Optimal rebinning of time-of-flight PET data.
Optimal rebinning of time-of-flight PET data.
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DOI：
10.1109/tmi.2011.2149537
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发表时间：
2011-10
影响因子：
10.6
通讯作者：
Leahy RM
中科院分区：
文献类型：
--
作者：
Ahn S;Cho S;Li Q;Lin Y;Leahy RM
飞行时间（TOF）正电子发射断层扫描（PET）扫描仪为临床PET中的信噪比（SNR）和病变可检测性提供了显着提高的潜力。但是，由于数据大小庞大，完全3D TOF PET图像重建是一项艰巨的任务。解决此问题的一种解决方案是将数据重新介绍为较低的维度格式。我们最近开发了将TOF数据映射到非TOF格式中的傅立叶重新介绍方法，这些格式保留了相对于没有TOF信息而获得的正式图的实质性SNR优势。但是，重新融资为非TOF格式的映射并不是唯一的，并且还没有广泛研究重新介绍方法的优化。在本文中，我们解决了最佳重新序列的问题，以便充分利用TOF信息。我们专注于foret-3D，该foret-3D大约重新构建3D TOF数据为3D非TOF sinogram格式，而无需在轴向方向上进行傅立叶变换。我们优化了Foret-3D的加权以最大程度地减少差异，从而导致H2加权Foret-3D，事实证明，它是合理近似值下最佳的线性无偏估计器（蓝色），此外，最小值的最小值（UMVU）无偏见（UMVU）估计值高斯噪声假设。这意味着由于最佳重新介绍而引起的任何信息丢失仅是由于推导重新介绍方程和开发最佳加权的近似值而导致的。我们使用模拟和真实的幻影TOF数据证明，与非优化的重新介绍权重相比，最佳重新系列方法可实现差异和对比度恢复的改善。在我们使用简化的模拟设置的初步研究中，最佳重新系列方法的性能与完全3D TOF MAP相当。
Time-of-flight (TOF) positron emission tomography (PET) scanners offer the potential for significantly improved signal-to-noise ratio (SNR) and lesion detectability in clinical PET. However, fully 3D TOF PET image reconstruction is a challenging task due to the huge data size. One solution to this problem is to rebin TOF data into a lower dimensional format. We have recently developed Fourier rebinning methods for mapping TOF data into non-TOF formats that retain substantial SNR advantages relative to sinograms acquired without TOF information. However, mappings for rebinning into non-TOF formats are not unique and optimization of rebinning methods has not been widely investigated. In this paper we address the question of optimal rebinning in order to make full use of TOF information. We focus on FORET-3D, which approximately rebins 3D TOF data into 3D non-TOF sinogram formats without requiring a Fourier transform in the axial direction. We optimize the weighting for FORET-3D to minimize the variance, resulting in H2-weighted FORET-3D, which turns out to be the best linear unbiased estimator (BLUE) under reasonable approximations and furthermore the uniformly minimum variance unbiased (UMVU) estimator under Gaussian noise assumptions. This implies that any information loss due to optimal rebinning is as a result only of the approximations used in deriving the rebinning equation and developing the optimal weighting. We demonstrate using simulated and real phantom TOF data that the optimal rebinning method achieves variance reduction and contrast recovery improvement compared to nonoptimized rebinning weightings. In our preliminary study using a simplified simulation setup, the performance of the optimal rebinning method was comparable to that of fully 3D TOF MAP.