Fast Reconstruction of Accelerated Dynamic MRI Using Manifold Kernel Regression
We present a novel method for fast reconstruction of dynamic MRI from undersampled k-space data, thus enabling highly accelerated acquisition. The method is based on kernel regression along the manifold structure of the sequence derived directly from k-space data. Unlike compressed sensing techniques which require solving a complex optimisation problem, our reconstruction is fast, taking under 5 seconds for a 30 frame sequence on conventional hardware. We demonstrate our method on 10 retrospectively undersampled cardiac cine MR sequences, showing improved performance over state-of-the-art compressed sensing.
KeywordsCompress Sensing Dynamic Magnetic Resonance Imaging Manifold Learning Magnetic Resonance Imaging Acquisition Complex Optimisation Problem
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