Accelerated dynamic MRI Using Patch Regularization for Implicit motion CompEnsation (PRICE)

Purpose: To introduce a fast algorithm for motion-compensated accelerated dynamic MRI. Methods: An efficient patch smoothness regularization scheme, which implicitly compensates for interframe motion, is introduced to recover dynamic MRI data from highly undersampled measurements. The regularization prior is a sum of distances between each rectangular patch in the dataset with other patches in the dataset using a saturating distance metric. Unlike current motion estimation and motion compensation (ME-MC) methods, the proposed scheme does not require reference frames or complex motion models. The proposed algorithm, which alternates between inter-patch shrinkage step and conjugate gradient algorithm, is considerably more computationally efficient than ME-MC methods. The reconstructions obtained using the proposed algorithm is compared against state-of-the-art methods. Results: The proposed method is observed to yield reconstructions with minimal spatiotemporal blurring and motion artifacts. In comparison to the existing state-of-the-art ME-MC methods, PRICE provides comparable or even better image quality with faster reconstruction times (approximately nine times faster). Conclusion: The presented scheme enables computationally efficient and effective motion-compensated reconstruction in a variety of applications with large inter-frame motion and contrast changes. This algorithm could be seen as an alternative over the current state-of-the-art ME-MC schemes that are computationally expensive.