Abascal Juan-Felipe P J, Arridge Simon R, Atkinson David, Horesh Raya, Fabrizi Lorenzo, De Lucia Marzia, Horesh Lior, Bayford Richard H, Holder David S
Laboratoire des Signaux et Systèmes, Supélec, Gif-Sur-Yvette, France.
Neuroimage. 2008 Nov 1;43(2):258-68. doi: 10.1016/j.neuroimage.2008.07.023. Epub 2008 Jul 23.
Electrical Impedance Tomography (EIT) is an imaging method which enables a volume conductivity map of a subject to be produced from multiple impedance measurements. It has the potential to become a portable non-invasive imaging technique of particular use in imaging brain function. Accurate numerical forward models may be used to improve image reconstruction but, until now, have employed an assumption of isotropic tissue conductivity. This may be expected to introduce inaccuracy, as body tissues, especially those such as white matter and the skull in head imaging, are highly anisotropic. The purpose of this study was, for the first time, to develop a method for incorporating anisotropy in a forward numerical model for EIT of the head and assess the resulting improvement in image quality in the case of linear reconstruction of one example of the human head. A realistic Finite Element Model (FEM) of an adult human head with segments for the scalp, skull, CSF, and brain was produced from a structural MRI. Anisotropy of the brain was estimated from a diffusion tensor-MRI of the same subject and anisotropy of the skull was approximated from the structural information. A method for incorporation of anisotropy in the forward model and its use in image reconstruction was produced. The improvement in reconstructed image quality was assessed in computer simulation by producing forward data, and then linear reconstruction using a sensitivity matrix approach. The mean boundary data difference between anisotropic and isotropic forward models for a reference conductivity was 50%. Use of the correct anisotropic FEM in image reconstruction, as opposed to an isotropic one, corrected an error of 24 mm in imaging a 10% conductivity decrease located in the hippocampus, improved localisation for conductivity changes deep in the brain and due to epilepsy by 4-17 mm, and, overall, led to a substantial improvement on image quality. This suggests that incorporation of anisotropy in numerical models used for image reconstruction is likely to improve EIT image quality.
电阻抗断层成像(EIT)是一种成像方法,它能够通过多次阻抗测量生成受试者的体积电导率图。它有潜力成为一种便携式非侵入性成像技术,特别适用于脑功能成像。准确的数值正向模型可用于改善图像重建,但到目前为止,这些模型都采用了组织电导率各向同性的假设。由于人体组织,尤其是头部成像中的白质和颅骨等组织,具有高度的各向异性,因此可以预期这种假设会引入不准确性。本研究的目的是首次开发一种方法,将各向异性纳入头部EIT的正向数值模型中,并在对一个人类头部实例进行线性重建的情况下,评估由此带来的图像质量提升。通过结构MRI生成了一个逼真的成人人脑有限元模型(FEM),该模型包含头皮、颅骨、脑脊液和脑的部分。根据同一受试者的扩散张量MRI估计脑的各向异性,并根据结构信息近似颅骨的各向异性。提出了一种将各向异性纳入正向模型及其在图像重建中的应用方法。通过生成正向数据,然后使用灵敏度矩阵方法进行线性重建,在计算机模拟中评估重建图像质量的提升。对于参考电导率,各向异性和各向同性正向模型之间的平均边界数据差异为50%。在图像重建中使用正确的各向异性有限元模型,而不是各向同性模型,校正了在海马体中成像10%电导率下降时24毫米的误差,将脑深部因癫痫导致的电导率变化的定位提高了4 - 17毫米,总体上导致图像质量有了显著提升。这表明在用于图像重建的数值模型中纳入各向异性可能会提高EIT图像质量。