DDoS-UNet: Incorporating temporal information using Dynamic Dual-channel UNet for enhancing super-resolution of dynamic MRI
Soumick Chatterjee, Chompunuch Sarasaen, Georg Rose, Andreas Nürnberger, Oliver Speck
Dynamic MRI is an essential tool for interventions to visualise movements or changes in the target organ. However, such MRI acquisition with high temporal resolution suffers from limited spatial resolution - also known as the spatio-temporal trade-off. Several approaches, including deep learning based super-resolution approaches, have been proposed to mitigate this trade-off. Nevertheless, such an approach typically aims to super-resolve each time-point separately, treating them as individual volumes. This research addresses the problem by creating a deep learning model that attempts to learn spatial and temporal relationships. The performance was tested with 3D dynamic data that was undersampled to different in-plane levels. The proposed network achieved an average SSIM value of 0.951±0.017 while reconstructing the lowest resolution data (i.e. only 4% of the k-space acquired), resulting in a theoretical acceleration factor of 25.
Thursday 7th July
Poster Session 2.1 - onsite 15:20 - 16:20, virtual 11:00 - 12:00 (UTC+2)