Show simple item record

Deep learning reconstruction for cardiac magnetic resonance fingerprinting T1 and T2 mapping

dc.contributor.authorHamilton, Jesse I.
dc.contributor.authorCurrey, Danielle
dc.contributor.authorRajagopalan, Sanjay
dc.contributor.authorSeiberlich, Nicole
dc.date.accessioned2021-01-05T18:46:25Z
dc.date.availableWITHHELD_16_MONTHS
dc.date.available2021-01-05T18:46:25Z
dc.date.issued2021-04
dc.identifier.citationHamilton, 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.issn0740-3194
dc.identifier.issn1522-2594
dc.identifier.urihttps://hdl.handle.net/2027.42/163866
dc.publisherWiley Periodicals, Inc.
dc.subject.otherT2 mapping
dc.subject.othertissue characterization
dc.subject.otherdeep learning
dc.subject.othermagnetic resonance fingerprinting
dc.subject.otherneural network
dc.subject.otherT1 mapping
dc.titleDeep learning reconstruction for cardiac magnetic resonance fingerprinting T1 and T2 mapping
dc.typeArticle
dc.rights.robotsIndexNoFollow
dc.subject.hlbtoplevelHealth Sciences
dc.description.peerreviewedPeer Reviewed
dc.description.bitstreamurlhttp://deepblue.lib.umich.edu/bitstream/2027.42/163866/1/mrm28568.pdf
dc.description.bitstreamurlhttp://deepblue.lib.umich.edu/bitstream/2027.42/163866/2/mrm28568_am.pdf
dc.identifier.doi10.1002/mrm.28568
dc.identifier.sourceMagnetic Resonance in Medicine
dc.identifier.citedreferenceFessler J, Sutton B. Nonuniform fast Fourier transforms using min‐max interpolation. IEEE Trans Signal Process. 2003; 51: 560 ‐ 574.
dc.identifier.citedreferenceHinojar 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.citedreferenceGiri 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.citedreferenceMa D, Gulani V, Seiberlich N, et al. Magnetic resonance fingerprinting. Nature. 2013; 495: 187 ‐ 192.
dc.identifier.citedreferenceHamilton 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.citedreferenceHamilton 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.citedreferenceMa 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.citedreferenceBuonincontri 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.citedreferenceCohen O, Zhu B, Rosen MS. MR fingerprinting deep reconstruction network (DRONE). Magn Reson Med. 2018; 80: 885 ‐ 894.
dc.identifier.citedreferenceFang 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.citedreferenceCao 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.citedreferenceHamilton JI, Seiberlich N. Machine learning for rapid magnetic resonance fingerprinting tissue property quantification. Proc IEEE. 2019; 108: 1 ‐ 17.
dc.identifier.citedreferenceHamilton 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.citedreferenceJiang 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.citedreferenceWinkelmann 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.citedreferenceHargreaves B. Variable‐Density Spiral Design Functions. http://mrsrl.stanford.edu/~brian/vdspiral/. Published 2005. Accessed June 1, 2017.
dc.identifier.citedreferenceVirtue 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.citedreferenceWissmann L, Santelli C, Segars WP, Kozerke S. MRXCAT: Realistic numerical phantoms for cardiovascular magnetic resonance. J Cardiovasc Magn Reson. 2014; 16: 63.
dc.identifier.citedreferenceBland JM, Altman DG. Statistical methods for assessing agreement between two methods of clinical measurement. Lancet. 1986; 1: 307 ‐ 310.
dc.identifier.citedreferenceShao 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.citedreferenceKnoll 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.citedreferenceSeiberlich 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.citedreferenceOkur 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.owningcollnameInterdisciplinary and Peer-Reviewed


Files in this item

Show simple item record

Remediation of Harmful Language

The University of Michigan Library aims to describe library materials in a way that respects the people and communities who create, use, and are represented in our collections. Report harmful or offensive language in catalog records, finding aids, or elsewhere in our collections anonymously through our metadata feedback form. More information at Remediation of Harmful Language.

Accessibility

If you are unable to use this file in its current format, please select the Contact Us link and we can modify it to make it more accessible to you.