Magnetic Induction Tomography of IntraCerebral Hemorrhage Based on Improved Newton-Raphson Algorithm

CAO Honggui; YE Bo; JIANG Ying; LUO Siqi; CAO Zhongkai; OUYANG Junlin

doi:10.11999/JEIT221393

Volume 45 Issue 12

Dec. 2023

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Article Navigation > Journal of Electronics & Information Technology > 2023 > 45(12): 4477-4488

CAO Honggui, YE Bo, JIANG Ying, LUO Siqi, CAO Zhongkai, OUYANG Junlin. Magnetic Induction Tomography of IntraCerebral Hemorrhage Based on Improved Newton-Raphson Algorithm[J]. Journal of Electronics & Information Technology, 2023, 45(12): 4477-4488. doi: 10.11999/JEIT221393

Citation:

CAO Honggui, YE Bo, JIANG Ying, LUO Siqi, CAO Zhongkai, OUYANG Junlin. Magnetic Induction Tomography of IntraCerebral Hemorrhage Based on Improved Newton-Raphson Algorithm[J]. Journal of Electronics & Information Technology, 2023, 45(12): 4477-4488. doi: 10.11999/JEIT221393

Citation:

CAO Honggui, YE Bo, JIANG Ying, LUO Siqi, CAO Zhongkai, OUYANG Junlin. Magnetic Induction Tomography of IntraCerebral Hemorrhage Based on Improved Newton-Raphson Algorithm[J]. Journal of Electronics & Information Technology, 2023, 45(12): 4477-4488. doi: 10.11999/JEIT221393

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Magnetic Induction Tomography of IntraCerebral Hemorrhage Based on Improved Newton-Raphson Algorithm

doi: 10.11999/JEIT221393

1.
Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, China
2.
Yunnan Key Laboratory of Artificial Intelligence, Kunming 650500, China

Funds: The National Natural Science Foundation of China(62203195), The Young and Middle-Aged Academic and Technical Leaders Reserve Talents Project of Yunnan Province (202305AC160062), The Yunnan College Students' Innovation and Entrepreneurship Training Program (2021106740015)

Received Date: 2022-11-07
Rev Recd Date: 2023-03-13

Available Online: 2023-03-21

Publish Date: 2023-12-26

Abstract

Abstract

To solve the problems of over-simplified positive problem model, low image reconstruction quality, low algorithm convergence efficiency, large artifacts between lesion and background, and long time consuming in IntraCerebral Hemorrhage (ICH) Magnetic Induction Tomography (MIT), an improved Newton-Raphson (NR) algorithm for MIT of intracerebral hemorrhage is proposed. The calculation results of Linear Back Projection (LBP) algorithm are used as the iterative initial values of the improved NR algorithm, the adaptive acceleration penalty term and the L2 norm penalty term are added to the objective function to improve the efficiency of each iteration of the algorithm and reduce the artifacts of the reconstructed image. A real three-dimensional brain model including scalp, skull, cerebrospinal fluid and brain parenchyma is constructed by Comsol Multiphysics. The phase difference detection value and sensitivity matrix are simulated and calculated for subsequent image reconstruction. The proposed improved NR algorithm and five image reconstruction algorithms are used to perform magnetic induction tomography on intracerebral hemorrhage with blood loss of 24 ml, 14 ml and 2 ml at three locations, respectively. The experimental results show that the proposed algorithm has higher quality of reconstructed images than the other five algorithms. The average imaging time is only 1/3 of the NR algorithm. The higher quality image is reconstructed with fewer iterations, the image reconstruction of 2 ml intracerebral hemorrhage can be realized, which provides a new and effective algorithm for MIT detection of intracerebral hemorrhage.
- IntraCerebral Hemorrhage (ICH),
- Magnetic Induction Tomography (MIT),
- Finite element analysis,
- Newton-Raphson algorithm,
- Image reconstruction

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References(30)

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