Jaime L Shaw1,2,3, Qi Yang1,4, Zhengwei Zhou1, Zixin Deng1,2, Christopher Nguyen1, Debiao Li1,2, Anthony G Christodoulou1. 1. Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, California. 2. Department of Bioengineering, University of California, Los Angeles, California. 3. Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, California. 4. Department of Radiology, Xuanwu Hospital, Beijing, China.
Abstract
PURPOSE: To evaluate the accuracy and repeatability of a free-breathing, non-electrocardiogram (ECG), continuous myocardial T1 and extracellular volume (ECV) mapping technique adapted from the Multitasking framework. METHODS: The Multitasking framework is adapted to quantify both myocardial native T1 and ECV with a free-breathing, non-ECG, continuous acquisition T1 mapping method. We acquire interleaved high-spatial resolution image data and high-temporal resolution auxiliary data following inversion-recovery pulses at set intervals and perform low-rank tensor imaging to reconstruct images at 344 inversion times, 20 cardiac phases, and 6 respiratory phases. The accuracy and repeatability of Multitasking T1 mapping in generating native T1 and ECV maps are compared with conventional techniques in a phantom, a simulation, 12 healthy subjects, and 10 acute myocardial infarction patients. RESULTS: In phantoms, Multitasking T1 mapping correlated strongly with the gold-standard spin-echo inversion recovery (R2 = 0.99). A simulation study demonstrated that Multitasking T1 mapping has similar myocardial sharpness to the fully sampled ground truth. In vivo native T1 and ECV values from Multitasking T1 mapping agree well with conventional MOLLI values and show good repeatability for native T1 and ECV mapping for 60 seconds, 30 seconds, or 15 seconds of data. Multitasking native T1 and ECV in myocardial infarction patients correlate positively with values from MOLLI. CONCLUSION: Multitasking T1 mapping can quantify native T1 and ECV in the myocardium with free-breathing, non-ECG, continuous scans with good image quality and good repeatability in vivo in healthy subjects, and correlation with MOLLI T1 and ECV in acute myocardial infarction patients.
PURPOSE: To evaluate the accuracy and repeatability of a free-breathing, non-electrocardiogram (ECG), continuous myocardial T1 and extracellular volume (ECV) mapping technique adapted from the Multitasking framework. METHODS: The Multitasking framework is adapted to quantify both myocardial native T1 and ECV with a free-breathing, non-ECG, continuous acquisition T1 mapping method. We acquire interleaved high-spatial resolution image data and high-temporal resolution auxiliary data following inversion-recovery pulses at set intervals and perform low-rank tensor imaging to reconstruct images at 344 inversion times, 20 cardiac phases, and 6 respiratory phases. The accuracy and repeatability of Multitasking T1 mapping in generating native T1 and ECV maps are compared with conventional techniques in a phantom, a simulation, 12 healthy subjects, and 10 acute myocardial infarctionpatients. RESULTS: In phantoms, Multitasking T1 mapping correlated strongly with the gold-standard spin-echo inversion recovery (R2 = 0.99). A simulation study demonstrated that Multitasking T1 mapping has similar myocardial sharpness to the fully sampled ground truth. In vivo native T1 and ECV values from Multitasking T1 mapping agree well with conventional MOLLI values and show good repeatability for native T1 and ECV mapping for 60 seconds, 30 seconds, or 15 seconds of data. Multitasking native T1 and ECV in myocardial infarctionpatients correlate positively with values from MOLLI. CONCLUSION: Multitasking T1 mapping can quantify native T1 and ECV in the myocardium with free-breathing, non-ECG, continuous scans with good image quality and good repeatability in vivo in healthy subjects, and correlation with MOLLI T1 and ECV in acute myocardial infarctionpatients.
Authors: Kelvin Chow; Jacqueline A Flewitt; Jordin D Green; Joseph J Pagano; Matthias G Friedrich; Richard B Thompson Journal: Magn Reson Med Date: 2013-07-23 Impact factor: 4.668
Authors: Daniel Gensler; Philipp Mörchel; Florian Fidler; Oliver Ritter; Harald H Quick; Mark E Ladd; Wolfgang R Bauer; Georg Ertl; Peter M Jakob; Peter Nordbeck Journal: Radiology Date: 2014-11-13 Impact factor: 11.105
Authors: Daniel R Messroghli; James C Moon; Vanessa M Ferreira; Lars Grosse-Wortmann; Taigang He; Peter Kellman; Julia Mascherbauer; Reza Nezafat; Michael Salerno; Erik B Schelbert; Andrew J Taylor; Richard Thompson; Martin Ugander; Ruud B van Heeswijk; Matthias G Friedrich Journal: J Cardiovasc Magn Reson Date: 2017-10-09 Impact factor: 5.364
Authors: Sen Ma; Christopher T Nguyen; Fei Han; Nan Wang; Zixin Deng; Nader Binesh; Franklin G Moser; Anthony G Christodoulou; Debiao Li Journal: Magn Reson Med Date: 2019-11-25 Impact factor: 4.668
Authors: Zhehao Hu; Anthony G Christodoulou; Nan Wang; Jaime L Shaw; Shlee S Song; Marcel M Maya; Mariko L Ishimori; Lindsy J Forbess; Jiayu Xiao; Xiaoming Bi; Fei Han; Debiao Li; Zhaoyang Fan Journal: Magn Reson Med Date: 2020-04-16 Impact factor: 4.668
Authors: Burhaneddin Yaman; Sebastian Weingärtner; Nikolaos Kargas; Nicholas D Sidiropoulos; Mehmet Akçakaya Journal: IEEE Trans Comput Imaging Date: 2019-09-12
Authors: Sen Ma; Nan Wang; Zhaoyang Fan; Marwa Kaisey; Nancy L Sicotte; Anthony G Christodoulou; Debiao Li Journal: Magn Reson Med Date: 2020-10-26 Impact factor: 4.668