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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.
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Thursday 7th July
Poster Session 2.1 - onsite 15:20 - 16:20, virtual 11:00 - 12:00 (UTC+2)
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