Imaging of intracerebral hemorrhage with adaptive genetic algorithm in brain electrical impedance tomography

Intracerebral hemorrhage refers to bleeding caused by the spontaneous rupture of blood vessels. Accurate diagnosis of hemorrhage is vital in the treatment of a patient. As a new medical imaging technique, electrical impedance tomography (EIT) is able to offer images of conductivity distribution variation caused by pathological change. However, image reconstruction of EIT suffers from the problem of serious ill-posedness. In particular, in brain imaging, irregular and multi-layered head structure together with the low conductivity of the skull further aggravate the problem. In order to address this problem, a new image-reconstruction method is proposed for imaging of hemorrhage in this work. With current solutions solving by a Tikhonov regularization method for the original conductivity distribution, the proposed method enhances the reconstruction quality by introducing an adaptive genetic algorithm. To test the performance of the proposed method, simulation work is conducted. A three-layer head model is established and an inclusion, which simulates hemorrhage, is placed at six different locations in the brain layer. Images reconstructed by the Tikhonov method, Newton-Raphson method and the traditional genetic algorithm are used for comparisons. Quantitative evaluation is also performed. The anti-noise performance of the proposed method is estimated by considering noise with differing signal-to-noise ratios. In addition to simulation, phantom experiments are carried out to further verify the performance of the proposed method. The results show that the proposed method performs well in the reconstruction of simulated intracerebral hemorrhage. With the proposed method, the inclusion can be more accurately reconstructed and the background is much clearer than the other three traditional methods.