PDF(Pubmed)
Abstract：
OBJECTIVE: Given the potential risk of motion artifacts, acquisition time reduction is desirable in pediatric 99m Tc-dimercaptosuccinic acid (DMSA) scintigraphy. The aim of this study was to evaluate the performance of predicted full-acquisition-time images from short-acquisition-time pediatric 99m Tc-DMSA planar images with only 1/5th acquisition time using deep learning in terms of image quality and quantitative renal uptake measurement accuracy.
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.
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.
摘要：
目标：鉴于运动伪影的潜在风险，在儿科99mTc-二巯基琥珀酸（DMSA）闪烁显像中，需要减少采集时间。这项研究的目的是评估预测的全采集时间图像的性能从短采集时间儿科99mTc-DMSA平面图像只有1/5采集时间使用深度学习在图像质量和定量肾摄取测量精度方面。
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。
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。
