A deep learning approach for reconstruction of undersampled Cartesian and Radial data
Chatterjee, Soumick ; Breitkopf, Mario ; Sarasaen, Chompunuch ; Rose, Georg ; Nürnberger, Andreas ; Speck, OliverMagnetic resonance imaging (MRI) is an inherently slow process turning the real-time monitoring of a patient during interventions into a challenging task. Discarding image signal parts (i.e. undersampling) during data acquisition might be one way to shorten scan times, however negatively affecting image quality.
This sub-project focuses on the reconstruction of highly undersampled MR data, which equals solving an enormous underdetermined system of equations with an infinite number of solutions.
To cope with this task, it is useful to take additional information into account by, for instance, integrating prior information from planning datasets or clinical scans acquired on a daily basis.
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