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XD‐GRASP: Golden‐angle radial MRI with reconstruction of extra motion‐state dimensions using compressed sensing
Author(s) -
Feng Li,
Axel Leon,
Chandarana Hersh,
Block Kai Tobias,
Sodickson Daniel K.,
Otazo Ricardo
Publication year - 2016
Publication title -
magnetic resonance in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.696
H-Index - 225
eISSN - 1522-2594
pISSN - 0740-3194
DOI - 10.1002/mrm.25665
Subject(s) - computer vision , grasp , compressed sensing , computer science , artificial intelligence , motion (physics) , context (archaeology) , image quality , motion compensation , iterative reconstruction , image (mathematics) , paleontology , biology , programming language
Purpose To develop a novel framework for free‐breathing MRI called XD‐GRASP, which sorts dynamic data into extra motion‐state dimensions using the self‐navigation properties of radial imaging and reconstructs the multidimensional dataset using compressed sensing. Methods Radial k‐space data are continuously acquired using the golden‐angle sampling scheme and sorted into multiple motion‐states based on respiratory and/or cardiac motion signals derived directly from the data. The resulting undersampled multidimensional dataset is reconstructed using a compressed sensing approach that exploits sparsity along the new dynamic dimensions. The performance of XD‐GRASP is demonstrated for free‐breathing three‐dimensional (3D) abdominal imaging, two‐dimensional (2D) cardiac cine imaging and 3D dynamic contrast‐enhanced (DCE) MRI of the liver, comparing against reconstructions without motion sorting in both healthy volunteers and patients. Results XD‐GRASP separates respiratory motion from cardiac motion in cardiac imaging, and respiratory motion from contrast enhancement in liver DCE‐MRI, which improves image quality and reduces motion‐blurring artifacts. Conclusion XD‐GRASP represents a new use of sparsity for motion compensation and a novel way to handle motions in the context of a continuous acquisition paradigm. Instead of removing or correcting motion, extra motion‐state dimensions are reconstructed, which improves image quality and also offers new physiological information of potential clinical value. Magn Reson Med 75:775–788, 2016. © 2015 Wiley Periodicals, Inc.