三维OSEM和中位根先验(3D OSEM- mrp)重建算法的实现与评价

V. Bettinardi, E. Pagani, S. Alenius, M. Teras, M. Gilardi, C. Labbé, M. Jacobsen, K. Thielemans, M. Sadki, C. Morel, R. Levkovitz, A. Ben-Tal, T. Spinks, G. Mitra, F. Fazio
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引用次数: 2

摘要

3D OSEM和中位根先验(3D OSEM- mrp)算法被评估用于重建3D PET研究。该算法采用欧盟PARAPET项目开发的软件包实现。利用实验幻影数据模拟对PET脑的形状和大小进行评估。对于每个幻影,获得高(/spl sim/200 Mcounts)和低(<50 Mcounts)计数统计3D PET数据。基于感兴趣区域的使用,通过计算简单的优点数字(例如对比度,变异系数,两个区域之间的活动比)来评估算法的性能。使用不同的重构滤波器,将3D OSEM- mrp与“纯”3D OSEM和PROMIS算法的性能进行了比较。在所有考虑的实验情况下,3D OSEM-MRP显示:1)收敛到一个稳定的解,2)定量准确,3)在降噪方面非常有效,特别是对于低统计数据,4)保持“良好”的空间分辨率。与3D OSEM和PROMIS算法相比,3D OSEM- mrp根据用于图像重建的配置参数提供更好或相似的结果。
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Implementation and evaluation of a 3D OSEM and median root prior (3D OSEM-MRP) reconstruction algorithm
A 3D OSEM and Median Root Prior (3D OSEM-MRP) algorithm has been evaluated for the reconstruction of 3D PET studies. The algorithm was implemented using the software package developed during the EU project PARAPET. Evaluation was performed using experimental phantom data simulating in terms of shape and size PET brain studies. For each phantom, high (/spl sim/200 Mcounts) and low (<50 Mcounts) count statistics 3D PET data were acquired. The performances of the algorithm were evaluated by calculating simple figures of merit (e.g. contrast, coefficient of variation, activity ratio between two regions) based on the use of regions of interest. The performances of the 3D OSEM-MRP were compared with those of a "pure" 3D OSEM and of the PROMIS algorithm, using different reconstruction filters. In all the considered experimental situations, 3D OSEM-MRP shows: 1) to converge to a stable solution, 2) to be quantitatively accurate, 3) to be very effective in noise reduction, particularly for low statistics data, 4) to maintain "good" spatial resolution. Compared with the 3D OSEM and PROMIS algorithms, 3D OSEM-MRP provides better or comparable results depending on the configuration parameters used for the reconstruction of the images.
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