Arsénio Marta, Vigário Ricardo, Mota Ana M
Physics Department, NOVA School of Science and Technology, NOVA University of Lisbon, 2829-516 Caparica, Portugal.
Laboratory for Instrumentation Biomedical Engineering and Radiation Physis, NOVA University of Lisbon, 2829-516 Caparica, Portugal.
J Imaging. 2025 Mar 19;11(3):88. doi: 10.3390/jimaging11030088.
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.
本研究的目的是提出一种先进的图像增强策略，以应对小儿肾闪烁扫描中降低辐射剂量的挑战。使用了来自公共动态肾闪烁扫描数据库的数据。基于噪声更大的图像，对四种去噪神经网络（DnCNN、UDnCNN、DUDnCNN和AttnGAN）进行了评估。为了在细节损失最小的情况下评估降噪质量，使用了肾脏信噪比（SNR）和多尺度结构相似性（MS-SSIM）。尽管所有网络都能降低噪声，但UDnCNN在SNR和MS-SSIM之间实现了最佳平衡，从而使图像质量得到了最显著的改善。在临床实践中，采集到的所有数据的100%会被累加起来以生成最终图像。为了模拟剂量降低，我们只累加了50%，模拟辐射成比例减少。所提出的用于图像增强的深度学习方法确保了采集的所有帧中的一半可能产生与完整数据集相当的结果，这表明降低患者的辐射暴露是可行的。本研究表明，所评估的神经网络可以显著提高肾闪烁扫描图像质量，有助于以较低辐射剂量进行高质量成像，这将使儿科人群受益匪浅。
