Motion Compensated Dynamic Imaging without Explicit Motion Estimation

The focus of this abstract is to recover dynamic MRI data from highly under-sampled measurements. Compressed sensing schemes that exploit sparsity in Fourier and gradient domains have enjoyed a lot of success in breath-held cardiac MRI. However, these schemes often result in un-acceptable spatio-temporal blurring and residual alias artifacts, when applied to free breathing cardiac MRI with or without cardiac gating. The main reason is the high inter-frame motion, often introduced by respiration. Methods that combine motion estimation and compensation (ME-MC) have been shown to improve the results in this context, but they come with considerably increased computational complexity. In addition, the joint estimation of the motion model parameters and the signal involves a complex non-convex optimization criterion, which is often difficult to solve. Our main objective is to introduce a novel framework for motion-compensated dynamic MR image recovery that does not suffer from the above mentioned drawbacks.

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