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A new convergent MAP reconstruction algorithm for emission tomography using ordered subsets and separable surrogates

机译:一种采用有序子集和可分离替代物的放射线层析成像的新式MAP收敛算法

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We investigate a new, fast and provably convergent MAP reconstruction algorithm for emission tomography. The new algorithm, termed C-OSEM has its origin in the alternating algorithm derivation of the well known EM algorithm for emission tomography. In this re-derivation, the complete data explicitly enters the objective function as an unknown variable. While the entire complete data gets updated in each iteration of EM, in C-OSEM the complete data is updated only along ordered subsets. C-OSEM has a straightforward extension to the MAP case especially when using convex, smoothing priors. Unlike RAMLA and BSREM, C-OSEM does not require relaxation parameters to be set at each iteration. We derive the MAP C-OSEM algorithm using the separable surrogate method and anecdotally compare performance with MAP EM and BSREM.
机译:我们研究了一种新的,快速且可证明收敛的MAP重建算法,用于放射线断层摄影。称为C-OSEM的新算法起源于交替的算法,该算法是对放射线断层摄影术的著名EM算法的替代。在此重新推导中,完整数据将作为未知变量显式输入目标函数。在EM的每次迭代中更新完整的完整数据的同时,在C-OSEM中,仅沿有序子集更新完整的数据。 C-OSEM对MAP情况有一个直接的扩展,特别是在使用凸的,平滑的先验条件时。与RAMLA和BSREM不同,C-OSEM不需要在每次迭代时都设置松弛参数。我们使用可分离的替代方法得出MAP C-OSEM算法,并与MAP EM和BSREM进行性能比较。

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