Deep learning reconstruction for cardiac magnetic resonance fingerprinting T1 and T2 mapping
dc.contributor.author | Hamilton, Jesse I. | |
dc.contributor.author | Currey, Danielle | |
dc.contributor.author | Rajagopalan, Sanjay | |
dc.contributor.author | Seiberlich, Nicole | |
dc.date.accessioned | 2021-01-05T18:46:25Z | |
dc.date.available | WITHHELD_16_MONTHS | |
dc.date.available | 2021-01-05T18:46:25Z | |
dc.date.issued | 2021-04 | |
dc.identifier.citation | Hamilton, Jesse I.; Currey, Danielle; Rajagopalan, Sanjay; Seiberlich, Nicole (2021). "Deep learning reconstruction for cardiac magnetic resonance fingerprinting T1 and T2 mapping." Magnetic Resonance in Medicine (4): 2127-2135. | |
dc.identifier.issn | 0740-3194 | |
dc.identifier.issn | 1522-2594 | |
dc.identifier.uri | https://hdl.handle.net/2027.42/163866 | |
dc.publisher | Wiley Periodicals, Inc. | |
dc.subject.other | T2 mapping | |
dc.subject.other | tissue characterization | |
dc.subject.other | deep learning | |
dc.subject.other | magnetic resonance fingerprinting | |
dc.subject.other | neural network | |
dc.subject.other | T1 mapping | |
dc.title | Deep learning reconstruction for cardiac magnetic resonance fingerprinting T1 and T2 mapping | |
dc.type | Article | |
dc.rights.robots | IndexNoFollow | |
dc.subject.hlbtoplevel | Health Sciences | |
dc.description.peerreviewed | Peer Reviewed | |
dc.description.bitstreamurl | http://deepblue.lib.umich.edu/bitstream/2027.42/163866/1/mrm28568.pdf | |
dc.description.bitstreamurl | http://deepblue.lib.umich.edu/bitstream/2027.42/163866/2/mrm28568_am.pdf | |
dc.identifier.doi | 10.1002/mrm.28568 | |
dc.identifier.source | Magnetic Resonance in Medicine | |
dc.identifier.citedreference | Fessler J, Sutton B. Nonuniform fast Fourier transforms using min‐max interpolation. IEEE Trans Signal Process. 2003; 51: 560 ‐ 574. | |
dc.identifier.citedreference | Hinojar R, Nagel E, Puntmann VO. T1 mapping in myocarditis ‐ headway to a new era for cardiovascular magnetic resonance. Expert Rev Cardiovasc Ther. 2015; 13: 871 ‐ 874. | |
dc.identifier.citedreference | Giri S, Chung Y‐C, Merchant A, et al. T2 quantification for improved detection of myocardial edema. J Cardiovasc Magn Reson. 2009; 11: 56. | |
dc.identifier.citedreference | Ma D, Gulani V, Seiberlich N, et al. Magnetic resonance fingerprinting. Nature. 2013; 495: 187 ‐ 192. | |
dc.identifier.citedreference | Hamilton JI, Jiang Y, Chen Y, et al. MR fingerprinting for rapid quantification of myocardial T1, T2, and proton spin density. Magn Reson Med. 2017; 77: 1446 ‐ 1458. | |
dc.identifier.citedreference | Hamilton JI, Jiang Y, Ma D, et al. Investigating and reducing the effects of confounding factors for robust T1 and T2 mapping with cardiac MR fingerprinting. Magn Reson Imaging. 2018; 53: 40 ‐ 51. | |
dc.identifier.citedreference | Ma D, Coppo S, Chen Y, et al. Slice profile and B1 corrections in 2D magnetic resonance fingerprinting. Magn Reson Med. 2017; 78: 1781 ‐ 1789. | |
dc.identifier.citedreference | Buonincontri G, Schulte RF, Cosottini M, Tosetti M. Spiral MR fingerprinting at 7 T with simultaneous B1 estimation. Magn Reson Imaging. 2017; 41: 1 ‐ 6. | |
dc.identifier.citedreference | Cohen O, Zhu B, Rosen MS. MR fingerprinting deep reconstruction network (DRONE). Magn Reson Med. 2018; 80: 885 ‐ 894. | |
dc.identifier.citedreference | Fang Z, Chen Y, Hung S, Zhang X, Lin W, Shen D. Submillimeter MR fingerprinting using deep learning–based tissue quantification. Magn Reson Med. 2020; 84: 579 ‐ 591. | |
dc.identifier.citedreference | Cao P, Cui D, Vardhanabhuti V, Hui ES. Development of fast deep learning quantification for magnetic resonance fingerprinting in vivo. Magn Reson Imaging. 2020; 70: 81 ‐ 90. | |
dc.identifier.citedreference | Hamilton JI, Seiberlich N. Machine learning for rapid magnetic resonance fingerprinting tissue property quantification. Proc IEEE. 2019; 108: 1 ‐ 17. | |
dc.identifier.citedreference | Hamilton JI, Pahwa S, Adedigba J, et al. Simultaneous mapping of T1 and T2 using cardiac magnetic resonance fingerprinting in a cohort of healthy subjects at 1.5T. J Magn Reson Imaging. 2020; 52: 1044 ‐ 1052. | |
dc.identifier.citedreference | Jiang Y, Ma D, Seiberlich N, Gulani V, Griswold MA. MR fingerprinting using fast imaging with steady state precession (FISP) with spiral readout. Magn Reson Med. 2015; 74: 1621 ‐ 1631. | |
dc.identifier.citedreference | Winkelmann S, Schaeffter T, Koehler T, Eggers H, Doessel O. An optimal radial profile order based on the golden ratio for time‐resolved MRI. IEEE Trans Med Imaging. 2007; 26: 68 ‐ 76. | |
dc.identifier.citedreference | Hargreaves B. Variable‐Density Spiral Design Functions. http://mrsrl.stanford.edu/~brian/vdspiral/. Published 2005. Accessed June 1, 2017. | |
dc.identifier.citedreference | Virtue P, Tamir JI, Doneva M, Yu SX, Lustig M. Learning contrast synthesis from MR fingerprinting. In: Proc. 26th Annu. Meet. ISMRM. Paris, France; 2018, p. 676. | |
dc.identifier.citedreference | Wissmann L, Santelli C, Segars WP, Kozerke S. MRXCAT: Realistic numerical phantoms for cardiovascular magnetic resonance. J Cardiovasc Magn Reson. 2014; 16: 63. | |
dc.identifier.citedreference | Bland JM, Altman DG. Statistical methods for assessing agreement between two methods of clinical measurement. Lancet. 1986; 1: 307 ‐ 310. | |
dc.identifier.citedreference | Shao J, Ghodrati V, Nguyen K, Hu P. Fast and accurate calculation of myocardial T1 and T2 values using deep learning Bloch equation simulations (DeepBLESS). Magn Reson Med. 2020. 84: 2831 ‐ 2845. | |
dc.identifier.citedreference | Knoll F, Schwarzl A, Diwoky C, Sodickson DK. gpuNUFFT ‐ An open source GPU library for 3D regridding with direct Matlab interface. In: Proceedings of the ISMRM. 2014, p. 4297. | |
dc.identifier.citedreference | Seiberlich N, Breuer FA, Blaimer M, Barkauskas K, Jakob PM, Griswold MA. Non‐Cartesian data reconstruction using GRAPPA operator gridding (GROG). Magn Reson Med. 2007; 58: 1257 ‐ 1265. | |
dc.identifier.citedreference | Okur A, Kantarcı M, Kızrak Y, et al. Quantitative evaluation of ischemic myocardial scar tissue by unenhanced T1 mapping using 3.0 Tesla MR scanner. Diagn Interv Radiol. 2014; 20: 407 ‐ 413. | |
dc.owningcollname | Interdisciplinary and Peer-Reviewed |
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