Dose Reduction in Scintigraphic Imaging Through Enhanced Convolutional Autoencoder-Based Denoising
Objective: This study proposes a novel deep learning approach for enhancing low-dose bone scintigraphy images using an Enhanced Convolutional Autoencoder (ECAE), aiming to reduce patient radiation exposure while preserving diagnostic quality, as assessed by both expert-based quantitative image metri...
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MDPI AG
2025-06-01
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| Series: | Journal of Imaging |
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| Online Access: | https://www.mdpi.com/2313-433X/11/6/197 |
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| author | Nikolaos Bouzianis Ioannis Stathopoulos Pipitsa Valsamaki Efthymia Rapti Ekaterini Trikopani Vasiliki Apostolidou Athanasia Kotini Athanasios Zissimopoulos Adam Adamopoulos Efstratios Karavasilis |
| author_facet | Nikolaos Bouzianis Ioannis Stathopoulos Pipitsa Valsamaki Efthymia Rapti Ekaterini Trikopani Vasiliki Apostolidou Athanasia Kotini Athanasios Zissimopoulos Adam Adamopoulos Efstratios Karavasilis |
| author_sort | Nikolaos Bouzianis |
| collection | DOAJ |
| description | Objective: This study proposes a novel deep learning approach for enhancing low-dose bone scintigraphy images using an Enhanced Convolutional Autoencoder (ECAE), aiming to reduce patient radiation exposure while preserving diagnostic quality, as assessed by both expert-based quantitative image metrics and qualitative evaluation. Methods: A supervised learning framework was developed using real-world paired low- and full-dose images from 105 patients. Data were acquired using standard clinical gamma cameras at the Nuclear Medicine Department of the University General Hospital of Alexandroupolis. The ECAE architecture integrates multiscale feature extraction, channel attention mechanisms, and efficient residual blocks to reconstruct high-quality images from low-dose inputs. The model was trained and validated using quantitative metrics—Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM)—alongside qualitative assessments by nuclear medicine experts. Results: The model achieved significant improvements in both PSNR and SSIM across all tested dose levels, particularly between 30% and 70% of the full dose. Expert evaluation confirmed enhanced visibility of anatomical structures, noise reduction, and preservation of diagnostic detail in denoised images. In blinded evaluations, denoised images were preferred over the original full-dose scans in 66% of all cases, and in 61% of cases within the 30–70% dose range. Conclusion: The proposed ECAE model effectively reconstructs high-quality bone scintigraphy images from substantially reduced-dose acquisitions. This approach supports dose reduction in nuclear medicine imaging while maintaining—or even enhancing—diagnostic confidence, offering practical benefits in patient safety, workflow efficiency, and environmental impact. |
| format | Article |
| id | doaj-art-654ddc1fb29841b8868e86680d82ff01 |
| institution | Kabale University |
| issn | 2313-433X |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Imaging |
| spelling | doaj-art-654ddc1fb29841b8868e86680d82ff012025-08-20T03:27:17ZengMDPI AGJournal of Imaging2313-433X2025-06-0111619710.3390/jimaging11060197Dose Reduction in Scintigraphic Imaging Through Enhanced Convolutional Autoencoder-Based DenoisingNikolaos Bouzianis0Ioannis Stathopoulos1Pipitsa Valsamaki2Efthymia Rapti3Ekaterini Trikopani4Vasiliki Apostolidou5Athanasia Kotini6Athanasios Zissimopoulos7Adam Adamopoulos8Efstratios Karavasilis9Medical Physics Laboratory, School of Medicine, Democritus University of Thrace, 69100 Alexandroupolis, Greece2nd Department of Radiology, Medical School, Attikon University Hospital, National and Kapodistrian University of Athens, 11527 Athens, GreeceNuclear Medicine Department, University General Hospital of Alexandroupolis, Dragana, 69100 Alexandroupolis, GreeceNuclear Medicine Department, University General Hospital of Alexandroupolis, Dragana, 69100 Alexandroupolis, GreeceMedical Physics Laboratory, School of Medicine, Democritus University of Thrace, 69100 Alexandroupolis, GreeceNuclear Medicine Department, University General Hospital of Alexandroupolis, Dragana, 69100 Alexandroupolis, GreeceMedical Physics Laboratory, School of Medicine, Democritus University of Thrace, 69100 Alexandroupolis, GreeceNuclear Medicine Department, Medical School, Democritus University of Thrace, Dragana, 69100 Alexandroupolis, GreeceMedical Physics Laboratory, School of Medicine, Democritus University of Thrace, 69100 Alexandroupolis, GreeceMedical Physics Laboratory, School of Medicine, Democritus University of Thrace, 69100 Alexandroupolis, GreeceObjective: This study proposes a novel deep learning approach for enhancing low-dose bone scintigraphy images using an Enhanced Convolutional Autoencoder (ECAE), aiming to reduce patient radiation exposure while preserving diagnostic quality, as assessed by both expert-based quantitative image metrics and qualitative evaluation. Methods: A supervised learning framework was developed using real-world paired low- and full-dose images from 105 patients. Data were acquired using standard clinical gamma cameras at the Nuclear Medicine Department of the University General Hospital of Alexandroupolis. The ECAE architecture integrates multiscale feature extraction, channel attention mechanisms, and efficient residual blocks to reconstruct high-quality images from low-dose inputs. The model was trained and validated using quantitative metrics—Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM)—alongside qualitative assessments by nuclear medicine experts. Results: The model achieved significant improvements in both PSNR and SSIM across all tested dose levels, particularly between 30% and 70% of the full dose. Expert evaluation confirmed enhanced visibility of anatomical structures, noise reduction, and preservation of diagnostic detail in denoised images. In blinded evaluations, denoised images were preferred over the original full-dose scans in 66% of all cases, and in 61% of cases within the 30–70% dose range. Conclusion: The proposed ECAE model effectively reconstructs high-quality bone scintigraphy images from substantially reduced-dose acquisitions. This approach supports dose reduction in nuclear medicine imaging while maintaining—or even enhancing—diagnostic confidence, offering practical benefits in patient safety, workflow efficiency, and environmental impact.https://www.mdpi.com/2313-433X/11/6/197bone scintigraphyconvolutional autoencoderdeep learningimage denoisinglow-dose imagingnuclear medicine |
| spellingShingle | Nikolaos Bouzianis Ioannis Stathopoulos Pipitsa Valsamaki Efthymia Rapti Ekaterini Trikopani Vasiliki Apostolidou Athanasia Kotini Athanasios Zissimopoulos Adam Adamopoulos Efstratios Karavasilis Dose Reduction in Scintigraphic Imaging Through Enhanced Convolutional Autoencoder-Based Denoising Journal of Imaging bone scintigraphy convolutional autoencoder deep learning image denoising low-dose imaging nuclear medicine |
| title | Dose Reduction in Scintigraphic Imaging Through Enhanced Convolutional Autoencoder-Based Denoising |
| title_full | Dose Reduction in Scintigraphic Imaging Through Enhanced Convolutional Autoencoder-Based Denoising |
| title_fullStr | Dose Reduction in Scintigraphic Imaging Through Enhanced Convolutional Autoencoder-Based Denoising |
| title_full_unstemmed | Dose Reduction in Scintigraphic Imaging Through Enhanced Convolutional Autoencoder-Based Denoising |
| title_short | Dose Reduction in Scintigraphic Imaging Through Enhanced Convolutional Autoencoder-Based Denoising |
| title_sort | dose reduction in scintigraphic imaging through enhanced convolutional autoencoder based denoising |
| topic | bone scintigraphy convolutional autoencoder deep learning image denoising low-dose imaging nuclear medicine |
| url | https://www.mdpi.com/2313-433X/11/6/197 |
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