606-01-005 · Ultra-High Field Applications
· Thursday, 14 May, 8:30 AM–10:20 AM · Auditorium 2
Keywords:PTxRF Pulse DesignHigh field MRI
Accepted
Zimu Huo 1, Omer Oran2, Kyle M Gilbert3,4, Ravi Menon3,4, Victor A Stenger5, Kawin Setsompop6,7,8
1Department of Radiology, Stanford Medicine, Stanford, United States of America
2Siemens Healthineers, Malvern, United States of America
3Centre for Functional and Metabolic Mapping, Western University, London, Canada
4Department of Medical Biophysics, Western University, London, Canada
5Department of Medicine, University of Hawaii, Honolulu, United States of America
6Stanford Medicine, Stanford, United States of America
7Department of Radiology, Stanford University, Stanford, United States of America
8Electrical Engineering, Stanford University, Stanford, United States of America
Presenting Author: Zimu Huo
Synopsis
Motivation:
Goals:
Approach:
Results:
Full abstract & presentation
The full text, figures, and any recorded presentation for this abstract are not shown here. Log in if you are a member or registered attendee with access.
Full abstracts, figures, and presentations for Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition are available to registered attendees. This content becomes freely available to the public roughly two years after the meeting.
To request or purchase access, contact the ISMRM Central Office at info@ismrm.org.
1. Grissom W, Yip CY, Zhang Z, Stenger VA, Fessler JA, Noll DC. Spatial domain method for the design of RF pulses in multicoil parallel excitation. Magn Reson Med. 2006 Sep;56(3):620-9. doi: 10.1002/mrm.20978. PMID: 16894579. [doi][pmid]
2. Guérin B, Setsompop K, Ye H, Poser BA, Stenger AV, Wald LL. Design of parallel transmission pulses for simultaneous multislice with explicit control for peak power and local specific absorption rate. Magn Reson Med. 2015 May;73(5):1946-53. doi: 10.1002/mrm.25325. Epub 2014 Jun 17. PMID: 24938991; PMCID: PMC4269582. [doi][pmid]
3. Eichfelder G, Gebhardt M. Local specific absorption rate control for parallel transmission by virtual observation points. Magn Reson Med. 2011 Nov;66(5):1468-76. doi: 10.1002/mrm.22927. Epub 2011 May 20. PMID: 21604294. [doi][pmid]
4. Setsompop K, Wald LL, Alagappan V, Gagoski BA, Adalsteinsson E. Magnitude least squares optimization for parallel radio frequency excitation design demonstrated at 7 Tesla with eight channels. Magn Reson Med. 2008 Apr;59(4):908-15. doi: 10.1002/mrm.21513. PMID: 18383281; PMCID: PMC2715966. [doi][pmid]
8. Boris Eberhardt, Benedikt A. Poser, N. Jon Shah, Jörg Felder, B1 field map synthesis with generative deep learning used in the design of parallel-transmit RF pulses for ultra-high field MRI, ISSN 0939-3889, https://doi.org/10.1016/j.zemedi.2021.12.003. [doi]
9. Eberhardt B, Poser BA, Shah NJ, Felder J. Application of Evolution Strategies to the Design of SAR Efficient Parallel Transmit Multi-Spoke Pulses for Ultra-High Field MRI. IEEE Trans Med Imaging. 2020 Dec;39(12):4225-4236. doi: 10.1109/TMI.2020.3013982. Epub 2020 Nov 30. PMID: 32763849. [doi][pmid]
10. Tomi-Tricot R, Gras V, Thirion B, Mauconduit F, Boulant N, Cherkaoui H, Zerbib P, Vignaud A, Luciani A, Amadon A. SmartPulse, a machine learning approach for calibration-free dynamic RF shimming: Preliminary study in a clinical environment. Magn Reson Med. 2019 Dec;82(6):2016-2031. doi: 10.1002/mrm.27870. Epub 2019 Jun 30. PMID: 31257612. [doi][pmid]
11. Kilic T, Liebig P, Demirel OB, Herrler J, Nagel AM, Ugurbil K, Akçakaya M. Unsupervised deep learning with convolutional neural networks for static parallel transmit design: A retrospective study. Magn Reson Med. 2024 Jun;91(6):2498-2507. doi: 10.1002/mrm.30014. Epub 2024 Jan 21. PMID: 38247050; PMCID: PMC10997461. [doi][pmid]
12. Sacolick LI, Wiesinger F, Hancu I, Vogel MW. B1 mapping by Bloch-Siegert shift. Magn Reson Med. 2010;63(5):1315-1322. doi:10.1002/mrm.22357 [doi]
13. Gilbert KM, Klassen LM, Mashkovtsev A, Zeman P, Menon RS, Gati JS. Radiofrequency coil for routine ultra-high-field imaging with an unobstructed visual field. NMR Biomed. 2021;34(3):e4457. doi:10.1002/nbm.4457 [doi]
14. K. He, X. Zhang, S. Ren and J. Sun, "Deep Residual Learning for Image Recognition," 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA, 2016, pp. 770-778, doi: 10.1109/CVPR.2016.90. [doi]