Purpose: Given the potential risk of motion artifacts, acquisition time reduction is desirable in pediatric 99mTc-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 99mTc-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 99mTc-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 99mTc-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 99mTc-DMSA planar imaging.
CITATION STYLE
Ichikawa, S., Sugimori, H., Ichijiri, K., Yoshimura, T., & Nagaki, A. (2023). Acquisition time reduction in pediatric 99mTc-DMSA planar imaging using deep learning. Journal of Applied Clinical Medical Physics, 24(6). https://doi.org/10.1002/acm2.13978
Mendeley helps you to discover research relevant for your work.