Blind CT Image Quality Assessment Using DDPM-Derived Content and Transformer-Based Evaluator

Yongyi Shi;Wenjun Xia;Ge Wang;Xuanqin Mou
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Abstract

Lowering radiation dose per view and utilizing sparse views per scan are two common CT scan modes, albeit often leading to distorted images characterized by noise and streak artifacts. Blind image quality assessment (BIQA) strives to evaluate perceptual quality in alignment with what radiologists perceive, which plays an important role in advancing low-dose CT reconstruction techniques. An intriguing direction involves developing BIQA methods that mimic the operational characteristic of the human visual system (HVS). The internal generative mechanism (IGM) theory reveals that the HVS actively deduces primary content to enhance comprehension. In this study, we introduce an innovative BIQA metric that emulates the active inference process of IGM. Initially, an active inference module, implemented as a denoising diffusion probabilistic model (DDPM), is constructed to anticipate the primary content. Then, the dissimilarity map is derived by assessing the interrelation between the distorted image and its primary content. Subsequently, the distorted image and dissimilarity map are combined into a multi-channel image, which is inputted into a transformer-based image quality evaluator. By leveraging the DDPM-derived primary content, our approach achieves competitive performance on a low-dose CT dataset.
使用 DDPM 派生内容和基于变换器的评估器进行盲 CT 图像质量评估。
降低每个视图的辐射剂量和利用每次扫描的稀疏视图是两种常见的 CT 扫描模式,但往往会导致以噪声和条纹伪影为特征的图像失真。盲图像质量评估(BIQA)致力于评估与放射科医生感知一致的感知质量,这在推进低剂量 CT 重建技术方面发挥着重要作用。一个令人感兴趣的方向是开发能模仿人类视觉系统(HVS)运行特征的 BIQA 方法。内部生成机制(IGM)理论揭示了 HVS 会主动推导主要内容以提高理解能力。在本研究中,我们引入了一种创新的 BIQA 指标,以模拟 IGM 的主动推理过程。首先,构建一个以去噪扩散概率模型(DDPM)形式实现的主动推理模块,以预测主要内容。然后,通过评估失真图像与其主要内容之间的相互关系得出相似性图。随后,将扭曲图像和差异图组合成多通道图像,并将其输入基于变换器的图像质量评估器。通过利用从 DDPM 派生的主要内容,我们的方法在低剂量 CT 数据集上实现了具有竞争力的性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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