RDDM: 一种基于残差驱动的高保真低剂量CT去噪模型
RDDM: A Residual-Driven Drifting Model for High-Fidelity Low-Dose CT Denoising
AI总结：
本文提出RDDM模型，通过引入残差漂移场实现高效且高保真的低剂量CT去噪，解决了传统方法在实时应用中的性能限制，实验表明其在去噪效果和速度上均优于现有方法。
摘要：
低剂量CT（LDCT）去噪在医学影像中仍然是一个重要但具有挑战性的问题。尽管最近基于学习的方法显示出有希望的性能，但那些使用经典像素级目标优化的模型往往会产生过度平滑的重建结果。现有的主流生成模型，如扩散模型，虽然在保真度上有所提升，但代价是昂贵的多步迭代推断，这限制了它们在实时应用中的实用性。为了解决这一差距，我们提出了一种残差驱动漂移模型（RDDM）以实现有效的、高效的和高保真的LDCT去噪。受最近提出的漂移模型的启发，RDDM通过残差漂移场将多步分布演变纳入训练动态中，从而实现一步去噪。具体来说，残差漂移场由LDCT与正常剂量CT（NDCT）之间的残差所产生的吸引力和生成残差所产生的排斥力共同形成。此外，通过调整参数设置并结合像素级监督，我们开发了三种RDDM变体，覆盖了从细节保留到更强噪声抑制的应用需求。广泛实验表明，RDDM在监督基线中实现了最先进的去噪性能。特别是，RDDM-Fine生成的重建结果与NDCT高度一致，实现了优越的PSNR和SSIM，同时具有最佳的FID值5.87，并保留了真实的解剖纹理。此外，RDDM支持即需即用的推断，仅需约15毫秒即可去噪一个单个的512x512 LDCT切片。这些结果确立了RDDM作为高保真和实时LDCT去噪在临床应用中的有前景的解决方案。
英文摘要：
Low-dose CT (LDCT) denoising remains an important yet challenging problem in medical imaging. Although recent learning-based methods have shown promising performance, those optimized using classical pixel-level objectives often produce over-smoothed reconstructions. Existing mainstream generative models, such as diffusion models, have improved fidelity at the cost of expensive multi-step iterative inference, which limits their practicality for real-time use. To address this gap, we propose a Residual-Driven Drifting Model (RDDM) for effective, efficient, and high-fidelity LDCT denoising. Inspired by the recently proposed Drifting Models, RDDM incorporates the multi-step distribution evolution into the training dynamics through a residual drifting field, thereby enabling one-step denoising. Specifically, the residual drifting field is formed by an attractive force induced by the residuals between LDCT and normal-dose CT (NDCT) and a repulsive force induced by the generated residuals. In addition, by adjusting the parameter settings and incorporating pixel-level supervision, we develop three RDDM variants, covering application needs from detail preservation to stronger noise suppression. Extensive experiments demonstrate that RDDM achieves state-of-the-art denoising performance among supervised baselines. In particular, RDDM-Fine produces reconstructions that are highly consistent with NDCT, achieving superior PSNR and SSIM together with the best FID of 5.87 while preserving realistic anatomical textures. Moreover, RDDM enables on-the-fly inference, requiring only about 15 ms to denoise a single 512 x 512 LDCT slice. These results establish RDDM as a promising solution for high-fidelity and real-time LDCT denoising in clinical applications.
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