FSP-Diff: 全谱先验增强双域潜在扩散用于超低剂量能谱CT重建
FSP-Diff: Full-Spectrum Prior-Enhanced DualDomain Latent Diffusion for Ultra-Low-Dose Spectral CT Reconstruction
AI总结：
FSP-Diff通过全谱先验增强双域潜在扩散框架，提升超低剂量能谱CT重建的图像质量和效率。
摘要：
能谱CT利用光电计数探测器在材料鉴别和组织表征方面具有巨大潜力。然而，在超低剂量条件下，能量特定投影中的信号噪声比（SNR）显著下降，导致重建图像中出现严重伪影和结构细节丢失。为此，我们提出FSP-Diff，一种全谱先验增强双域潜在扩散框架，用于超低剂量能谱CT重建。我们的框架整合了三个核心策略：1）互补特征构建：我们将直接图像重建与投影域去噪结果相结合。前者在重噪声中保留潜在纹理细节，后者提供稳定的结构框架以平衡细节保真度和噪声抑制。2）全谱先验整合：通过将多能投影融合为高SNR全谱图像，我们建立了一个统一的结构参考，指导所有能谱 bin 的重建。3）高效潜在扩散合成：为缓解高维能谱数据的高计算负担，多路径特征被嵌入到紧凑的潜在空间中。这使扩散过程能够在较低维流形上促进交互式特征融合，实现加速重建的同时保持精细细节恢复。在模拟和真实世界数据集上的广泛实验表明，FSP-Diff在图像质量和计算效率上均显著优于现有最先进方法，凸显了其在临床可行超低剂量能谱CT成像中的潜力。
英文摘要：
Spectral computed tomography (CT) with photon-counting detectors holds immense potential for material discrimination and tissue characterization. However, under ultra-low-dose conditions, the sharply degraded signal-to-noise ratio (SNR) in energy-specific projections poses a significant challenge, leading to severe artifacts and loss of structural details in reconstructed images. To address this, we propose FSP-Diff, a full-spectrum prior-enhanced dual-domain latent diffusion framework for ultra-low-dose spectral CT reconstruction. Our framework integrates three core strategies: 1) Complementary Feature Construction: We integrate direct image reconstructions with projection-domain denoised results. While the former preserves latent textural nuances amidst heavy noise, the latter provides a stable structural scaffold to balance detail fidelity and noise suppression. 2) Full-Spectrum Prior Integration: By fusing multi-energy projections into a high-SNR full-spectrum image, we establish a unified structural reference that guides the reconstruction across all energy bins. 3) Efficient Latent Diffusion Synthesis: To alleviate the high computational burden of high-dimensional spectral data, multi-path features are embedded into a compact latent space. This allows the diffusion process to facilitate interactive feature fusion in a lower-dimensional manifold, achieving accelerated reconstruction while maintaining fine-grained detail restoration. Extensive experiments on simulated and real-world datasets demonstrate that FSP-Diff significantly outperforms state-of-the-art methods in both image quality and computational efficiency, underscoring its potential for clinically viable ultra-low-dose spectral CT imaging.
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