| Project/Area Number |
09450168
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| Research Category |
Grant-in-Aid for Scientific Research (B).
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Research Field |
計測・制御工学
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| Research Institution | Tokyo Institute of Technology |
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Principal Investigator |
KOSUGI Yokio Tokyo Institute of Technology, Frontier Collaborative Research Center, Professor, フロンティア創造共同研究センター, 教授 (30108237)
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| Co-Investigator(Kenkyū-buntansha) |
KAMEYAMA Keisuke University of Tsukuba, Institute of Information Sciences and Electronics, Center for Tsukuba Advanced Research Alliance, Assistant Professor, 電子・情報工学系, 講師 (40242309)
SAECHOUT Vichai Frontier Collaborative Research Center, Research Associate, フロンティア創造共同研究センター, 助手 (10235096)
OMATA Tohru Interdisciplinary Graduate School of Science and Engineering, Associate Professor, 大学院・総合理工学研究科, 助教授 (10262312)
IREN Valova 日本学術振興会, 外国人特別研究員
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| Project Period (FY) |
1997 – 2000
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| Project Status |
Completed (Fiscal Year 2000)
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| Budget Amount *help |
¥11,300,000 (Direct Cost: ¥11,300,000)
Fiscal Year 2000: ¥1,700,000 (Direct Cost: ¥1,700,000)
Fiscal Year 1999: ¥1,600,000 (Direct Cost: ¥1,600,000)
Fiscal Year 1998: ¥2,300,000 (Direct Cost: ¥2,300,000)
Fiscal Year 1997: ¥5,700,000 (Direct Cost: ¥5,700,000)
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| Keywords | equivalent dipole / positron emission tomography / network inversion / dynamic regularization / a priori knowledge / impedance tomography / surface reconstruction / stereo images / ネットワークインバーション / 地理画像 / 遺伝的アルゴリズム / 複号化問題 |
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| Research Abstract |
In this research, we evaluated the effectiveness of dynamic regularization used in solving inverse problems, such as estimating the neural activities from electrical potential distribution on the scalp, or positron emission tomograpy (PET) data. The dynamic regularization is a technique to change the regularization parameter, according to the progress of iteration in operating the network inversion.
In the case of estimating the equivalent current dipoles from evoked potentials, we have realized the estimation of three dipoles simultaneously by introducing the dynamic regularization, for indicating the trace of cortical activities as a locus of a triangle with three dipoles located on the vertices.
In case of the visualization of FDG distribution in the brain, we introduced a hidden Markov model into the FDG transfer model for utilizing the a priori knowledge of temporal continuity of the FDG concentration in cach compartment.
For the case of obtaining conductivity profile, from the electrical potential data, we also made use of the dynamic regularization technique, to stabilize the electrical impedance tomography results.
In addition to the above, we expanded our dynamic regularization technique to the inverse problems of reconstructing a 3-D surface, from a set of stereo images. In this problem, we developed a new network which automatically realizes the corresponding points acquisition as well as generating the smooth surface in the iterative operation.
Through the above examples, we showed the effectiveness of the dynamic regularization built in with the iterative procedure of inverse problem solvers.
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