用噪声医学图像训练生成模型，构建更真实的物体随机模型。
Ambient Denoising Diffusion Generative Adversarial Networks for Establishing Stochastic Object Models from Noisy Image Data
- 提出改进的扩散生成对抗网络，可在噪声图像中学习物体分布。
- 在CT和乳腺断层成像数据上验证，生成图像质量显著优于现有方法。
- 适合医学影像质量评估、复杂纹理图像生成任务的研究者使用。
医学影像系统应通过任务驱动的图像质量评估来客观评价，理想情况下需考虑图像数据中所有随机来源，包括被成像对象的变异性。可从对象分布中随机采样的随机物体模型（SOMs）可用于表征这种变异性。为建立真实可靠的SOMs以支持任务驱动的图像质量分析，宜采用实验获取的影像数据。然而，医疗成像系统采集的实验图像常受测量噪声影响。此前研究探索了基于增强生成对抗网络（GAN）的AmbientGAN在噪声数据中建立SOMs的能力。近期，去噪扩散模型（DDMs）作为主流生成模型，在图像合成方面表现出优于GAN的图像质量。但原版DDMs因去噪步骤中的高斯假设导致生成速度慢。最近提出的去噪扩散GAN（DDGAN）可在保持与原版DDMs相当的图像质量前提下实现快速生成。本文提出一种增强型DDGAN架构——环境去噪扩散生成对抗网络（ADDGAN），用于从噪声图像数据中学习SOMs。通过针对临床计算机断层扫描（CT）和数字乳腺断层成像（DBT）图像的数值研究，验证了所提ADDGAN在从噪声数据中学习真实SOMs方面的有效性。结果表明，ADDGAN在生成具有复杂纹理的高分辨率医学图像方面显著优于先进版AmbientGAN模型。
原文摘要 · Abstract (English)
It is widely accepted that medical imaging systems should be objectively assessed via task-based image quality (IQ) measures that ideally account for all sources of randomness in the measured image data, including the variation in the ensemble of objects to be imaged. Stochastic object models (SOMs) that can randomly draw samples from the object distribution can be employed to characterize object variability. To establish realistic SOMs for task-based IQ analysis, it is desirable to employ experimental image data. However, experimental image data acquired from medical imaging systems are subject to measurement noise. Previous work investigated the ability of deep generative models (DGMs) that employ an augmented generative adversarial network (GAN), AmbientGAN, for establishing SOMs from noisy measured image data. Recently, denoising diffusion models (DDMs) have emerged as a leading DGM for image synthesis and can produce superior image quality than GANs. However, original DDMs possess a slow image-generation process because of the Gaussian assumption in the denoising steps. More recently, denoising diffusion GAN (DDGAN) was proposed to permit fast image generation while maintain high generated image quality that is comparable to the original DDMs. In this work, we propose an augmented DDGAN architecture, Ambient DDGAN (ADDGAN), for learning SOMs from noisy image data. Numerical studies that consider clinical computed tomography (CT) images and digital breast tomosynthesis (DBT) images are conducted. The ability of the proposed ADDGAN to learn realistic SOMs from noisy image data is demonstrated. It has been shown that the ADDGAN significantly outperforms the advanced AmbientGAN models for synthesizing high resolution medical images with complex textures.
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