论文标题
低剂量CT使用20 $ \ times $速度的Denoising扩散概率模型
Low-Dose CT Using Denoising Diffusion Probabilistic Model for 20$\times$ Speedup
论文作者
论文摘要
低剂量计算机断层扫描（LDCT）是过去几十年来放射学领域的重要主题。 LDCT降低了电离辐射引起的患者健康风险，但也导致信噪比较低（SNR）和诊断性能的潜在妥协。在本文中，为了提高LDCT降解性能，我们介绍了有条件的降级扩散概率模型（DDPM），并以较高的计算效率显示令人鼓舞的结果。具体而言，鉴于原始DDPM模型的高采样成本，我们适应了快速的普通微分方程（ODE）求解器，以提高改进的采样效率。实验表明，加速的DDPM可以实现20倍加速，而不会损害图像质量。
Low-dose computed tomography (LDCT) is an important topic in the field of radiology over the past decades. LDCT reduces ionizing radiation-induced patient health risks but it also results in a low signal-to-noise ratio (SNR) and a potential compromise in the diagnostic performance. In this paper, to improve the LDCT denoising performance, we introduce the conditional denoising diffusion probabilistic model (DDPM) and show encouraging results with a high computational efficiency. Specifically, given the high sampling cost of the original DDPM model, we adapt the fast ordinary differential equation (ODE) solver for a much-improved sampling efficiency. The experiments show that the accelerated DDPM can achieve 20x speedup without compromising image quality.
