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用于模拟肿瘤治疗电场(TTFields)的简化逼真人体头部模型。

Simplified realistic human head model for simulating Tumor Treating Fields (TTFields).

作者信息

Wenger Cornelia, Bomzon Ze'ev, Salvador Ricardo, Basser Peter J, Miranda Pedro C

出版信息

Annu Int Conf IEEE Eng Med Biol Soc. 2016 Aug;2016:5664-5667. doi: 10.1109/EMBC.2016.7592012.

Abstract

Tumor Treating Fields (TTFields) are alternating electric fields in the intermediate frequency range (100-300 kHz) of low-intensity (1-3 V/cm). TTFields are an anti-mitotic treatment against solid tumors, which are approved for Glioblastoma Multiforme (GBM) patients. These electric fields are induced non-invasively by transducer arrays placed directly on the patient's scalp. Cell culture experiments showed that treatment efficacy is dependent on the induced field intensity. In clinical practice, a software called NovoTalTM uses head measurements to estimate the optimal array placement to maximize the electric field delivery to the tumor. Computational studies predict an increase in the tumor's electric field strength when adapting transducer arrays to its location. Ideally, a personalized head model could be created for each patient, to calculate the electric field distribution for the specific situation. Thus, the optimal transducer layout could be inferred from field calculation rather than distance measurements. Nonetheless, creating realistic head models of patients is time-consuming and often needs user interaction, because automated image segmentation is prone to failure. This study presents a first approach to creating simplified head models consisting of convex hulls of the tissue layers. The model is able to account for anisotropic conductivity in the cortical tissues by using a tensor representation estimated from Diffusion Tensor Imaging. The induced electric field distribution is compared in the simplified and realistic head models. The average field intensities in the brain and tumor are generally slightly higher in the realistic head model, with a maximal ratio of 114% for a simplified model with reasonable layer thicknesses. Thus, the present pipeline is a fast and efficient means towards personalized head models with less complexity involved in characterizing tissue interfaces, while enabling accurate predictions of electric field distribution.

摘要

肿瘤治疗电场(TTFields)是低强度(1 - 3 V/cm)的中频范围(100 - 300 kHz)的交变电场。TTFields是一种针对实体瘤的抗有丝分裂治疗方法,已被批准用于多形性胶质母细胞瘤(GBM)患者。这些电场由直接放置在患者头皮上的换能器阵列非侵入性地诱导产生。细胞培养实验表明,治疗效果取决于诱导场强。在临床实践中,一种名为NovoTalTM的软件利用头部测量来估计最佳阵列放置位置,以最大限度地将电场传递到肿瘤。计算研究预测,当使换能器阵列适应肿瘤位置时,肿瘤的电场强度会增加。理想情况下,可以为每个患者创建个性化的头部模型,以计算特定情况下的电场分布。因此,可以从场计算而非距离测量中推断出最佳换能器布局。尽管如此,创建患者的真实头部模型既耗时又常常需要用户交互,因为自动图像分割容易失败。本研究提出了一种创建由组织层凸包组成的简化头部模型的初步方法。该模型能够通过使用从扩散张量成像估计的张量表示来考虑皮质组织中的各向异性电导率。在简化和真实头部模型中比较了感应电场分布。在真实头部模型中,大脑和肿瘤中的平均场强通常略高,对于具有合理层厚度的简化模型,最大比率为114%。因此,当前的流程是一种快速有效的方法,可用于创建个性化头部模型,在表征组织界面时涉及的复杂性较低,同时能够准确预测电场分布。

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