用大模型评估低剂量CT图像质量，自动识别噪声模糊等问题
LMM-IQA: Image Quality Assessment for Low-Dose CT Imaging
- 基于大模型生成图像质量数值评分和文字描述
- 多策略融合使评估结果与医生评分高度相关
- 输出可解释，适合临床辅助诊断场景
低剂量计算机断层扫描（CT）通过降低辐射剂量显著提升患者安全性，但伴随噪声增加、模糊和对比度下降，影响诊断质量。因此，图像质量评估的一致性与鲁棒性对临床应用至关重要。本文提出一种基于大语言模型的图像质量评估系统，能够生成数值评分及关于噪声、模糊和对比度损失等退化的文本描述。同时，系统性地评估了从零样本到元数据融合、错误反馈等多种推理策略，揭示了各方法对整体性能的渐进贡献。最终评估结果不仅与医生评分高度相关，还提供可解释输出，增强临床工作流价值。代码已开源：https://github.com/itu-biai/lmms_ldct_iqa。
原文摘要 · Abstract (English)
Low-dose computed tomography (CT) represents a significant improvement in patient safety through lower radiation doses, but increased noise, blur, and contrast loss can diminish diagnostic quality. Therefore, consistency and robustness in image quality assessment become essential for clinical applications. In this study, we propose an LLM-based quality assessment system that generates both numerical scores and textual descriptions of degradations such as noise, blur, and contrast loss. Furthermore, various inference strategies - from the zero-shot approach to metadata integration and error feedback - are systematically examined, demonstrating the progressive contribution of each method to overall performance. The resultant assessments yield not only highly correlated scores but also interpretable output, thereby adding value to clinical workflows. The source codes of our study are available at https://github.com/itu-biai/lmms_ldct_iqa.
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