving high spatial resolution - consequently alleviating the spatio-temporal trade-off of dynamic MRI, by incorporating prior knowledge of spatio-temporal information from the available high-resolution planning scan and the existing temporal redundancy of time-series images into the network model.