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1.
J. biomed. eng ; Sheng wu yi xue gong cheng xue za zhi;(6): 401-408, 2023.
Artículo en Zh | WPRIM | ID: wpr-981556

RESUMEN

In transcranial magnetic stimulation (TMS), the conductivity of brain tissue is obtained by using diffusion tensor imaging (DTI) data processing. However, the specific impact of different processing methods on the induced electric field in the tissue has not been thoroughly studied. In this paper, we first used magnetic resonance image (MRI) data to create a three-dimensional head model, and then estimated the conductivity of gray matter (GM) and white matter (WM) using four conductivity models, namely scalar (SC), direct mapping (DM), volume normalization (VN) and average conductivity (MC), respectively. Isotropic empirical conductivity values were used for the conductivity of other tissues such as the scalp, skull, and cerebrospinal fluid (CSF), and then the TMS simulations were performed when the coil was parallel and perpendicular to the gyrus of the target. When the coil was perpendicular to the gyrus where the target was located, it was easy to get the maximum electric field in the head model. The maximum electric field in the DM model was 45.66% higher than that in the SC model. The results showed that the conductivity component along the electric field direction of which conductivity model was smaller in TMS, the induced electric field in the corresponding domain corresponding to the conductivity model was larger. This study has guiding significance for TMS precise stimulation.


Asunto(s)
Estimulación Magnética Transcraneal , Imagen de Difusión Tensora , Conductividad Eléctrica , Electricidad , Cuero Cabelludo
2.
Artículo en Zh | WPRIM | ID: wpr-954336

RESUMEN

Surgery is the mainstay of treatment for early lung cancers, but there is still a risk of recurrence and metastasis after surgery. With the advancement of molecular biology and detection methods, detecting the level of postoperative peripheral blood minimal residual disease (MRD) in patients can dynamically monitor recurrence and determine prognosis. Due to the wide variety of MRD detection methods, uneven detection power, lack of uniform standards and prospective study validation, clinical application is still controversial. The further development of MRD detection for early stage cell lung cancer still needs technical progress, standardized detection criteria and credible clinical data.

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