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Application of Ordered-Subsets Expectation-Maximization (OSEM) Algorithm to Cone-Beam SPECT for Accelerated 3D Reconstruction

机译:订购子集期望 - 最大化(OSEM)算法在加速3D重建中对锥形光束SPECT的应用

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摘要

We investigated the performance of an ordered-subsets expectation-maximization (OSEM) algorithm for accelerated reconstruction in cone-beam SPECT. SPECT scans were performed using a Defrise phantom filled with 0.9 μCi/ml of Tc-99m and a dual-head gamma camera equipped with one cone-beam (CBC, f=70 cm) and one parallel-beam collimator (PBQ. Images were reconstructed using a fully-3D approach with resolution and attenuation modeling and an ordered-subsets version of a maximum-likelihood expectation-maximization algorithm (MLEM). Three grouping patterns of subsets were applied: consecutive, orthogonal, and uniform. In contrast to PBC SPECT, we observe that, in CBC SPECT, the reconstruction grouping pattern of the subsets is very important for the image quality obtained. Only when the projection data grouped into a subset were selected as uniformly as possible from all the acquired views, were the image quality and the noise in the images very close to results obtained using MLEM. However, we note that, for both CBC and PBC SPECT, the loglikelihood for a given iteration is practically the same for different grouping patterns of subsets.
机译:我们调查了锥形束SPECT加速重建的有序亚群期望 - 最大化(OSEM)算法的性能。使用填充有0.9μCI/ mL TC-99M的碎屑模型和配备有一个锥形束(CBC,F = 70cm)和一个平行束准直器(PBQ的双头伽马摄像机进行SPECT扫描。使用完全3D方法重建分辨率和衰减建模和最大似然预期最大化算法(MLEM)的有序亚群版本。应用了三个子集的分组模式:连续,正交和均匀。与PBC相比SPECT,我们观察到,在CBC SPECT中,子集的重建分组模式对于获得的图像质量非常重要。只有当从所有获取的视图中尽可能均匀地选择分组到子集时的投影数据是图像质量和图像中的噪声非常接近使用MLEM获得的结果。但是,我们注意到,对于CBC和PBC SPECT,给定迭代的loglikelihie几乎是不同分组模式的不同分组模式TS。

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