Siemens Gammasonics Inc., Hoffman Estates, IL.
IEEE Trans Med Imaging. 1991;10(4):621-8. doi: 10.1109/42.108598.
An iterative reconstruction method which minimizes the effects of ill-conditioning is discussed. Based on the modified Newton-Raphson algorithm, a regularization method which integrates prior information into the image reconstruction was developed. This improves the conditioning of the information matrix in the modified Newton-Raphson algorithm. Optimal current patterns were used to obtain voltages with maximal signal-to-noise ratio (SNR). A complete finite element model (FEM) was used for both the internal and the boundary electric fields. Reconstructed images from phantom data show that the use of regularization optimal current patterns, and a complete FEM model improves image accuracy. The authors also investigated factors affecting the image quality of the iterative algorithm such as the initial guess, image iteration, and optimal current updating.
讨论了一种最小化病态影响的迭代重建方法。基于修正牛顿-拉普森算法，开发了一种将先验信息集成到图像重建中的正则化方法。这改善了修正牛顿-拉普森算法中信息矩阵的条件数。最优电流模式用于获得具有最大信噪比（SNR）的电压。完整的有限元模型（FEM）用于内部和边界电场。来自幻影数据的重建图像表明，使用正则化最优电流模式和完整的 FEM 模型可以提高图像精度。作者还研究了影响迭代算法图像质量的因素，如初始猜测、图像迭代和最优电流更新。
