LMM-IQA: Image Quality Assessment for Low-Dose CT Imaging

TL;DR

This paper introduces an LLM-based system for assessing low-dose CT image quality, providing both numerical scores and textual descriptions of degradations, with various inference strategies evaluated for improved performance and interpretability.

Contribution

It presents a novel LLM-based approach for comprehensive image quality assessment in low-dose CT, combining scoring and interpretability, and systematically examines inference strategies.

Findings

High correlation between assessments and expert evaluations

Effective textual descriptions of image degradations

Progressive improvement with different inference strategies

Abstract

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.