METHODS: One hundred and fifty-five cases that underwent pediatric 99m Tc-DMSA planar imaging as dynamic data for 10 min were retrospectively collected for the development of three deep learning models (DnCNN, Win5RB, and ResUnet), and the generation of full-time images from short-time images. We used the normalized mean squared error (NMSE), peak signal-to-noise ratio (PSNR), and structural similarity index metrics (SSIM) to evaluate the accuracy of the predicted full-time images. In addition, the renal uptake of 99m Tc-DMSA was calculated, and the difference in renal uptake from the reference full-time images was assessed using scatter plots with Pearson correlation and Bland-Altman plots.
RESULTS: The predicted full-time images from the deep learning models showed a significant improvement in image quality compared to the short-time images with respect to the reference full-time images. In particular, the predicted full-time images obtained by ResUnet showed the lowest NMSE (0.4 [0.4-0.5] %) and the highest PSNR (55.4 [54.7-56.1] dB) and SSIM (0.997 [0.995-0.997]). For renal uptake, an extremely high correlation was achieved in all short-time and three predicted full-time images (R2 > 0.999 for all). The Bland-Altman plots showed the lowest bias (-0.10) of renal uptake in ResUnet, while short-time images showed the lowest variance (95% confidence interval: -0.14, 0.45) of renal uptake.
CONCLUSIONS: Our proposed method is capable of producing images that are comparable to the original full-acquisition-time images, allowing for a reduction of acquisition time/injected dose in pediatric 99m Tc-DMSA planar imaging.
方法:回顾性收集了105例接受儿科99mTc-DMSA平面成像的10分钟动态数据,以开发三种深度学习模型(DnCNN,Win5RB,和ResUnet),以及从短时图像生成全时图像。我们使用归一化均方误差(NMSE),峰值信噪比(PSNR),和结构相似性指数度量(SSIM)来评估预测的全时图像的准确性。此外,计算99mTc-DMSA的肾脏摄取,使用Pearson相关散点图和Bland-Altman图评估了参考全职图像中肾脏摄取的差异。
结果:与参考全职图像相比,来自深度学习模型的预测全职图像显示出图像质量的显着改善。特别是,ResUnet获得的预测全职图像显示出最低的NMSE(0.4[0.4-0.5]%)和最高的PSNR(55.4[54.7-56.1]dB)和SSIM(0.997[0.995-0.997]).对于肾脏摄取,在所有短时图像和三个预测的全时图像中实现了极高的相关性(全部R2>0.999)。Bland-Altman图显示ResUnet中肾脏摄取的偏倚最低(-0.10),而短时间图像显示肾脏摄取的方差最低(95%置信区间:-0.14,0.45)。
结论:我们提出的方法能够产生与原始全时间采集图像相当的图像,允许减少儿科99mTc-DMSA平面成像的采集时间/注射剂量。