The Nonlinear Variation Regularization Algorithm for the Magnetic Resonance Electrical Impedance Tomography

摘要

The nonlinear variation regularization algorithm (NVRA) is an effective method to enhance the contrast and robustness of the reconstruction in medical imaging. In order to improve the reconstruction quality the variation regularization is introduced to the nonlinear algorithm based on one component of the magnetic flux density by injecting one current. Firstly, we propose a novel algorithm for magnetic resonance electrical impedance tomography (MREIT) using NVRA, and clarify the implementation of this algorithm. Secondly, we analyze the performance of the proposed nonlinear algorithm and the linear sensitivity method with noisy data in the phantom models. Finally, in the case of 0.36 T low field intensity magnetic resonance scanner, we present the method for reducing the electrode model error, and evaluate the performance of two reconstruction algorithms in the agar gel model. The results indicate that the NVRA is able to improve the reconstruction quality with sharp contrast and more robust to noise in comparison to the sensitivity method. In addition, this study shows that with just one current injection and one component of the magnetic flux density we can obtain a high quality imaging, which promotes the MREIT in clinical application.