A Noninvasive and Dispersive Framework for Estimating Nonuniform Conductivity of Brain Tumor in Patient-Specific Head Models

IF 4.5 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC
IEEE journal of microwaves Pub Date : 2026-03-01 Epub Date: 2026-04-22 DOI:10.1109/JMW.2026.3681237
Yoshiki Kubota;Yosuke Nagata;Manabu Tamura;Akimasa Hirata
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引用次数: 0

Abstract

We propose a noninvasive and dispersive framework for estimating the spatially nonuniform conductivity of brain tumors using MR images. The method consists of two components: (i) voxel-wise assignment of tumor conductivity based on reference values fitted to the Cole–Cole model using empirical data from the literature and (ii) fine-tuning of a deep learning model pretrained on healthy participants A total of 67 cases, comprising both healthy participants and tumor patients and including 9,806 paired T1- and T2-weighted MR images, were used for training and evaluation. The proposed method successfully estimated patient-specific conductivity maps, exhibiting smooth spatial variations that reflected tissue characteristics, such as edema, necrosis, and rim-associated intensity gradients observed in T1- and T2-weighted MR images. At 10 kHz, case-wise mean conductivity values varied across patients, ranging from 0.132 to 0.512 S/m in the rim (defined as the region within 2 mm of the tumor boundary), from 0.132 to 0.608 S/m in the core (the area inside the rim), and from 0.141 to 0.542 S/m in the entire tumor. Electromagnetic simulations for transcranial magnetic stimulation in individualized head models showed substantial differences in intratumoral field distributions between uniform assignments and the proposed nonuniform maps. Furthermore, the proposed framework enables voxel-wise conductivity mapping across multiple frequencies (10 kHz, 1 MHz, and 100 MHz) by combining spatially resolved conductivity estimation with a physics-informed dispersive scaling model. This framework supports accurate whole-brain conductivity estimation by incorporating both individual anatomical structures and tumor-specific characteristics. Collectively, these results advance patient-specific EM modeling for tumor-bearing brains and lay the groundwork for subsequent validation in higher-frequency electromagnetic applications.
一种非侵入性和分散性的框架,用于估计患者特异性头部模型中脑肿瘤的非均匀电导率
我们提出了一种非侵入性和分散的框架,用于使用MR图像估计脑肿瘤的空间非均匀电导率。该方法由两部分组成:(i)基于参考值拟合到Cole-Cole模型的肿瘤电导率的体素分配,使用文献中的经验数据;(ii)对健康参与者预训练的深度学习模型进行微调。共有67例,包括健康参与者和肿瘤患者,包括9,806对T1和t2加权MR图像,用于训练和评估。所提出的方法成功地估计了患者特异性电导率图,显示出平滑的空间变化,反映了组织特征,如水肿、坏死和在T1和t2加权MR图像中观察到的边缘相关强度梯度。在10 kHz时,不同患者的病例平均电导率值不同,边缘(定义为肿瘤边界2 mm内的区域)的电导率范围为0.132至0.512 S/m,核心(边缘内区域)的电导率范围为0.132至0.608 S/m,整个肿瘤的电导率范围为0.141至0.542 S/m。个体化头部模型经颅磁刺激的电磁模拟显示,均匀分配和非均匀映射在肿瘤内场分布上存在实质性差异。此外,所提出的框架通过将空间分辨电导率估计与物理信息色散缩放模型相结合,实现了跨多个频率(10 kHz, 1 MHz和100 MHz)的体素电导率映射。该框架通过结合个体解剖结构和肿瘤特异性特征,支持准确的全脑电导率估计。总的来说,这些结果推进了肿瘤大脑的患者特异性EM建模,并为后续高频电磁应用的验证奠定了基础。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
10.70
自引率
0.00%
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0
审稿时长
8 weeks
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