Kim Kangsan, Byun Byung Hyun, Lim Ilhan, Lim Sang Moo, Woo Sang-Keun
Division of Applied RI, Korea Institute of Radiological and Medical Sciences, Seoul 01812, Republic of Korea.
Department of Nuclear Medicine, Korea Institute of Radiological and Medical Sciences, Seoul 01812, Republic of Korea.
Diagnostics (Basel). 2023 Sep 25;13(19):3045. doi: 10.3390/diagnostics13193045.
The acquisition of in vivo radiopharmaceutical distribution through imaging is time-consuming due to dosimetry, which requires the subject to be scanned at several time points post-injection. This study aimed to generate delayed positron emission tomography images from early images using a deep-learning-based image generation model to mitigate the time cost and inconvenience. Eighteen healthy participants were recruited and injected with [F]Fluorodeoxyglucose. A paired image-to-image translation model, based on a generative adversarial network (GAN), was used as the generation model. The standardized uptake value (SUV) mean of the generated image of each organ was compared with that of the ground-truth. The least square GAN and perceptual loss combinations displayed the best performance. As the uptake time of the early image became closer to that of the ground-truth image, the translation performance improved. The SUV mean values of the nominated organs were estimated reasonably accurately for the muscle, heart, liver, and spleen. The results demonstrate that the image-to-image translation deep learning model is applicable for the generation of a functional image from another functional image acquired from normal subjects, including predictions of organ-wise activity for specific normal organs.
由于剂量测定的原因，通过成像获取体内放射性药物分布的过程很耗时，这需要在注射后多个时间点对受试者进行扫描。本研究旨在使用基于深度学习的图像生成模型从早期图像生成延迟正电子发射断层扫描图像，以减少时间成本和不便之处。招募了18名健康参与者并注射了[F]氟脱氧葡萄糖。基于生成对抗网络（GAN）的配对图像到图像转换模型被用作生成模型。将每个器官生成图像的标准化摄取值（SUV）均值与真实值进行比较。最小二乘GAN和感知损失组合表现出最佳性能。随着早期图像的摄取时间与真实图像的摄取时间越来越接近，转换性能得到改善。对于肌肉、心脏、肝脏和脾脏，指定器官的SUV均值估计相当准确。结果表明，图像到图像转换深度学习模型适用于从正常受试者获取的另一幅功能图像生成功能图像，包括对特定正常器官的器官特异性活性进行预测。
