Abstract: This method which is an adaptive regularized MAP of CT image reconstruction can adaptively choose regularization parameters with making full use of the results of every iteration to update regularization parameters and gets the final reconstruction image through multiple iterations.It simulates a slice of Shepp-Logan model to verify the feasibility of the algorithm.The results show that compared with the traditional methods，ML-EM and FBP，the method is more applicable，can better maintain the image edge information，has a higher signal to noise ratio images and improves the image quality.

Key words: prior distribution, adaptive regularized, image reconstruction

摘要： 提出了一种基于自适应正则最大后验概率估计（MAP）的计算机断层（CT）图像重建方法，该方法可以自适应地选择正则化参数，并充分利用每一次迭代的重建结果的信息，不断对其进行更新，通过多次迭代得到最终的重建图像。通过对头部模型的一个切片进行仿真实验，验证了该方法的可行性。用实际实验数据，与传统的方法、最大似然期望（ML-EM）算法和滤波反投影（FBP）算法相比较，表明该方法适用性较强，能较好地保持图像的边缘信息，而且图像的信噪比较高。

关键词: 先验知识分布, 自适应正则, 图像重建