{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,19]],"date-time":"2026-03-19T03:29:13Z","timestamp":1773890953010,"version":"3.50.1"},"reference-count":27,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2025,3,19]],"date-time":"2025-03-19T00:00:00Z","timestamp":1742342400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Fundacao para a Ciencia e Tecnologia\u2014Portugal","award":["UIDB\/00645\/2020"],"award-info":[{"award-number":["UIDB\/00645\/2020"]}]},{"name":"Laboratory for Instrumentation, Biomedical Engineering, and Radiation Physics","award":["UIDB\/00645\/2020"],"award-info":[{"award-number":["UIDB\/00645\/2020"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J. Imaging"],"abstract":"<jats:p>The objective of this study is to propose an advanced image enhancement strategy to address the challenge of reducing radiation doses in pediatric renal scintigraphy. Data from a public dynamic renal scintigraphy database were used. Based on noisier images, four denoising neural networks (DnCNN, UDnCNN, DUDnCNN, and AttnGAN) were evaluated. To evaluate the quality of the noise reduction, with minimal detail loss, the kidney signal-to-noise ratio (SNR) and multiscale structural similarity (MS-SSIM) were used. Although all the networks reduced noise, UDnCNN achieved the best balance between SNR and MS-SSIM, leading to the most notable improvements in image quality. In clinical practice, 100% of the acquired data are summed to produce the final image. To simulate the dose reduction, we summed only 50%, simulating a proportional decrease in radiation. The proposed deep-learning approach for image enhancement ensured that half of all the frames acquired may yield results that are comparable to those of the complete dataset, suggesting that it is feasible to reduce patients\u2019 exposure to radiation. This study demonstrates that the neural networks evaluated can markedly improve the renal scintigraphic image quality, facilitating high-quality imaging with lower radiation doses, which will benefit the pediatric population considerably.<\/jats:p>","DOI":"10.3390\/jimaging11030088","type":"journal-article","created":{"date-parts":[[2025,3,19]],"date-time":"2025-03-19T07:48:37Z","timestamp":1742370517000},"page":"88","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Recovering Image Quality in Low-Dose Pediatric Renal Scintigraphy Using Deep Learning"],"prefix":"10.3390","volume":"11","author":[{"given":"Marta","family":"Ars\u00e9nio","sequence":"first","affiliation":[{"name":"Physics Department, NOVA School of Science and Technology, NOVA University of Lisbon, 2829-516 Caparica, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0950-6035","authenticated-orcid":false,"given":"Ricardo","family":"Vig\u00e1rio","sequence":"additional","affiliation":[{"name":"Physics Department, NOVA School of Science and Technology, NOVA University of Lisbon, 2829-516 Caparica, Portugal"},{"name":"Laboratory for Instrumentation Biomedical Engineering and Radiation Physis, NOVA University of Lisbon, 2829-516 Caparica, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1931-294X","authenticated-orcid":false,"given":"Ana M.","family":"Mota","sequence":"additional","affiliation":[{"name":"Instituto de Biof\u00edsica e Engenharia Biom\u00e9dica, Faculdade de Ci\u00eancias, Universidade de Lisboa, 1749-016 Lisboa, Portugal"}]}],"member":"1968","published-online":{"date-parts":[[2025,3,19]]},"reference":[{"key":"ref_1","first-page":"47","article-title":"Comparison of Different Renal Imaging Modalities: An Overview","volume":"47","author":"Kaur","year":"2018","journal-title":"Prog. 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