Cheng Ju-Chieh Kevin, Matthews Julian, Sossi Vesna, Anton-Rodriguez Jose, Salomon André, Boellaard Ronald
Department of Radiology and Nuclear Medicine, VU University Medical Center, De Boelelaan 1117, Amsterdam, 1081 HV, Netherlands. Division of Informatics, Imaging and Data Sciences, The University of Manchester, Wolfson Molecular Imaging Centre, Manchester M20 3LJ, United Kingdom. Department of Physics and Astronomy, The University of British Columbia, 6224 Agricultural Road, Vancouver, BC V6T 1Z1, Canada. Pacific Parkinson's Research Centre, The University of British Columbia, 2215 Wesbrook Mall, Vancouver, BC V6T 1Z3, Canada.
Phys Med Biol. 2017 Aug 1;62(16):6666-6687. doi: 10.1088/1361-6560/aa7b66.
HighlY constrained back-PRojection (HYPR) is a post-processing de-noising technique originally developed for time-resolved magnetic resonance imaging. It has been recently applied to dynamic imaging for positron emission tomography and shown promising results. In this work, we have developed an iterative reconstruction algorithm (HYPR-OSEM) which improves the signal-to-noise ratio (SNR) in static imaging (i.e. single frame reconstruction) by incorporating HYPR de-noising directly within the ordered subsets expectation maximization (OSEM) algorithm. The proposed HYPR operator in this work operates on the target image(s) from each subset of OSEM and uses the sum of the preceding subset images as the composite which is updated every iteration. Three strategies were used to apply the HYPR operator in OSEM: (i) within the image space modeling component of the system matrix in forward-projection only, (ii) within the image space modeling component in both forward-projection and back-projection, and (iii) on the image estimate after the OSEM update for each subset thus generating three forms: (i) HYPR-F-OSEM, (ii) HYPR-FB-OSEM, and (iii) HYPR-AU-OSEM. Resolution and contrast phantom simulations with various sizes of hot and cold regions as well as experimental phantom and patient data were used to evaluate the performance of the three forms of HYPR-OSEM, and the results were compared to OSEM with and without a post reconstruction filter. It was observed that the convergence in contrast recovery coefficients (CRC) obtained from all forms of HYPR-OSEM was slower than that obtained from OSEM. Nevertheless, HYPR-OSEM improved SNR without degrading accuracy in terms of resolution and contrast. It achieved better accuracy in CRC at equivalent noise level and better precision than OSEM and better accuracy than filtered OSEM in general. In addition, HYPR-AU-OSEM has been determined to be the more effective form of HYPR-OSEM in terms of accuracy and precision based on the studies conducted in this work.
高度约束反投影（HYPR）是一种后处理去噪技术，最初是为时间分辨磁共振成像开发的。最近它已被应用于正电子发射断层扫描的动态成像，并显示出有前景的结果。在这项工作中，我们开发了一种迭代重建算法（HYPR - OSEM），通过将HYPR去噪直接纳入有序子集期望最大化（OSEM）算法，提高了静态成像（即单帧重建）中的信噪比（SNR）。本文中提出的HYPR算子作用于OSEM每个子集中的目标图像，并以前一个子集图像的总和作为每次迭代更新的合成图像。在OSEM中应用HYPR算子使用了三种策略：（i）仅在前向投影的系统矩阵的图像空间建模组件内，（ii）在前向投影和反投影的图像空间建模组件内，以及（iii）在每个子集的OSEM更新后的图像估计上，从而产生三种形式：（i）HYPR - F - OSEM，（ii）HYPR - FB - OSEM，和（iii）HYPR - AU - OSEM。使用具有各种大小的热区和冷区的分辨率和对比度体模模拟以及实验体模和患者数据来评估三种形式的HYPR - OSEM的性能，并将结果与使用和不使用重建后滤波器的OSEM进行比较。观察到从所有形式的HYPR - OSEM获得的对比度恢复系数（CRC）的收敛速度比从OSEM获得的慢。然而，HYPR - OSEM在不降低分辨率和对比度精度的情况下提高了SNR。在等效噪声水平下，它在CRC方面实现了比OSEM更好的精度和比OSEM更好的精度，并且总体上比滤波后的OSEM具有更好的精度。此外，基于这项工作中进行的研究，已确定HYPR - AU - OSEM在精度和精度方面是HYPR - OSEM更有效的形式。
