Q.Clear, a Bayesian penalized likelihood reconstruction algorithm, has shown high potential in improving quantitation accuracy in PET systems. The Q.Clear algorithm controls noise during the iterative reconstruction through a β penalization factor. This study aimed to determine the optimal β-factor for accurate quantitation of dynamic PET scans. A Flangeless Esser PET Phantom with eight hollow spheres (4–25 mm) was scanned on a GE Discovery MI PET/CT system. Data were reconstructed into five sets of variable acquisition times using Q.Clear with 18 different β-factors ranging from 100 to 3500. The recovery coefficient (RC), coefficient of variation (CVRC) and root-mean-square error (RMSERC) were evaluated for the phantom data. Two male patients with recurrent glioblastoma were scanned on the same scanner using 18F-PSMA-1007. Using an irreversible two-tissue compartment model, the area under curve (AUC) and the net influx rate Ki were calculated to assess the impact of different β-factors on the pharmacokinetic analysis of clinical PET brain data. In general, RC and CVRC decreased with increasing β-factor in the phantom data. For small spheres (< 10 mm), and in particular for short acquisition times, low β-factors resulted in high variability and an overestimation of measured activity. Increasing the β-factor improves the variability, however at a cost of underestimating the measured activity. For the clinical data, AUC decreased and Ki increased with increased β-factor; a change in β-factor from 300 to 1000 resulted in a 25.5% increase in the Ki. In a complex dynamic dataset with variable acquisition times, the optimal β-factor provides a balance between accuracy and precision. Based on our results, we suggest a β-factor of 300–500 for quantitation of small structures with dynamic PET imaging, while large structures may benefit from higher β-factors. Clinicaltrials.gov, NCT03951142. Registered 5 October 2019, https://clinicaltrials.gov/ct2/show/NCT03951142 . EudraCT no 2018-003229-27. Registered 26 February 2019, https://www.clinicaltrialsregister.eu/ctr-search/trial/2018-003229-27/NO .

中文翻译：

---

动态 18F PET 成像的 Q.Clear 重建优化

Q.Clear 是一种贝叶斯惩罚似然重建算法，在提高 PET 系统的定量准确性方面显示出巨大的潜力。Q.Clear 算法通过 β 惩罚因子控制迭代重建过程中的噪声。本研究旨在确定动态 PET 扫描准确定量的最佳 β 因子。在 GE Discovery MI PET/CT 系统上对具有八个空心球（4-25 毫米）的无凸缘 Esser PET 体模进行扫描。使用 Q.Clear 将数据重建为五组可变采集时间，其中包含 100 至 3500 之间的 18 个不同 β 因子。回收系数 (RC)、变异系数 (CVRC) 和均方根误差 (RMSERC) 分别为评估幻象数据。使用 18F-PSMA-1007 在同一台扫描仪上对两名患有复发性胶质母细胞瘤的男性患者进行扫描。使用不可逆的二组织室模型，计算曲线下面积（AUC）和净流入率Ki，以评估不同β因子对临床PET脑数据的药代动力学分析的影响。一般来说，RC 和 CVRC 随着模型数据中 β 因子的增加而降低。对于小球体（< 10 mm），特别是对于较短的采集时间，低 β 因子会导致高变异性和对测量活性的高估。增加 β 因子可改善变异性，但代价是低估测量的活动。临床数据显示，随着β因子的增加，AUC降低，Ki增加；β 因子从 300 更改为 1000 导致 Ki 增加 25.5%。在具有可变采集时间的复杂动态数据集中，最佳 β 因子可在准确度和精确度之间实现平衡。根据我们的结果，我们建议使用动态 PET 成像对小型结构进行定量时使用 300-500 的 β 因子，而较大的结构可能受益于更高的 β 因子。临床试验.gov，NCT03951142。2019 年 10 月 5 日注册，https://clinicaltrials.gov/ct2/show/NCT03951142。EudraCT 编号 2018-003229-27。2019 年 2 月 26 日注册，https://www.clinicaltrialsregister.eu/ctr-search/trial/2018-003229-27/NO 。