This study aims to decrease the scan time and enhance image quality in pediatric total-body PET imaging by utilizing multimodal artificial intelligence techniques. A total of 270 pediatric patients who underwent total-body PET/CT scans with a uEXPLORER at the Sun Yat-sen University Cancer Center were retrospectively enrolled. 18F-fluorodeoxyglucose (18F-FDG) was administered at a dose of 3.7 MBq/kg with an acquisition time of 600 s. Short-term scan PET images (acquired within 6, 15, 30, 60 and 150 s) were obtained by truncating the list-mode data. A three-dimensional (3D) neural network was developed with a residual network as the basic structure, fusing low-dose CT images as prior information, which were fed to the network at different scales. The short-term PET images and low-dose CT images were processed by the multimodal 3D network to generate full-length, high-dose PET images. The nonlocal means method and the same 3D network without the fused CT information were used as reference methods. The performance of the network model was evaluated by quantitative and qualitative analyses. Multimodal artificial intelligence techniques can significantly improve PET image quality. When fused with prior CT information, the anatomical information of the images was enhanced, and 60 s of scan data produced images of quality comparable to that of the full-time data. Multimodal artificial intelligence techniques can effectively improve the quality of pediatric total-body PET/CT images acquired using ultrashort scan times. This has the potential to decrease the use of sedation, enhance guardian confidence, and reduce the probability of motion artifacts.

中文翻译：

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利用多模态人工智能技术缩短儿科全身 PET/CT 成像扫描时间

本研究旨在利用多模态人工智能技术减少儿科全身 PET 成像的扫描时间并提高图像质量。回顾性纳入中山大学肿瘤防治中心使用 uEXPLORER 进行全身 PET/CT 扫描的 270 名儿童患者。18F-氟脱氧葡萄糖（18F-FDG）以3.7MBq/kg的剂量施用，采集时间为600秒。通过截断列表模式数据获得短期扫描 PET 图像（在 6、15、30、60 和 150 秒内采集）。以残差网络为基本结构开发了三维（3D）神经网络，融合低剂量CT图像作为先验信息，以不同的尺度输入网络。短期 PET 图像和低剂量 CT 图像由多模态 3D 网络处理，生成全长、高剂量 PET 图像。非局部均值方法和没有融合CT信息的相同3D网络被用作参考方法。通过定量和定性分析评估网络模型的性能。多模态人工智能技术可以显着提高PET图像质量。当与先前的 CT 信息融合时，图像的解剖信息得到增强，60 秒的扫描数据产生的图像质量可与全时数据相当。多模态人工智能技术可以有效提高超短扫描时间获取的儿科全身PET/CT图像的质量。这有可能减少镇静剂的使用，增强监护人的信心，并减少运动伪影的可能性。