More widespread use of positron emission tomography (PET) imaging is limited by its high cost and radiation dose. Reductions in PET scan time or radiotracer dosage typically degrade diagnostic image quality (DIQ). Deep-learning-based reconstruction may improve DIQ, but such methods have not been clinically evaluated in a realistic multicenter, multivendor environment. In this study, we evaluated the performance and generalizability of a deep-learning-based image-quality enhancement algorithm applied to fourfold reduced-count whole-body PET in a realistic clinical oncologic imaging environment with multiple blinded readers, institutions, and scanner types. We demonstrate that the low-count-enhanced scans were noninferior to the standard scans in DIQ (p < 0.05) and overall diagnostic confidence (p < 0.001) independent of the underlying PET scanner used. Lesion detection for the low-count-enhanced scans had a high patient-level sensitivity of 0.94 (0.83–0.99) and specificity of 0.98 (0.95–0.99). Interscan kappa agreement of 0.85 was comparable to intrareader (0.88) and pairwise inter-reader agreements (maximum of 0.72). SUV quantification was comparable in the reference regions and lesions (lowest p-value=0.59) and had high correlation (lowest CCC = 0.94). Thus, we demonstrated that deep learning can be used to restore diagnostic image quality and maintain SUV accuracy for fourfold reduced-count PET scans, with interscan variations in lesion depiction, lower than intra- and interreader variations. This method generalized to an external validation set of clinical patients from multiple institutions and scanner types. Overall, this method may enable either dose or exam-duration reduction, increasing safety and lowering the cost of PET imaging.

正电子发射断层扫描（PET）成像的更广泛使用因其高成本和辐射剂量而受到限制。PET 扫描时间或放射性示踪剂剂量的减少通常会降低诊断图像质量 (DIQ)。基于深度学习的重建可能会改善 DIQ，但此类方法尚未在现实​​的多中心、多供应商环境中进行临床评估。在这项研究中，我们评估了基于深度学习的图像质量增强算法的性能和通用性，该算法应用于具有多个盲法读者、机构和扫描仪类型的真实临床肿瘤成像环境中的四倍减少计数全身 PET。我们证明，低计数增强扫描在 DIQ ( p  < 0.05) 和整体诊断置信度 ( p  < 0.001) 方面不劣于标准扫描，与所使用的基础 PET 扫描仪无关。低计数增强扫描的病灶检测具有 0.94 (0.83–0.99) 的高患者水平敏感性和 0.98 (0.95–0.99) 的特异性。扫描间 kappa 一致性为 0.85，与读卡器内 (0.88) 和成对读卡器间一致性（最大值 0.72）相当。SUV 量化在参考区域和病变中具有可比性（最低p值 = 0.59），并且具有高度相关性（最低 CCC = 0.94）。因此，我们证明深度学习可用于恢复诊断图像质量并保持四倍减少计数 PET 扫描的 SUV 准确性，病灶描绘的扫描间变化低于读取器内和读取器间的变化。该方法推广到来自多个机构和扫描仪类型的临床患者的外部验证集。总体而言，这种方法可以减少剂量或检查持续时间，提高安全性并降低 PET 成像的成本。