| Project/Area Number |
07409002
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| Research Category |
Grant-in-Aid for Scientific Research (A)
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Research Field |
広領域
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| Research Institution | Tokyo Institute of Technology |
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Principal Investigator |
KOSUGI Yukio Tokyo Institute of Technology, Interdisciplinary Graduate School of Science and Engineering, Associate Professor, 大学院・総合理工学研究科, 助教授 (30108237)
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| Co-Investigator(Kenkyū-buntansha) |
KAMEYAMA Keisuke Tokyo Institute of Technology, Interdisciplinary Graduate School of Science and, 大学院・総合理工学研究科, 助手 (40242309)
TAKEMAE Tadashi Shizuoka University, Faculty of Eng., Associate Professor, 工学部, 助教授 (20115356)
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| Project Period (FY) |
1995 – 1996
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| Project Status |
Completed (Fiscal Year 1996)
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| Budget Amount *help |
¥10,500,000 (Direct Cost: ¥10,500,000)
Fiscal Year 1996: ¥500,000 (Direct Cost: ¥500,000)
Fiscal Year 1995: ¥10,000,000 (Direct Cost: ¥10,000,000)
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| Keywords | impedance tomography / neural networks / magnetically controlled tetrapolar method / segmentation / dynamic regularization / ill-posed problems / blood pulsatile wave / inverse problems / MR援用デコンボリューション / Impedance Tomagraphy / Neural Networks / Blood Flow / Inverse Problcins / Tetrapolar Method / Imaging |
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| Research Abstract |
Electrical impedance tomography (EIT) is capable of visualizing the electrical impedance distribution and its changes related to the anatomical disorders or physiological activities in the living body, to be applied for monitoring the biological conditions such as blood-flow changes in the brain, swellings and urinary bladder content etc.
In this research we developed neural network methods to solve the inverse problems in estimating the conductivity distribution from the superficial potential distribution measured via a set of electrodes densely placed on the body surface. For improving the resolution of the conductivity profile, MR-based deconvolution will be effective used when we want to identify the localized blood-flow increment in the brain ; in this case ill-posedness of the inverse problem can be well improved by applying the dynamic regularization technique. We also developed a GA method to select the best indices for MR segmentation, which is a key-technology for incorporating the MR data to the deconvolution network after segmenting the CSF,the gray-matter and the white-matter of the brain automatically.
Another topic, we investigated is the use of magnetically controlled tetrapolar method to monitor the pulsatile impedance changes in the brain : we experimentally tested the system to prove the effectiveness of the method in estimating the blood-glow increment during the mental-loading tests.
We are now planning to improve the resolution of magnetically controlled tetrapolar method by deconvolving the series of data obtained in the measurement with the magnetic field continuously shifted.
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