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Modelling and Validation of Diffuse Reflectance of the Adult Human Head for fNIRS: Scalp sub-layers Definition

机译:fNIRS的成人人头漫反射模型和验证:头皮亚层定义

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Accurate estimation of brain haemodynamics parameters such as cerebral blood flow and volume as well as oxygen consumption i.e. metabolic rate of oxygen, with funcional near infrared spectroscopy (fNIRS) requires precise characterization of light propagation through head tissues. An anatomically realistic forward model of the human adult head with unprecedented detailed specification of the 5 scalp sublayers to account for blood irrigation in the connective tissue layer is introduced. The full model consists of 9 layers, accounts for optical properties ranging from 750nm to 950nm and has a voxel size of 0.5mm. The whole model is validated comparing the predicted remitted spectra, using Monte Carlo simulations of radiation propagation with 108 photons, against continuous wave (CW) broadband fNIRS experimental data. As the true oxy- and deoxy-hemoglobin concentrations during acquisition are unknown, a genetic algorithm searched for the vector of parameters that generates a modelled spectrum that optimally fits the experimental spectrum. Differences between experimental and model predicted spectra was quantified using the Root mean square error (RMSE). RMSE was 0.071 ?0.004, 0.108 ?0.018 and 0.235 ?.015 at 1, 2 and 3cm interoptode distance respectively. The parameter vector of absolute concentrations of haemoglobin species in scalp and cortex retrieved with the genetic algorithm was within histologically plausible ranges. The new model capability to estimate the contribution of the scalp blood flow shall permit incorporating this information to the regularization of the inverse problem for a cleaner reconstruction of brain hemodynamics.
机译:使用功能性近红外光谱(fNIRS)来准确估计脑血流动力学参数,例如脑血流量和体积以及耗氧量,即氧气的代谢率,需要精确表征通过头部组织传播的光。介绍了人类头颅的解剖学逼真正向模型,该模型具有史无前例的5个头皮子层详细说明,可以说明结缔组织层中的血液冲洗。完整模型由9层组成,具有750nm至950nm的光学特性,体素大小为0.5mm。使用蒙特卡罗模拟法对108个光子的辐射传播,与连续波(CW)宽带fNIRS实验数据进行比较,验证了整个模型的预测发射光谱。由于在采集过程中真正的氧合血红蛋白和脱氧血红蛋白浓度是未知的,因此遗传算法搜索了参数向量,从而生成了最适合实验光谱的建模光谱。实验和模型预测光谱之间的差异使用均方根误差(RMSE)进行了量化。在1、2和3cm的光电二极管距离处,RMSE分别为0.071〜0.004、0.108〜0.018和0.235〜.015。用遗传算法检索的头皮和皮层中血红蛋白种类绝对浓度的参数向量在组织学上合理的范围内。估计头皮血流贡献的新模型功能应允许将此信息合并到反问题的正则化中,以更清晰地重建脑血流动力学。

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