Reconstruction of Cardiac Perfusion MRI with Motion Compensated Compressed Sensing

In cardiac perfusion MRI, the low imaging speed limits the spatiotemporal resolution. To improve the imaging speed and hence the spatiotemporal resolution, Compressed sensing (CS) has been utilized. The purpose of this paper is to improve reconstruction of under sampled data based on k-space data acquisition using a Golden Radial trajectory, and then applying CS reconstruction and a nonrigid temporal registration algorithm. We propose a multiresolution scheme in which coarser reconstructions serve as initialization of the finer ones. An initial, low temporal resolution sequence is obtained by a regular CS reconstruction. From the quantitative evaluation, we determined that Signal to Error Ratio (SER) differences are within the range of 5-6 dB. Our experiments showed that the proposed algorithm can reduce motion artifacts and temporal blurring.

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