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...

Full description

Saved in:
Bibliographic Details
Published in:Journal of imaging Vol. 11; no. 6; p. 197
Main Authors: Bouzianis, Nikolaos, Stathopoulos, Ioannis, Valsamaki, Pipitsa, Rapti, Efthymia, Trikopani, Ekaterini, Apostolidou, Vasiliki, Kotini, Athanasia, Zissimopoulos, Athanasios, Adamopoulos, Adam, Karavasilis, Efstratios
Format: Journal Article
Language:English
Published: Switzerland MDPI AG 14.06.2025
MDPI
Subjects:
Algorithms
Artificial intelligence
bone scintigraphy
Cameras
Comparative analysis
convolutional autoencoder
Data acquisition
Datasets
Deep learning
Design
Drug dosages
Image acquisition
image denoising
Image processing
Image quality
Image reconstruction
low-dose imaging
Machine learning
Medical equipment
Medical imaging
Metabolism
Methods
Noise reduction
Nuclear medicine
Nuclear safety
Patients
Radiation
Radiation effects
Radioisotope scanning
Scintigraphy
Signal to noise ratio
Supervised learning
Technology application
Tomography
Greece
Germany
deep learning
bone scintigraphy
nuclear medicine
convolutional autoencoder
low-dose imaging
image denoising
artificial intelligence
ISSN:2313-433X, 2313-433X
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
content type line 23
ISSN:2313-433X
2313-433X
DOI:10.3390/jimaging11060197