MIT中灵敏度矩阵的聚类优化

针对灵敏度矩阵的几何差异性问题，提出了一种基于聚类优化的灵敏度矩阵方法.首先，分析了灵敏度矩阵的几何差异性对MIT图像质量的影响;然后，基于几何差异性对灵敏度矩阵的向量进行聚类分组，应用能量函数对分组后的灵敏度向量赋予不同权值，构造一种聚类优化的灵敏度矩阵;最后，应用优化后的灵敏度矩阵，通过线性反投影算法和牛顿-拉夫逊迭代算法进行MIT图像重建.实验结果表明:采用聚类优化的灵敏度矩阵，使线性反投影算法的均方误差降低26%以上，图像相关系数提高10%以上， 使牛顿-拉夫逊迭代算法的均方误差降低5%以上，相关系数提高4%以上，证明了所提方法的有效性.

Clustering Optimization of Sensitivity Matrix in MIT

Aiming at the geometric difference of sensitivity matrix， a clustering-based method for optimizing sensitivity matrix was proposed. Firstly， the influence of geometrical difference of sensitivity matrix on MIT image quality was analyzed. Then， based on the geometric difference， the vector of the sensitivity matrix was clustered， and the energy function was applied to assign different weights to the grouped sensitivity vector， and a clustering optimization sensitivity matrix was constructed. Finally， the optimized sensitivity matrix was used to reconstruct the MIT image with the linear back projection algorithm and the Newton-Raphson(NR) iterative algorithm. Experimental results showed that with the sensitivity matrix of clustering optimization， the mean square error of the linear back projection algorithm is reduced by more than 26%， the image correlation coefficient is increased by more than 10%， the mean square error of NR iterative algorithm is decreased by more than 5%， and the correlation coefficient is increased by more than 4%， which proves the effectiveness of the proposed method.