2020 ISMRM and SMRT Virtual Conference and Exhibition
8 August 2020 – 14 August 2020 · Online, Virtual
0181 ISMRM Abstract

Improving Phase-based Conductivity Reconstructions by Means of Deep Learning-based Denoising of B1+ Phase Data

0181 · Contrast Mechanisms: Beyond the Usual Suspects · Monday, 10 August, 2:30 PM–3:15 PM · Monday Parallel 1 Live Q&A
Accepted
Kyu-Jin Jung 1, Stefano Mandija2,3, Jun-Hyeong Kim1, Kanghyun Ryu1, Soozy Jung1, Mina Park4, Mohammed A. Al-masni1, Cornelis A van den Berg2, Dong-Hyun Kim1
1Department of Electrical and Electronic Engineering, Yonsei University, Seoul, Korea, Republic of
2Department of Radiotherapy, Division of Imaging & Oncology, University Medical Center Utrecht, Utrecht, Netherlands
3Computational Imaging Group for MR Diagnostics and Therapy, Center for Image Sciences, University Medical Center Utrecht, Utrecht, Netherlands
4Department of Radiology, Gangnam Severance Hospital, 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. U. Katscher, T. Voigt, C. Findeklee, et al., Determination of Electric Conductivity and Local SAR Via B1 Mapping. IEEE TMI. 2009;28(9):1365-1374.
2. T. Voigt, Katscher U., Doessel O., Quantitative conductivity and permittivity imaging of the human brain using electrical properties tomography. Mag. Reson. Med., 2011;66(2): 456-466.
3. Seung-Kyun Lee, et al., Theoretical investigation of random noise-limited signal-to-noise ratio in MR-based electrical properties tomography. IEEE transaction on medical imaging, 2015, 34.11: 2220-2232.
4. Eunhee Kang, et al., Deep convolutional framelet denosing for low-dose CT via wavelet residual network. IEEE transactions on medical imaging, 2018;37.6:1358-1369.
5. Bob van den Bergen, et al., Ultra fast electromagnetic field computations for RF multi-transmit techniques in high field MRI. Physics in Medicine & Biology, 2009, 54.5: 1253.
6. P. A. Hasgall, et al., ITIS Database for thermal and electromagnetic parameters of biological tissues; 2013.
7. Stefano Mandija, et al., Error analysis of helmholtz‐based MR‐electrical properties tomography. Magnetic resonance in medicine, 2018, 80.1: 90-100.
8. Jaewook Shin, et al., Redesign of the Laplacian kernel for improvements in conductivity imaging using MRI. Magnetic resonance in medicine, 2019, 81.3: 2167-2175.
9. Marie-Christine Gosselin, 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.
10. Joonsung Lee, et al., MR‐based conductivity imaging using multiple receiver coils. Magnetic resonance in medicine, 2016, 76.2: 530-539.

Cite this abstract

http://echo.ismrm.org/p/ISMRM2020/0181