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.

中文翻译：

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电阻抗断层扫描脑卒中成像的改进金枪鱼群优化算法


脑卒中是一种常见病，具有高致残率、高死亡率的特点。准确检测和持续监测对于中风的治疗至关重要。作为一种有前景的医学成像技术，电阻抗断层扫描（EIT）能够为脑成像提供替代方案。通过该技术，可以将病理变化引起的电导率分布变化可视化。然而，图像重建是一个严重不适定的逆问题。特别是在脑成像中，不规则和多层的头部结构以及低电导率的头骨进一步加剧了精确重建的挑战。为了解决这个问题，提出了一种基于改进金枪鱼群优化的新型图像重建方法，用于可视化大脑 EIT 中的电导率分布。为了评估所提出方法的性能，对三层头部模型进行了广泛的模拟。通过考虑不同信噪比的噪声来估计该方法的抗噪声性能。除了仿真之外，还进行了体模实验以进一步验证所提方法的有效性。重建图像和定量评估都表明所提出的方法在模拟脑出血和继发性缺血的重建中表现良好。这项工作将为基于 EIT 的医学成像精确重建提供一种替代方案。