Image quality and quantitative accuracy of positron emission tomography (PET) depend on several factors such as uptake time, scanner characteristics and image reconstruction methods. Ordered subset expectation maximization (OSEM) is considered the gold standard for image reconstruction. Penalized-likelihood estimation (PL) algorithms have been recently developed for PET reconstruction to improve quantitation accuracy while maintaining or even improving image quality. In PL algorithms, a regularization parameter β controls the penalization of relative differences between neighboring pixels and determines image characteristics. In the present study, we aim to compare the performance of Q.Clear (PL algorithm, GE Healthcare) and OSEM (3 iterations, 8 subsets, 6-mm post-processing filter) for 68Ga-DOTATATE (68Ga-DOTA) PET studies, both visually and quantitatively. Thirty consecutive whole-body 68Ga-DOTA studies were included. The data were acquired in list mode and were reconstructed using 3D OSEM and Q.Clear with various values of β and various acquisition times per bed position (bp), thus generating images with reduced injected dose (1.5 min/bp: β = 300–1100; 1.0 min/bp: β = 600–1400 and 0.5 min/bp: β = 800–2200). An additional analysis adding β values up to 1500, 1700 and 3000 for 1.5, 1.0 and 0.5 min/bp, respectively, was performed for a random sample of 8 studies. Evaluation was performed using a phantom and clinical data. Two experienced nuclear medicine physicians blinded to the variables assessed the image quality visually. Clinical images reconstructed with Q.Clear, set at 1.5, 1.0 and 0.5 min/bp using β = 1100, 1300 and 3000, respectively, resulted in images with noise equivalence to 3D OSEM (1.5 min/bp) with a mean increase in SUVmax of 14%, 13% and 4%, an increase in SNR of 30%, 24% and 10%, and an increase in SBR of 13%, 13% and 2%. Visual assessment yielded similar results for β values of 1100–1400 and 1300–1600 for 1.5 and 1.0 min/bp, respectively, although for 0.5 min/bp there was no significant improvement compared to OSEM. 68Ga-DOTA reconstructions with Q.Clear, 1.5 and 1.0 min/bp, resulted in increased tumor SUVmax and in improved SNR and SBR at a similar level of noise compared to 3D OSEM. Q.Clear with β = 1300–1600 enables one-third reduction of acquisition time or injected dose, with similar image quality compared to 3D OSEM.

中文翻译：

---

在⁶⁸ Ga-DOTATATE PET / CT研究中，可以使用惩罚似然估计算法来减少注射剂量或采集时间吗？

正电子发射断层扫描（PET）的图像质量和定量精度取决于几个因素，例如摄取时间，扫描仪特性和图像重建方法。有序子集期望最大化（OSEM）被认为是图像重建的黄金标准。最近开发了用于PET重建的惩罚似然估计（PL）算法，以提高定量精度，同时保持甚至改善图像质量。在PL算法中，正则化参数β控制相邻像素之间的相对差的惩罚并确定图像特性。在本研究中，我们旨在比较Q.Clear（PL算法，GE Healthcare）和OSEM（3次迭代，8个子集，6毫米后处理过滤器）在68Ga-DOTATATE（68Ga-DOTA）PET研究中的性能，视觉上和定量上。包括三十项连续的68Ga-DOTA全身研究。数据以列表模式获取，并使用3D OSEM和Q.Clear重建，具有不同的β值和每个床位（bp）的不同获取时间，从而生成了注射剂量减少的图像（1.5 min / bp：β= 300– 1100； 1.0 min / bp：β= 600-1400； 0.5 min / bp：β= 800-2200）。对于8个研究的随机样本，分别进行了分别增加1.5、1.0和0.5 min / bp的β值分别高达1500、1700和3000的附加分析。使用体模和临床数据进行评估。两位经验丰富的核医学医师对变量不了解，目视评估了图像质量。使用Q.Clear重建的临床图像，分别使用β= 1100、1300和3000分别设置为1.5、1.0和0.5 min / bp，导致图像具有与3D OSEM等效的图像（1.5 min / bp），SUVmax平均增加14％，13％和4％，SNR增加30％，24％和10％，SBR增加分别为13％，13％和2％。视觉评估得出的β值为1100–1400和1300–1600的β值分别为1.5和1.0 min / bp，尽管对于0.5 min / bp而言，与OSEM相比没有显着改善。与3D OSEM相比，Q.Clear分别以1.5和1.0 min / bp的Q.Clear进行68Ga-DOTA重建，可增加肿瘤SUVmax并提高SNR和SBR，且噪声水平相似。与3D OSEM相比，Q.Clear的β= 1300–1600可使采集时间或注射剂量减少三分之一，图像质量相似。SBR分别提高了13％，13％和2％。视觉评估得出的β值为1100–1400和1300–1600的β值分别为1.5和1.0 min / bp，尽管对于0.5 min / bp而言，与OSEM相比没有显着改善。与3D OSEM相比，Q.Clear分别以1.5和1.0 min / bp的Q.Clear进行68Ga-DOTA重建，可增加肿瘤SUVmax并提高SNR和SBR，且噪声水平相似。与3D OSEM相比，Q.Clear的β= 1300–1600可使采集时间或注射剂量减少三分之一，图像质量相似。SBR分别提高了13％，13％和2％。视觉评估得出的β值为1100–1400和1300–1600的β值分别为1.5和1.0 min / bp，尽管对于0.5 min / bp而言，与OSEM相比没有显着改善。与3D OSEM相比，Q.Clear分别以1.5和1.0 min / bp的Q.Clear进行68Ga-DOTA重建，可增加肿瘤SUVmax并提高SNR和SBR，且噪声水平相似。与3D OSEM相比，Q.Clear的β= 1300–1600可使采集时间或注射剂量减少三分之一，图像质量相似。与3D OSEM相比，在相似的噪声水平下，可导致肿瘤SUVmax增加，并改善SNR和SBR。与3D OSEM相比，Q.Clear的β= 1300–1600可使采集时间或注射剂量减少三分之一，图像质量相似。与3D OSEM相比，在相似的噪声水平下，可导致肿瘤SUVmax增加，并改善SNR和SBR。与3D OSEM相比，Q.Clear的β= 1300–1600可使采集时间或注射剂量减少三分之一，图像质量相似。