In computed tomography (CT) imaging, optimizing the balance between radiation dose and image quality is crucial due to the potentially harmful effects of radiation on patients. Although subjective assessments by radiologists are considered the gold standard in medical imaging, these evaluations can be time-consuming and costly. Thus, objective methods, such as the peak signal-to-noise ratio and structural similarity index measure, are often employed as alternatives. However, these metrics, initially developed for natural images, may not fully encapsulate the radiologists’ assessment process. Consequently, interest in developing deep learning-based image quality assessment (IQA) methods that more closely align with radiologists’ perceptions is growing. A significant barrier to this development has been the absence of open-source datasets and benchmark models specific to CT IQA. Addressing these challenges, we organized the Low-dose Computed Tomography Perceptual Image Quality Assessment Challenge in conjunction with the Medical Image Computing and Computer Assisted Intervention 2023. This event introduced the first open-source CT IQA dataset, consisting of 1,000 CT images of various quality, annotated with radiologists’ assessment scores. As a benchmark, this challenge offers a comprehensive analysis of six submitted methods, providing valuable insight into their performance. This paper presents a summary of these methods and insights. This challenge underscores the potential for developing no-reference IQA methods that could exceed the capabilities of full-reference IQA methods, making a significant contribution to the research community with this novel dataset. The dataset is accessible at .

中文翻译：

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低剂量计算机断层扫描感知图像质量评估


在计算机断层扫描 (CT) 成像中，由于辐射对患者具有潜在的有害影响，优化辐射剂量和图像质量之间的平衡至关重要。尽管放射科医生的主观评估被认为是医学成像的黄金标准，但这些评估可能既耗时又昂贵。因此，客观方法，例如峰值信噪比和结构相似性指数测量，通常被用作替代方法。然而，这些最初为自然图像开发的指标可能无法完全概括放射科医生的评估过程。因此，人们对开发更符合放射科医生认知的基于深度学习的图像质量评估（IQA）方法的兴趣与日俱增。这一发展的一个重大障碍是缺乏特定于 CT IQA 的开源数据集和基准模型。为了应对这些挑战，我们结合 2023 年医学图像计算和计算机辅助干预组织了低剂量计算机断层扫描感知图像质量评估挑战赛。该活动推出了第一个开源 CT IQA 数据集，由 1,000 张不同质量的 CT 图像组成，注释有放射科医生的评估分数。作为基准，该挑战对六种提交的方法进行了全面分析，提供了对其性能的宝贵见解。本文总结了这些方法和见解。这一挑战强调了开发无参考 IQA 方法的潜力，这些方法可能超越全参考 IQA 方法的能力，从而利用这个新颖的数据集为研究界做出重大贡献。该数据集可在 访问。