글로벌 연구동향
의학물리학
- 2019년 09월호
[IEEE Trans Med Imaging.] Conductivity Tensor Imaging of In Vivo Human Brain and Experimental Validation Using Giant Vesicle Suspension.경희대 / Nitish Katoch, 우응제*
- 출처
- IEEE Trans Med Imaging.
- 등재일
- 2019 Jul
- 저널이슈번호
- 38(7):1569-1577. doi: 10.1109/TMI.2018.2884440. Epub 2018 Dec 3.
- 내용
Abstract
Human brain mapping of low-frequency electrical conductivity tensors can realize patient-specific volume conductor models for neuroimaging and electrical stimulation. We report experimental validation and in vivo human experiments of a new electrodeless conductivity tensor imaging (CTI) method. From CTI imaging of a giant vesicle suspension using a 9.4-T MRI scanner, the relative error in the reconstructed conductivity tensor image was found to be less than 1.7% compared with the measured value using an impedance analyzer. In vivo human brain imaging experiments of five subjects were followed using a 3-T clinical MRI scanner. With the spatial resolution of 1.87 mm, the white matter conductivity showed considerably more position dependency compared with the gray matter and cerebrospinal fluid (CSF). The anisotropy ratio of the white matter was in the range of 1.96-3.25 with a mean value of 2.43, whereas that of the gray matter was in the range of 1.12-1.19 with a mean value of 1.16. The three diagonal components of the reconstructed conductivity tensors were from 0.08 to 0.27 S/m for the white matter, from 0.20 to 0.30 S/m for the gray matter, and from 1.55 to 1.82 S/m for the CSF. The reconstructed conductivity tensor images exhibited significant inter-subject variabilities in terms of frequency and position dependencies. The high-frequency and low-frequency conductivity values can quantify the total and extracellular water contents, respectively, at every pixel. Their difference can quantify the intracellular water content at every pixel. The CTI method can separately quantify the contributions of ion concentrations and mobility to the conductivity tensor.Author information
Katoch N, Choi BK, Sajib SZK, Lee E, Kim HJ, Kwon OI, Woo EJ.
- 덧글달기
- 이전글 [J Nucl Med.] Generation of PET Attenuation Map for Whole-Body Time-of-Flight 18F-FDG PET/MRI Using a Deep Neural Network Trained with Simultaneously Reconstructed Activity and Attenuation Maps.
- 다음글 [Phys Med Biol.] Deep learning-enabled accurate normalization of reconstruction kernel effects on emphysema quantification in low-dose CT.