环境去噪扩散生成对抗网络：从噪声图像数据建立随机对象模型
Ambient Denoising Diffusion Generative Adversarial Networks for Establishing Stochastic Object Models from Noisy Image Data
AI总结：
提出环境去噪扩散生成对抗网络（ADDGAN），从含噪声医学图像中学习随机对象模型，在CT和DBT图像上优于AmbientGAN。
摘要：
人们普遍认为，医学成像系统应通过基于任务的图像质量（IQ）指标进行客观评估，这些指标理想情况下应考虑到测量图像数据中所有随机性的来源，包括待成像对象集合中的变异性。能够从对象分布中随机抽取样本的随机对象模型（SOM）可用于表征对象变异性。为了建立用于基于任务的IQ分析的真实SOM，期望使用实验图像数据。然而，从医学成像系统获取的实验图像数据会受到测量噪声的影响。先前的工作研究了采用增强生成对抗网络（GAN）的深度生成模型（DGM）——AmbientGAN——从噪声测量图像数据建立SOM的能力。最近，去噪扩散模型（DDM）已成为用于图像合成的领先DGM，并能产生优于GAN的图像质量。然而，原始DDM由于去噪步骤中的高斯假设而具有缓慢的图像生成过程。更近期，提出了去噪扩散GAN（DDGAN），以在保持与原始DDM相当的高生成图像质量的同时实现快速图像生成。在这项工作中，我们提出了一种增强的DDGAN架构——环境DDGAN（ADDGAN），用于从噪声图像数据学习SOM。进行了考虑临床计算机断层扫描（CT）图像和数字乳腺断层合成（DBT）图像的数值研究。展示了所提出的ADDGAN从噪声图像数据学习真实SOM的能力。结果表明，ADDGAN在合成具有复杂纹理的高分辨率医学图像方面显著优于先进的AmbientGAN模型。
英文摘要：
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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