PDF(Pubmed)
Abstract：
Objective.The study aimed to generate synthetic contrast-enhanced Dual-energy CT (CE-DECT) images from non-contrast single-energy CT (SECT) scans, addressing the limitations posed by the scarcity of DECT scanners and the health risks associated with iodinated contrast agents, particularly for high-risk patients.Approach.A conditional denoising diffusion probabilistic model (C-DDPM) was utilized to create synthetic images. Imaging data were collected from 130 head-and-neck (HN) cancer patients who had undergone both non-contrast SECT and CE-DECT scans.Main Results.The performance of the C-DDPM was evaluated using Mean Absolute Error (MAE), Structural Similarity Index (SSIM), and Peak Signal-to-Noise Ratio (PSNR). The results showed MAE values of 27.37±3.35 Hounsfield Units (HU) for high-energy CT (H-CT) and 24.57±3.35HU for low-energy CT (L-CT), SSIM values of 0.74±0.22 for H-CT and 0.78±0.22 for L-CT, and PSNR values of 18.51±4.55 decibels (dB) for H-CT and 18.91±4.55 dB for L-CT.Significance.The study demonstrates the efficacy of the deep learning model in producing high-quality synthetic CE-DECT images, which significantly benefits radiation therapy planning. This approach provides a valuable alternative imaging solution for facilities lacking DECT scanners and for patients who are unsuitable for iodine contrast imaging, thereby enhancing the reach and effectiveness of advanced imaging in cancer treatment planning.
摘要：
在这项研究中,开发了一种利用条件去噪扩散概率模型（C-DDPM）的深度学习方法，以从非对比单能量CT（SECT）扫描中创建合成对比增强双能量CT（CE-DECT）图像。CE-DECT扫描对于生成碘密度图和描绘目标和危险器官（OAR）至关重要。在放射治疗计划的标准CT模拟期间，这是必不可少的，但经常受到双能量CT（DECT）扫描仪可用性有限的限制。为了应对这一挑战，我们提出的方法提供了一个有价值的替代方案，减轻与碘化造影剂相关的健康风险，特别是那些高危患者。在这项研究中,从130名头颈部（HN）癌症患者收集影像学数据，谁经历了非对比SECT和CE-DECT扫描。使用平均绝对误差(MAE)、结构相似性指数(SSIM)，和峰值信噪比（PSNR）。评估显示了有希望的结果，高能CT（H-CT）的MAE值为27.37±3.35Hounsfield单位（HU），低能CT（L-CT）的MAE值为24.57±3.35HU，H-CT的SSIM值为0.74±0.22，L-CT的SSIM值为0.78±0.22，H-CT的PSNR值为18.51±4.55分贝（dB），L-CT的PSNR值为18.91±4.55dB。这些指标突出了深度学习模型的功效及其通过生成合成对比剂DECT来显著受益于放射治疗计划的潜力，即使在缺乏DECT扫描仪的设施中。此外,它为不适合碘对比成像的患者提供了更安全的替代成像解决方案，从而扩大了先进成像在癌症治疗计划中的应用范围和有效性。
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