@article{250f6eade32e45c99ee5ddd367933242,
title = "5D whole-heart sparse MRI",
abstract = "Purpose: A 5D whole-heart sparse imaging framework is proposed for simultaneous assessment of myocardial function and high-resolution cardiac and respiratory motion-resolved whole-heart anatomy in a single continuous noncontrast MR scan. Methods: A non–electrocardiograph (ECG)-triggered 3D golden-angle radial balanced steady-state free precession sequence was used for data acquisition. The acquired 3D k-space data were sorted into a 5D dataset containing separated cardiac and respiratory dimensions using a self-extracted respiratory motion signal and a recorded ECG signal. Images were then reconstructed using XD-GRASP, a multidimensional compressed sensing technique exploiting correlations/sparsity along cardiac and respiratory dimensions. 5D whole-heart imaging was compared with respiratory motion-corrected 3D and 4D whole-heart imaging in nine volunteers for evaluation of the myocardium, great vessels, and coronary arteries. It was also compared with breath-held, ECG-gated 2D cardiac cine imaging for validation of cardiac function quantification. Results: 5D whole-heart images received systematic higher quality scores in the myocardium, great vessels and coronary arteries. Quantitative coronary sharpness and length were always better for the 5D images. Good agreement was obtained for quantification of cardiac function compared with 2D cine imaging. Conclusion: 5D whole-heart sparse imaging represents a robust and promising framework for simplified comprehensive cardiac MRI without the need for breath-hold and motion correction. Magn Reson Med 79:826–838, 2018.",
keywords = "3D radial sampling, compressed sensing, coronary MRA, golden-angle, respiratory motion, whole-heart MRI",
author = "Li Feng and Simone Coppo and Davide Piccini and Jerome Yerly and Lim, {Ruth P.} and Masci, {Pier Giorgio} and Matthias Stuber and Sodickson, {Daniel K.} and Ricardo Otazo",
note = "Funding Information: This work was supported in part by the NIH and Swiss National Science Foundation, and was performed under the rubric of the Center for Advanced Imaging Innovation and Research (CAI2R), a NIBIB Biomedical Technology Resource Center (NIH P41 EB017183). Early support was also derived from NIH R01 EB000447. The authors thank Dr. Florian Knoll at NYU School of Medicine for providing the gpuNUFFT toolbox (http://www.ismrm. org/MR-Hub/). Funding Information: 1Center for Advanced Imaging Innovation and Research (CAI2R), and Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, New York, USA. 2Department of Radiology, University Hospital (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland. 3Center for Biomedical Imaging (CIBM), Lausanne, Switzerland. 4Advanced Clinical Imaging Technology, Siemens Healthcare, Lausanne, Switzerland. 5Department of Radiology, Austin Health and The University of Melbourne, Melbourne, Victoria, Australia. 6Division of Cardiology and Cardiac MR Center, University Hospital (CHUV), Lausanne, Switzerland. Grant sponsor: NIH; Grant numbers: P41 EB017183, R01 EB000447, SNF 320030_143923. *Correspondence to: Li Feng, Ph.D., Center for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, New York University School of Medicine, 660 First Avenue, New York, NY 10016. E-mail: li.feng@nyumc.org †These authors contributed equally to this work. Received 9 August 2016; revised 9 April 2017; accepted 11 April 2017 DOI 10.1002/mrm.26745 Published online 11 May 2017 in Wiley Online Library (wileyonlinelibrary. com). Publisher Copyright: {\textcopyright} 2017 International Society for Magnetic Resonance in Medicine",
year = "2018",
month = feb,
doi = "10.1002/mrm.26745",
language = "English (US)",
volume = "79",
pages = "826--838",
journal = "Magnetic resonance in medicine",
issn = "0740-3194",
publisher = "John Wiley and Sons Inc.",
number = "2",
}