Is Laplacian indispensable to Magnetic Resonance Electrical Property Tomography (MREPT)- An analysis from the perspective of Reconstruction Error Compensation Neural Networks-Reference-Cited by-同舟云学术

Is Laplacian indispensable to Magnetic Resonance Electrical Property Tomography (MREPT)- An analysis from the perspective of Reconstruction Error Compensation Neural Networks

Published:2023-08-19 Issue: Volume: Page:
ISSN:
Container-title:2023 XXXVth General Assembly and Scientific Symposium of the International Union of Radio Science (URSI GASS)
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Author:

Qin Ruian¹,Garcia Inda Adan Jafet²,Zhou Zhongchao²,Enomoto Yukihiro²,Yang Tianyi²,İmamoğlu Nevrez³,Gomez-Tames Jose²,Huang Shaoying⁴,Yu Wenwei²

Affiliation:

1. Department of Medical Engineering, Chiba University, Chiba, Japan

2. Chiba University,Department of Medical Engineering,Chiba,Japan

3. National Institute of Advanced Industrial Science and Technology,Digital Architecture Research Center,Tokyo,Japan

4. National University of Singapore, Singapore,Department of Surgery,Singapore

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10265282/10265283/10265641.pdf?arnumber=10265641

Reference12 articles.

1. Physics Informed Neural Networks (PINN) for Low Snr Magnetic Resonance Electrical Properties Tomography (MREPT)

2. Machine-learning-enhanced stabilized cr-mrept for noise-robust and artifact-reduced electrical properties reconstruction;inda;2020 IEEE International Conference on Computational Electromagnetics (ICCEM),2020

3. Explaining the 68-95-99.7 rule for a normal distribution;galarnyk;Towards Data Science,2018

4. Rec-nn: A reconstruction error compensation neural network for magnetic resonance electrical property tomography (mrept);qin;Proceedings of 45th Annual International Conference of the IEEE Engineering in Medicine and Biology Society,2023

5. Error analysis of helmholtz-based MR-electrical properties tomography