Q.Clear is a block sequential regularized expectation maximization (BSREM) penalized-likelihood reconstruction algorithm for PET. It tries to improve image quality by controlling noise amplification during image reconstruction. In this study, the noise properties of this BSREM were compared to the ordered-subset expectation maximization (OSEM) algorithm for both phantom and patient data acquired on a state-of-the-art PET/CT. The NEMA IQ phantom and a whole-body patient study were acquired on a GE DMI 3-rings system in list mode and different datasets with varying noise levels were generated. Phantom data was evaluated using four different contrast ratios. These were reconstructed using BSREM with different β-factors of 300–3000 and with a clinical setting used for OSEM including point spread function (PSF) and time-of-flight (TOF) information. Contrast recovery (CR), background noise levels (coefficient of variation, COV), and contrast-to-noise ratio (CNR) were used to determine the performance in the phantom data. Findings based on the phantom data were compared with clinical data. For the patient study, the SUV ratio, metabolic active tumor volumes (MATVs), and the signal-to-noise ratio (SNR) were evaluated using the liver as the background region. Based on the phantom data for the same count statistics, BSREM resulted in higher CR and CNR and lower COV than OSEM. The CR of OSEM matches to the CR of BSREM with β = 750 at high count statistics for 8:1. A similar trend was observed for the ratios 6:1 and 4:1. A dependence on sphere size, counting statistics, and contrast ratio was confirmed by the CNR of the ratio 2:1. BSREM with β = 750 for 2.5 and 1.0 min acquisition has comparable COV to the 10 and 5.0 min acquisitions using OSEM. This resulted in a noise reduction by a factor of 2–4 when using BSREM instead of OSEM. For the patient data, a similar trend was observed, and SNR was reduced by at least a factor of 2 while preserving contrast. The BSREM reconstruction algorithm allowed a noise reduction without a loss of contrast by a factor of 2–4 compared to OSEM reconstructions for all data evaluated. This reduction can be used to lower the injected dose or shorten the acquisition time.

中文翻译：

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在飞行时间 PET-CT 扫描仪上使用贝叶斯惩罚似然重建算法进行降噪

Q.Clear 是 PET 的块顺序正则化期望最大化 (BSREM) 惩罚似然重建算法。它试图通过在图像重建过程中控制噪声放大来提高图像质量。在这项研究中，针对在最先进的 PET/CT 上获取的体模和患者数据，将该 BSREM 的噪声特性与有序子集期望最大化 (OSEM) 算法进行了比较。NEMA IQ 体模和全身患者研究是在 GE DMI 3 环系统上以列表模式获取的，并生成具有不同噪声水平的不同数据集。使用四种不同的对比度来评估模型数据。这些是使用具有 300-3000 不同 β 因子的 BSREM 以及用于 OSEM 的临床设置（包括点扩散函数 (PSF) 和飞行时间 (TOF) 信息）进行重建的。对比度恢复 (CR)、背景噪声水平（变异系数，COV）和对比度噪声比 (CNR) 用于确定模型数据的性能。将基于模型数据的发现与临床数据进行比较。对于患者研究，使用肝脏作为背景区域评估 SUV 比率、代谢活性肿瘤体积 (MATV) 和信噪比 (SNR)。基于相同计数统计的虚拟数据，BSREM 比 OSEM 具有更高的 CR 和 CNR 以及更低的 COV。OSEM 的 CR 与 BSREM 的 CR 匹配，在高计数统计中 β = 750，比例为 8:1。6:1 和 4:1 的比例也观察到类似的趋势。2:1 比率的 CNR 证实了对球体尺寸、计数统计和对比度的依赖性。BSREM（β = 750，2.5 分钟和 1.0 分钟采集）的 COV 与使用 OSEM 的 10 分钟和 5.0 分钟采集的 COV 相当。当使用 BSREM 代替 OSEM 时，噪音降低了 2-4 倍。对于患者数据，观察到类似的趋势，SNR 在保持对比度的同时至少降低了 2 倍。与所有评估数据的 OSEM 重建相比，BSREM 重建算法可以在不损失对比度的情况下降低噪声 2-4 倍。这种减少可用于降低注射剂量或缩短采集时间。