Yang Lin, Xu Canhua, Dai Meng, Fu Feng, Shi Xuetao, Dong Xiuzhen
Physiol Meas. 2016 Dec;37(12):2317-2335. doi: 10.1088/1361-6579/37/12/2317. Epub 2016 Nov 29.
Multi-frequency electrical impedance tomography (MFEIT) reconstructs the image of conductivity inside the human body based on the dependence of tissue conductivity on frequency. As there exist differences in the conductivity over frequency between blood, ischemic cortical tissue and normal cortical tissue, MFEIT has potential application in the detection of acute stroke. However, because the conductivity distribution of the human head is highly inhomogeneous and the conductivities of normal head tissue and stroke lesion tissue both change with frequency, the anomaly and normal head tissues are often mixed together in the reconstructed image, which makes it difficult to discern the anomaly. Here we present a spectral decomposition frequency-difference (SD-FD) imaging algorithm in an attempt to address this issue: firstly, we reconstruct so-called EIT spectral images according to the conductivity spectra of tissues; secondly, we obtain the EIT image of the anomaly from the spectral images by using independent component analysis. The results show that the proposed algorithm is capable of detecting the anomaly in a numerical head phantom, as well as in a realistic human head tank with frequency-dependent and heterogeneous conductivities distribution. The proposed SD-FD algorithm may support MFEIT use for human stroke imaging in the future.
多频电阻抗断层成像（MFEIT）基于组织电导率对频率的依赖性来重建人体内部的电导率图像。由于血液、缺血性皮质组织和正常皮质组织在不同频率下的电导率存在差异，MFEIT在急性中风检测方面具有潜在应用价值。然而，由于人体头部的电导率分布高度不均匀，且正常头部组织和中风病变组织的电导率均随频率变化，在重建图像中异常组织和正常头部组织常常混合在一起，这使得异常情况难以辨别。在此，我们提出一种频谱分解频差（SD-FD）成像算法来试图解决这一问题：首先，根据组织的电导率频谱重建所谓的EIT频谱图像；其次，通过独立成分分析从频谱图像中获取异常情况的EIT图像。结果表明，所提出的算法能够在数值头部模型以及具有频率依赖性和非均匀电导率分布的真实人体头部模型中检测到异常情况。所提出的SD-FD算法未来可能支持MFEIT用于人体中风成像。
