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DC Field | Value | Language |
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dc.contributor.author | Ouypornkochagorn T. | |
dc.contributor.author | Ouypornkochagorn S. | |
dc.date.accessioned | 2021-04-05T03:02:59Z | - |
dc.date.available | 2021-04-05T03:02:59Z | - |
dc.date.issued | 2019 | |
dc.identifier.issn | 906964 | |
dc.identifier.other | 2-s2.0-85064503315 | |
dc.identifier.uri | https://ir.swu.ac.th/jspui/handle/123456789/12364 | - |
dc.identifier.uri | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85064503315&doi=10.1007%2fs10439-019-02254-9&partnerID=40&md5=9dd231176d8a4eb2bd492b00d679ff44 | |
dc.description.abstract | The conductivity of head tissues was noninva-sively estimated using electrical impedance tomography technique. Instead of using conventional unconstrained optimization method to estimate the conductivities, a constrained method with the scaled-logistic function was employed to improve the very high sensitivity of the skull region resulting in accuracy and robustness improvement. Estimation of five conductivities i.e. scalp, skull, cerebrospinal fluid (CSF), grey matter (GM), and white matter (WM) conductivity was investigated by simulation on random and low-value initial guesses. Simulation results showed that the performance of the unconstrained method depended directly to the difference between the exact skull conductivity value and the initial guess value of the skull conductivity. However, the approached constrained method was independent of the guess selection. It can reduce the sensitivity of the skull region by 126 times and reduce the condition number of the sensitivity matrix by 13–17 times. The estimation resulted in only positive and in-range of reported conductivity values. The estimation error of the skull conductivity decreased by 15% and the robustness increased by 2 times. However, the estimation of the CSF, the WM, and the GM may be not reliable due to the very low sensitivity of these regions in both methods. © Springer. All rights reserved. | |
dc.subject | Cerebrospinal fluid | |
dc.subject | Electric impedance | |
dc.subject | Electric impedance measurement | |
dc.subject | Electric impedance tomography | |
dc.subject | Number theory | |
dc.subject | Tissue | |
dc.subject | Bio-impedance | |
dc.subject | Bound constrained optimization | |
dc.subject | Cerebro spinal fluids | |
dc.subject | Electrical impe dance tomography (EIT) | |
dc.subject | Electrical impedance tomography | |
dc.subject | Initial guess | |
dc.subject | Tissue conductivity | |
dc.subject | Unconstrained optimization | |
dc.subject | Constrained optimization | |
dc.subject | article | |
dc.subject | cerebrospinal fluid | |
dc.subject | computer assisted impedance tomography | |
dc.subject | conductance | |
dc.subject | gray matter | |
dc.subject | human tissue | |
dc.subject | in vivo study | |
dc.subject | scalp | |
dc.subject | simulation | |
dc.subject | skull | |
dc.subject | white matter | |
dc.subject | adult | |
dc.subject | biological model | |
dc.subject | computer simulation | |
dc.subject | human | |
dc.subject | impedance | |
dc.subject | Adult | |
dc.subject | Cerebrospinal Fluid | |
dc.subject | Computer Simulation | |
dc.subject | Electric Impedance | |
dc.subject | Gray Matter | |
dc.subject | Humans | |
dc.subject | Models, Biological | |
dc.subject | Scalp | |
dc.subject | Skull | |
dc.subject | White Matter | |
dc.title | In vivo estimation of head tissue conductivities using bound constrained optimization | |
dc.type | Article | |
dc.rights.holder | Scopus | |
dc.identifier.bibliograpycitation | Annals of Biomedical Engineering. Vol 47, No.7 (2019), p.1575-1583 | |
dc.identifier.doi | 10.1007/s10439-019-02254-9 | |
Appears in Collections: | Scopus 1983-2021 |
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