ABSTRACT Median root prior expectation maximization (MRPEM) algorithm that belongs to the Bayesian iterative approximation is used in passive gamma emission tomography (GET) to reconstruct passive gamma emitter distribution. The algorithm converges slowly and may involve iterations of 50–200. Fast processing of the algorithm was integrated into MRPEM to accelerate the convergence. Then, the integrated MRPEM reconstructed a passive distribution of gamma emissions within a mock-up boiling water reactor (BWR) assembly. It was found that the reconstructions in GET using the integrated MRPEM could result in a 20% reduction in the mean absolute error (MAE) compared to the standard MRPEM.

中文翻译：

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被动伽马射线断层扫描中贝叶斯迭代逼近的快速重建

摘要 属于贝叶斯迭代近似的中值根先验期望最大化 (MRPEM) 算法用于被动伽马发射断层扫描 (GET) 以重建被动伽马发射器分布。该算法收敛缓慢，可能涉及 50-200 次迭代。该算法的快速处理被集成到 MRPEM 中以加速收敛。然后，集成的 MRPEM 重建了模拟沸水反应堆 (BWR) 组件内伽马排放的被动分布。结果发现，与标准 MRPEM 相比，使用集成 MRPEM 的 GET 重建可使平均绝对误差 (MAE) 降低 20%。