2022 Joint Annual Meeting ISMRM-ESMRMB and ISMRT 31st Annual Meeting
7 May 2022 – 15 May 2022 · London, United Kingdom
2921 ISMRM Abstract

A semi-realistic and reusable 3D printed brain phantom for MR-based Electrical Properties Tomography

2921 · Electromagnetic Properties & Oximetry · Thursday, 12 May, 9:15 AM–10:15 AM · Exhibition Hall:S8 & S9
Accepted
Thierry G Meerbothe 1,2, Sammy Florczak3, Peter R Stijnman1,2, Cornelis A van den Berg1,2, Riccardo Levato3,4, Stefano Mandija1,2
1Department of Radiotherapy, Division of Imaging and Oncology, University Medical Center Utrecht, Utrecht, Netherlands
2Computational Imaging Group for MR Diagnostics and Therapy, Center for Image Sciences, University Medical Center Utrecht, Utrecht, Netherlands
3Department of Orthopaedics, University Medical Center Utrecht, Utrecht, Netherlands
4Department of Clinical Sciences, Faculty of Veterinary Medicine, Utrecht University, Utrecht, Netherlands
Presenting Author: Thierry G Meerbothe
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References

1. McCann, Hannah, Giampaolo Pisano, and Leandro Beltrachini. "Variation in reported human head tissue electrical conductivity values." Brain topography 32.5 (2019): 825-858. Original manuscript 2. Mandija, Stefano, et al. "Brain Tissue Conductivity Measurements with MR-Electrical Properties Tomography: An In Vivo Study." Brain topography 34.1 (2021): 56-63.
2. Duan, Song, et al. "Quantitative analysis of the reconstruction errors of the currently popular algorithm of magnetic resonance electrical property tomography at the interfaces of adjacent tissues." NMR in Biomedicine 29.6 (2016): 744-750.
3. Mandija, Stefano, et al. "Error analysis of helmholtz‐based MR‐electrical properties tomography." Magnetic resonance in medicine 80.1 (2018): 90-100.
4. Amunts K, Lepage C, Borgeat L, Mohlberg H, Dickscheid T, Rousseau M-É, Bludau S, Bazin PL, Lewis LB, Oros-Peusquens AM, Shah NJ, Lippert T, Zilles K, Evans AC. BigBrain: An ultrahigh-resolution 3D human brain model. Science. 2013; 340(6139):1472-1475. doi: 10.1126/science.1235381. PMID: 23788795. [doi] [pmid]
5. Fedorov, Andriy, et al. "3D Slicer as an image computing platform for the Quantitative Imaging Network." Magnetic resonance imaging 30.9 (2012): 1323-1341.
6. Ultimaker, “Cura 3D Printing Slicing Software,” (Accessed: 2021-10-26). [Online]. Available: https://ultimaker.com/en/products/cura-software 8. Duan, Qi, et al. "Characterization of a dielectric phantom for high‐field magnetic resonance imaging applications." Medical physics 41.10 (2014): 102303.
7. Sim4Life (ZMT AG, Zurich, CH) 10. Hampe, Nils, et al. "Investigating the challenges and generalizability of deep learning brain conductivity mapping." Physics in Medicine & Biology 65.13 (2020): 135001.
8. Gosselin M C, Neufeld E, Moser H, Huber E, Farcito S, Gerber L, Jedensjö M, Hilber I, Di Gennaro F, Lloyd B, Cherubini E, Szczerba D, Kainz W, Kuster N, Development of a new generation of high-resolution anatomical models for medical device evaluation: the Virtual Population 3.0, Physics in Medicine and Biology, 59(18):5287-5303, 2014.
9. Yarnykh, Vasily L. "Actual flip‐angle imaging in the pulsed steady state: a method for rapid three‐dimensional mapping of the transmitted radiofrequency field." Magnetic Resonance in Medicine: An Official Journal of the International Society for Magnetic Resonance in Medicine 57.1 (2007): 192-200.

Cite this abstract

http://echo.ismrm.org/p/ISMRM2022/2921