Recovering Image Quality in Low-Dose Pediatric Renal Scintigraphy Using Deep Learning
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, UDn...
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MDPI AG
2025-03-01
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| Series: | Journal of Imaging |
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| author | Marta Arsénio Ricardo Vigário Ana M. Mota |
| author_facet | Marta Arsénio Ricardo Vigário Ana M. Mota |
| author_sort | Marta Arsénio |
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| description | 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’ 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. |
| format | Article |
| id | doaj-art-70ae167afca446f39ae976fb40cb95cd |
| institution | OA Journals |
| issn | 2313-433X |
| language | English |
| publishDate | 2025-03-01 |
| publisher | MDPI AG |
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| series | Journal of Imaging |
| spelling | doaj-art-70ae167afca446f39ae976fb40cb95cd2025-08-20T01:48:52ZengMDPI AGJournal of Imaging2313-433X2025-03-011138810.3390/jimaging11030088Recovering Image Quality in Low-Dose Pediatric Renal Scintigraphy Using Deep LearningMarta Arsénio0Ricardo Vigário1Ana M. Mota2Physics Department, NOVA School of Science and Technology, NOVA University of Lisbon, 2829-516 Caparica, PortugalPhysics Department, NOVA School of Science and Technology, NOVA University of Lisbon, 2829-516 Caparica, PortugalInstituto de Biofísica e Engenharia Biomédica, Faculdade de Ciências, Universidade de Lisboa, 1749-016 Lisboa, PortugalThe 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’ 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.https://www.mdpi.com/2313-433X/11/3/88<sup>99m</sup>Tc-MAG3deep learningnoise reductionpediatric renal scintigraphymedical imaging |
| spellingShingle | Marta Arsénio Ricardo Vigário Ana M. Mota Recovering Image Quality in Low-Dose Pediatric Renal Scintigraphy Using Deep Learning Journal of Imaging <sup>99m</sup>Tc-MAG3 deep learning noise reduction pediatric renal scintigraphy medical imaging |
| title | Recovering Image Quality in Low-Dose Pediatric Renal Scintigraphy Using Deep Learning |
| title_full | Recovering Image Quality in Low-Dose Pediatric Renal Scintigraphy Using Deep Learning |
| title_fullStr | Recovering Image Quality in Low-Dose Pediatric Renal Scintigraphy Using Deep Learning |
| title_full_unstemmed | Recovering Image Quality in Low-Dose Pediatric Renal Scintigraphy Using Deep Learning |
| title_short | Recovering Image Quality in Low-Dose Pediatric Renal Scintigraphy Using Deep Learning |
| title_sort | recovering image quality in low dose pediatric renal scintigraphy using deep learning |
| topic | <sup>99m</sup>Tc-MAG3 deep learning noise reduction pediatric renal scintigraphy medical imaging |
| url | https://www.mdpi.com/2313-433X/11/3/88 |
| work_keys_str_mv | AT martaarsenio recoveringimagequalityinlowdosepediatricrenalscintigraphyusingdeeplearning AT ricardovigario recoveringimagequalityinlowdosepediatricrenalscintigraphyusingdeeplearning AT anammota recoveringimagequalityinlowdosepediatricrenalscintigraphyusingdeeplearning |