2022 Joint Annual Meeting ISMRM-ESMRMB and ISMRT 31st Annual Meeting
7 May 2022 – 15 May 2022 · London, United Kingdom
0705 ISMRM Abstract

Feasibility study for conductivity reconstructions from spin-echo images using artificial neural network with simulation data in 3T MR system

Accepted
Kyu-Jin Jung 1, Stefano Mandija2,3, Jun-Hyung Kim1, Chuanjiang Cui1, Sanghyeok Choi1, Jaeuk Yi1, Mina Park4, Cornelis A van den Berg2,3, Dong-Hyun Kim1
1Department of Electrical and Electronic Engineering, Yonsei University, Seoul, Korea, Republic of
2Department of Radiotherapy, University Medical Center Utrecht, Utrecht, Netherlands
3Computational Imaging Group for MR Diagnostics and Therapy, University Medical Center Utrecht, Utrecht, Netherlands
4Department of Radiology, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul, Korea, Republic of
Presenting Author: Kyu-Jin Jung
Full abstract & presentation

The full text, figures, and any recorded presentation for this abstract are not shown here.

This content is freely available and no longer restricted to registered attendees. Log in or create a free account to view it.

Log in

References

1. Katscher U, et al., Determination of Electric Conductivity and Local SAR Via B1 Mapping. IEEE transactions on medical imaging, 2009;28(9):1365-1374.
2. Voigt T, et al., Quantitative conductivity and permittivity imaging of the human brain using electrical properties tomography. Magnetic Resonance in Medicine, 2011;66(2):456-466.
3. Kim S-Y, et al. Correlation between conductivity and prognostic factors in invasive breast cancer using magnetic resonance electric properties tomography (MREPT). European radiology, 2016;26(7):2317-2326.
4. Tha K.K., et al. Noninvasive electrical conductivity measurement by MRI: a test of its validity and the electrical conductivity characteristics of glioma. European radiology, 2018;28(1):348-355.
5. Mori N, et al. Diagnostic value of electric properties tomography (EPT) for differentiating benign from malignant breast lesions: comparison with standard dynamic contrast-enhanced MRI. European radiology, 2019;29(4):1778-1786.
6. Shin J, et al. Quantitative conductivity estimation error due to statistical noise in complex B1+ map. Journal of the Korean Society of Magnetic Resonance in Medicine, 2014;18(4):303-313.
7. Lee S.K., et al. Theoretical investigation of random noise-limited signal-to-noise ratio in MR-based electrical properties tomography. IEEE transactions on medical imaging, 2015;34(11):2220-2232.
8. Seo J, et al. Error analysis of nonconstant admittivity for MR-based electric property imaging. IEEE transactions on medical imaging, 2011;31(2):430-437.
9. Mandija S, et al. Error analysis of helmholtz‐based MR‐electrical properties tomography. Magnetic Resonance in Medicine, 2018;80(1):90-100.
10. Mandija S, et al. Opening a new window on MR-based Electrical Properties Tomography with deep learning. Scientific reports, 2019;9(1):1-9.
11. Leijsen R., et al. Combining deep learning and 3D contrast source inversion in MR‐based electrical properties tomography. NMR in Biomedicine, 2019;e4211.
12. Hampe N., et al. Investigating the challenges and generalizability of deep learning brain conductivity mapping. Physics in Medicine & Biology, 2020;65(13):135001.
13. Jung K-J, et al. Improving phase‐based conductivity reconstruction by means of deep learning–based denoising of B1+ phase data for 3T MRI. Magnetic Resonance in Medicine, 2021;86(4):2084-2094.
14. Christ A, et al. The Virtual Family—development of surface-based anatomical models of two adults and two children for dosimetric simulations. Physics in Medicine & Biology, 2009;55:N23.
15. Gosselin M-C, et al. Development of a new generation of high-resolution anatomical models for medical device evaluation: the Virtual Population 3.0. Physics in Medicine & Biology, 2014;59(18):5287.
16. Lee S-K, et al. Theoretical investigation of random noise-limited signal-to-noise ratio in MR-based electrical properties tomography. IEEE transactions on medical imaging, 2015,34(11): 2220-2232.
17. Lee J, et al. MR‐based conductivity imaging using multiple receiver coils. Magnetic Resonance in Medicine, 2016;76(2):530-539.
18. Mandija S, et al. Brain Tissue Conductivity Measurements with MR-Electrical Properties Tomography: An In Vivo Study. Brain topography, 2021;34(1):56-63.
19. Liao Y, et al. An MR technique for simultaneous quantitative imaging of water content, conductivity and susceptibility, with application to brain tumours using a 3T hybrid MR-PET scanner. Scientific reports, 2019;9(1):1-12.

Cite this abstract