用压缩空间扩散模型加速低剂量CT去噪，60倍提速仍保高质量。
MAN: Latent Diffusion Enhanced Multistage Anti-Noise Network for Efficient and High-Quality Low-Dose CT Image Denoising
- 在潜空间用自编码器压缩图像，实现快速确定性去噪。
- 推理速度比像素级扩散模型快60倍以上，PSNR/SSIM接近顶尖模型。
- 适合临床场景，兼顾高保真与实时性，推动生成模型落地医疗。
尽管扩散模型在低剂量计算机断层扫描（LDCT）去噪任务中达到新质量标杆，但其临床应用受限于极高的计算开销，单次扫描推理时间常超过数千秒。为此，我们提出MAN——一种基于潜空间扩散增强的多阶段抗噪网络，用于高效且高质量的低剂量CT图像去噪。方法通过感知优化的自编码器在压缩潜空间中操作，使基于注意力的条件U-Net实现快速、确定性的条件去噪扩散过程，显著降低计算开销。在LDCT和Projection数据集上，模型在感知质量上超越传统CNN/GAN方法，重建保真度媲美如DDPM、Dn-Dp等计算密集型扩散模型。最关键的是，推理阶段速度比典型像素空间扩散去噪器快60倍以上，同时保持竞争力的PSNR和SSIM指标。本工作为先进生成模型在医学影像中的临床可行性提供了切实路径。
原文摘要 · Abstract (English)
While diffusion models have set a new benchmark for quality in Low-Dose Computed Tomography (LDCT) denoising, their clinical adoption is critically hindered by extreme computational costs, with inference times often exceeding thousands of seconds per scan. To overcome this barrier, we introduce MAN, a Latent Diffusion Enhanced Multistage Anti-Noise Network for Efficient and High-Quality Low-Dose CT Image Denoising task. Our method operates in a compressed latent space via a perceptually-optimized autoencoder, enabling an attention-based conditional U-Net to perform the fast, deterministic conditional denoising diffusion process with drastically reduced overhead. On the LDCT and Projection dataset, our model achieves superior perceptual quality, surpassing CNN/GAN-based methods while rivaling the reconstruction fidelity of computationally heavy diffusion models like DDPM and Dn-Dp. Most critically, in the inference stage, our model is over 60x faster than representative pixel space diffusion denoisers, while remaining competitive on PSNR/SSIM scores. By bridging the gap between high fidelity and clinical viability, our work demonstrates a practical path forward for advanced generative models in medical imaging.
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