Analytic TOF PET reconstruction algorithm within DIRECT data partitioning framework.

Analytic TOF PET reconstruction algorithm within DIRECT data partitioning framework.
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DOI:
10.1088/0031-9155/61/9/3365
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发表时间:
2016-05-07
影响因子:
3.5
通讯作者:
Karp JS
Karp JS
中科院分区:
工程技术2区
文献类型:
--
作者:
Matej S;Daube-Witherspoon ME;Karp JS

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迭代重建算法经常用于临床实践;然而,分析算法由于其线性行为,是定量研究的相关候选算法。虽然迭代算法也受益于包含准确的数据和噪声模型,但TOF扫描仪的广泛使用对噪声和数据缺陷的敏感度较低,使得分析算法更加有前景。在我们以前的工作中,我们开发了一种新颖的迭代重建方法(Direct Image Recombition For TOF),提供了方便的TOF数据划分框架,并导致了非常高效的重建。在这项工作中,我们已经直接扩展到包括具有置信度加权的解析TOF算法,该算法结合了TOF和空间分辨率核的模型。利用模拟和测量数据进行的可行性研究表明,采用适当的分辨率和正则化滤波器的解析直接法能够为具有匹配分辨率模型的迭代TOF重建提供匹配的偏差与方差性能。
Iterative reconstruction algorithms are routinely used for clinical practice; however, analytic algorithms are relevant candidates for quantitative research studies due to their linear behavior. While iterative algorithms also benefit from the inclusion of accurate data and noise models the widespread use of TOF scanners with less sensitivity to noise and data imperfections make analytic algorithms even more promising. In our previous work we have developed a novel iterative reconstruction approach (Direct Image Reconstruction for TOF) providing convenient TOF data partitioning framework and leading to very efficient reconstructions. In this work we have expanded DIRECT to include an analytic TOF algorithm with confidence weighting incorporating models of both TOF and spatial resolution kernels. Feasibility studies using simulated and measured data demonstrate that analytic-DIRECT with appropriate resolution and regularization filters is able to provide matched bias vs. variance performance to iterative TOF reconstruction with a matched resolution model.
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