Intracerebral hemorrhage refers to bleeding caused by 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. Especially in the brain imaging, irregular and multi-layered head structure together with the low conductivity skull further aggravate the problem. In order to address the problem, a new image reconstruction method is proposed for imaging of hemorrhage in this work. With the solution solved by Tikhonov regularization method as the original conductivity distribution, the proposed method tends to enhance the reconstruction quality by introducing 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 Tikhonov method, Newton-Raphson method and traditional Genetic Algorithm method are used for comparisons. Quantitative evaluation is also performed. The anti-noise performance of the proposed method is estimated by considering noise with signal-to-noise ratio of different levels. Aside from the 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 other three traditional methods.
Purpose: Electrical impedance tomography (EIT) has shown its potential in the field of medical imaging. Physiological or pathological variation would cause the change of conductivity. EIT is favorable in reconstructing conductivity distribution inside the detected area. However, due to its ill-posed and nonlinear characteristics, reconstructed images suffer from low spatial resolution.Approach: Tikhonov regularization method is a popular and effective approach for image reconstruction in EIT. Nevertheless, excessive smoothness is observed when reconstruction is conducted based on Tikhonov method. To improve Tikhonov-based reconstruction quality in EIT, an innovative hybrid iterative optimization method is proposed. An efficient alternating minimization algorithm is introduced to solve the optimization problem.Results: To verify image reconstruction performance and anti-noise robustness of the proposed method, a series of simulation work and phantom experiments is carried out. Meanwhile, comparison is made with reconstruction results based on Landweber, Newton-Raphson, and Tikhonov methods. The reconstruction performance is also verified by quantitative comparison of blur radius and structural similarity values which further demonstrates the excellent performance of the proposed method.Conclusions: In contrast to Landweber, Newton-Raphson, and Tikhonov methods, it is found that images reconstructed by the proposed method are more accurate. Even under the impact of noise, the proposed method outperforms comparison methods.
Electrical impedance tomography (EIT) is receiving considerable research interest in a variety of applications. With this technique, it is possible to reconstruct conductivity distribution inside the detected region. Note that reconstruction of conductivity distribution is an ill-posed inverse problem. To deal with this problem, Tikhonov regularization method has been preferred. However, this method suffers from poor reconstruction quality. In this work, a novel approach which combines Tikhonov regularization method with wavelet frame is proposed for image reconstruction of EIT. The proposed method is superior to the popular Tikhonov regularization method in enforcing sparse of the solution and enhancing sharp feature. Simulation work has been conducted to validate the performance of the proposed method. Compared with Landweber method, total variation (TV) regularization method, and Tikhonov regularization method, reconstructions of six different models show that images reconstructed by the proposed method have better quality and are more robust to noise. In addition, image reconstruction is also performed based on phantom experimental data. The results further demonstrate that the proposed method outperforms other three methods since the inclusion has been more accurately reconstructed, and there are almost no artifacts in the reconstructed images.
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