以正子電腦斷層掃描造影為基礎的影像重建涉及對從患者組織細胞內放射性藥劑散發之成對光子群的未知量估計；光子群的產生可以用布拉松過程加以描述，而影像重建的困難在於如對各組織細胞間密度的分佈作精確的量化。利用最大可能值法，未知的滿像資訊可得較佳的估計；相較於其他以分析法為理論基礎的其他方法諸如反投射法，最大期望值估計法對於正子電腦斷層掃描造影探供了一個最適合的解決方法。儘管它在醫學診斷上的理想表現，由於其耗費大量電腦運算時間的特性影響了此種造影術在臨床上的大量使用。本研研報告提出一個在實際應用環境上可行的平行處理模式，針對最大期望值估計法的循環計算特性，以實驗證明如何在循環內如循環間加速影像重建的新方式。

　　　　　Image reconstruciton using positron emission tomography (PET) involves estgimating an unknown number of photon pairs emitted from the radipharmaceuticals within the tissues of the patient's body. The generation of the photons can be described as a Poisson process and the difficulty of image reconstructioninvolves approximating the parameter of the tissue density distribution function. Using the Maximum Likelihood (ML) for mulation, a better estimate can be made for the unknown image information. Expectation Maximization (EM) is an iterative method which adopts the ML reconstruction especially when compared to analytic methods like the filtered back-projection method (FBP). Despite the prognostication of its promises of better medical diagnoses, EM reconstruction has not been widely used in clinical environments because of its long image reconstruction time and large memory requirement. A parallelization scheme of the iterative method has been suggested to make the reconstruction feasible in applied settings. This research builds upon experimentation that demonstrated promising results in speeding up the algorithm in and between iterations using distributed-memory machines.