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Acquisition time reduction in pediatric 99m Tc-DMSA planar imaging using deep learning.

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Abstract 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… Click to show full abstract

Abstract 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 (R 2 > 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.

Keywords: time; full time; dmsa; acquisition time; time images

Journal Title: Journal of applied clinical medical physics
Year Published: 2023

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