Accelerated dynamic MRI Using Patch Regularization for Implicit motion CompEnsation (PRICE) Framework: We introduce a fast and an efficient patch smoothness regularization scheme, which implicitly compensates for interframe motion, 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 explicit prior information such as reference frames or motion model. 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 stateof-the-art methods using free-breathing cardiac cine and myocardial perfusion datasets, using retrospective and prospective multi-channel experiments
zoonono / price Goto Github PK
View Code? Open in Web Editor NEWThis project forked from ymohsin/price
Accelerated dynamic MRI Using Patch Regularization for Implicit motion CompEnsation (PRICE)