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Evaluation of Parallel Level Sets and Bowsher’s Method as Segmentation-Free Anatomical Priors for Time-of-Flight PET Reconstruction

机译:评价平行水平集和Bowsher方法作为飞行时间PET重建的无分割解剖先验

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摘要

In this article, we evaluate Parallel Level Sets (PLS) and Bowsher’s method as segmentation-free anatomical priors for regularized brain PET reconstruction. We derive the proximity operators for two PLS priors and use the EM-TV algorithm in combination with the first order primal-dual algorithm by Chambolle and Pock to solve the non-smooth optimization problem for PET reconstruction with PLS regularization. In addition, we compare the performance of two PLS versions against the symmetric and asymmetric Bowsher priors with quadratic and relative difference penalty function. For this aim, we first evaluate reconstructions of 30 noise realizations of simulated PET data derived from a real PET/MRI acquisition in terms of regional bias and noise. Second, we evaluate reconstructions of a real brain PET/MR data set acquired on a GE Signa TOF PET/MR in a similar way. The reconstructions of simulated and real 3D PET/MRI data show that all priors were superior to post-smoothed Maximum Likelihood Expectation Maximization with ordered subsets (OSEM) in terms of bias-noise characteristics in different regions of interest where the PET uptake follows anatomical boundaries. Our implementation of the asymmetric Bowsher prior showed slightly superior performance compared to the two versions of PLS and the symmetric Bowsher prior. At very high regularization weights, all investigated anatomical priors suffer from the transfer of non-shared gradients.
机译:在本文中,我们将并行水平集(PLS)和Bowsher的方法评估为用于常规PET重建的无分割解剖先验。我们推导了两个PLS先验的近似算子,并将EM-TV算法与Chambolle和Pock的一阶原始对偶算法结合使用,以解决用PLS正则化进行PET重建的非平滑优化问题。此外,我们将两个PLS版本的性能与具有二次和相对差罚函数的对称和不对称Bowsher先验进行比较。为此,我们首先根据区域偏差和噪声,评估从实际PET / MRI采集获得的模拟PET数据的30种噪声实现的重构。其次,我们以类似的方式评估在GE Signa TOF PET / MR上获得的真实大脑PET / MR数据集的重建。对模拟和实际3D PET / MRI数据的重建表明,就PET吸收遵循解剖学边界的不同感兴趣区域中的偏向噪声特性而言,所有先验均优于平滑后的最大有序子集最大似然期望(OSEM)。 。与两个版本的PLS和对称Bowsher先验产品相比,我们对非对称Bowsher先验产品的实施表现出稍强的性能。在非常高的正则化权重下,所有研究的解剖学先验都遭受非共享梯度的转移。

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