Modified tuna swarm optimization algorithm for brain stroke imaging with electrical impedance tomography

Stroke is a common disease characterized by high disability rate and high mortality rate. Accurate detection and continuous monitoring are vital for the treatment of stroke. As a promising medical imaging technique, electrical impedance tomography (EIT) is able to provide an alternative for brain imaging. With this technique, conductivity distribution variation caused by pathological change can be visualized. However, image reconstruction is a severely ill-posed inverse problem. Particularly in brain imaging, irregular and multi-layered head structure along with low-conductivity skull further aggravate the challenge for accurate reconstruction. To solve this problem, a novel image-reconstruction method based on modified tuna swarm optimization is proposed for visualizing the conductivity distribution in brain EIT. To evaluate the performance of the proposed method, extensive simulations are carried out on a three-layer head model. The anti-noise performance of the proposed method is estimated by considering noise with different signal-to-noise ratios. In addition to simulation, phantom experiments are conducted to further verify the effectiveness of the proposed method. Both reconstructed images and quantitative evaluations demonstrate that the proposed approach performs well in the reconstruction of simulated intracerebral hemorrhage and secondary ischemia. This work would offer an alternative for accurate reconstruction in medical imaging based on EIT.