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Temporally-Aware Neural Networks For interventional Cine MRI Reconstruction From Severely Undersampled Data
- Mahajan, Niraj Rajesh
- Advisor(s): Contijoch, Francisco;
- McAuley, Julian
No data is associated with this publication.
Abstract
Magnetic Resonance Imaging (MRI) guidance of interventional procedures requires fast image reconstruction and display. Current techniques for iCMR (interventional Cardiovascular Magnetic Resonance), like parallel imaging, have a trade-off between speed and quality. Neural network(NN)-based methods have been shown to improve iCMR reconstruction speed while maintaining high quality using undersampled k-space data. In this thesis, we propose FOURIER-Net (FOUrier Recurrent ImagE Reconstruction Network), a novel architecture that can reconstruct images from severely undersampled k-space data with very low latency. We compare our method’s reconstruction speed and quality to the existing methods.