Image quality assessment (IQA) plays a critical role in optimizing radiation dose and developing novel medical imaging techniques in computed tomography (CT). Traditional IQA methods relying on hand-crafted features have limitations in summarizing the subjective perceptual experience of image quality. Recent deep learning-based approaches have demonstrated strong modeling capabilities and potential for medical IQA, but challenges remain regarding model generalization and perceptual accuracy. In this work, we propose a multi-scale distributions regression approach to predict quality scores by constraining the output distribution, thereby improving model generalization. Furthermore, we design a dual-branch alignment network to enhance feature extraction capabilities. Additionally, semi-supervised learning is introduced by utilizing pseudo-labels for unlabeled data to guide model training. Extensive qualitative experiments demonstrate the effectiveness of our proposed method for advancing the state-of-the-art in deep learning-based medical IQA. Code is available at: https://github.com/zunzhumu/MD-IQA.

翻译：图像质量评估（IQA）在优化辐射剂量和开发计算机断层扫描（CT）新型医学成像技术中发挥关键作用。传统依赖手工特征的IQA方法在总结图像质量的主观感知体验方面存在局限性。近期基于深度学习的方法展现出强大的建模能力与医学IQA潜力，但在模型泛化性和感知准确性方面仍面临挑战。本文提出一种多尺度分布回归方法，通过约束输出分布预测质量分数，从而提升模型泛化能力。进一步，我们设计了双分支对齐网络以增强特征提取能力。此外，引入半监督学习策略，利用未标注数据的伪标签指导模型训练。大量定性实验证明了所提方法在推动基于深度学习的医学IQA领域前沿进展方面的有效性。代码见：https://github.com/zunzhumu/MD-IQA。