2018 Fiscal Year Annual Research Report
高分解能EISTとアルブミン輸送モデルによる早期リンパ浮腫診断法と予測法の確立
| Project/Area Number |
18F18060
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| Research Institution | Chiba University |
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Principal Investigator |
武居 昌宏 千葉大学, 大学院工学研究院, 教授 (90277385)
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| Co-Investigator(Kenkyū-buntansha) |
BAIDILLAH MARLIN 千葉大学, 大学院工学研究院, 外国人特別研究員
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| Project Period (FY) |
2018-04-25 – 2020-03-31
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| Keywords | electrically / equivalent / phantom |
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| Outline of Annual Research Achievements |
1.The electrically equivalent upper arm phantom of c-EIST to detect the 2D accumulation albumin distribution has been developed.
2.In order to reconstruct the albumin accumulation distribution, it is needed to eliminate the systematic error caused by the variant of unknown contact impedance. We proposed a novel method to compensate the volatile-distributed current as the systematic error due to variance of the unknown contact impedance.
3.The electrical spectroscopy imaging has been conducted by involving some healthy-volunteers. This is a part of clinical test studies, and the reconstructed image is evaluated to extract of adipose tissue segmentation and physiological condition change. The reconstructed image of electrical spectroscopy imaging are a spatio-temporal distribution.
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| Current Status of Research Progress |
Current Status of Research Progress
2: Research has progressed on the whole more than it was originally planned.
Reason
The biomechanically phantom is not established yet. This is due to the difficulty to find the most optimum materials that suitable with the electrically equivalent phantom. However, in order to predict the biofluid mechanics phenomenon for establishing the Albumin Transport Model, we will evaluate with other alternative method the we will evaluate on the second year of research.
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| Strategy for Future Research Activity |
This year to implement of c-EIST for measurement of hydrodynamic properties of early and middle stage Lymphedema patients or normal volunteer with changing the physiological condition.
Research plans are:
1.To simulate at macro scale of hydrodynamic properties of limb by using numerical simulation.
2.To analyze the c-EIST reconstructed images by using deep learning and stochastic phenomenon.
3.To develop the Albumin transport model as a results of deep learning analysis by using an artificial of differential pressure.
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