Bouzianis Nikolaos, Stathopoulos Ioannis, Valsamaki Pipitsa, Rapti Efthymia, Trikopani Ekaterini, Apostolidou Vasiliki, Kotini Athanasia, Zissimopoulos Athanasios, Adamopoulos Adam, Karavasilis Efstratios
Medical Physics Laboratory, School of Medicine, Democritus University of Thrace, 69100 Alexandroupolis, Greece.
Nuclear Medicine Department, University General Hospital of Alexandroupolis, Dragana, 69100 Alexandroupolis, Greece.
J Imaging. 2025 Jun 14;11(6):197. doi: 10.3390/jimaging11060197.
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.
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.
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.
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.
本研究提出一种新颖的深度学习方法，即使用增强卷积自编码器（ECAE）来增强低剂量骨闪烁扫描图像，旨在在保持诊断质量的同时减少患者的辐射暴露，诊断质量通过基于专家的定量图像指标和定性评估来评估。
利用来自105名患者的真实世界低剂量和全剂量配对图像开发了一个监督学习框架。数据是在亚历山德鲁波利斯大学总医院核医学科使用标准临床伽马相机采集的。ECAE架构集成了多尺度特征提取、通道注意力机制和高效残差块，以从低剂量输入重建高质量图像。该模型使用定量指标——峰值信噪比（PSNR）和结构相似性指数（SSIM）——以及核医学专家的定性评估进行训练和验证。
该模型在所有测试剂量水平下的PSNR和SSIM方面均取得了显著改进，尤其是在全剂量的30%至70%之间。专家评估证实，去噪图像中解剖结构的可见性增强、噪声降低且诊断细节得以保留。在盲法评估中，在所有病例的66%中，去噪图像比原始全剂量扫描更受青睐，在30%至70%剂量范围内的病例中，这一比例为61%。
所提出的ECAE模型能够有效地从大幅降低剂量的采集数据中重建高质量的骨闪烁扫描图像。这种方法支持核医学成像中的剂量降低，同时保持甚至提高诊断可信度，在患者安全、工作流程效率和环境影响方面具有实际益处。
