CAS-IQA: Teaching Vision-Language Models for Synthetic Angiography Quality Assessment
Journal:
arXiv
Published Date:
May 23, 2025
Abstract
Synthetic X-ray angiographies generated by modern generative models hold
great potential to reduce the use of contrast agents in vascular interventional
procedures. However, low-quality synthetic angiographies can significantly
increase procedural risk, underscoring the need for reliable image quality
assessment (IQA) methods. Existing IQA models, however, fail to leverage
auxiliary images as references during evaluation and lack fine-grained,
task-specific metrics necessary for clinical relevance. To address these
limitations, this paper proposes CAS-IQA, a vision-language model (VLM)-based
framework that predicts fine-grained quality scores by effectively
incorporating auxiliary information from related images. In the absence of
angiography datasets, CAS-3K is constructed, comprising 3,565 synthetic
angiographies along with score annotations. To ensure clinically meaningful
assessment, three task-specific evaluation metrics are defined. Furthermore, a
Multi-path featUre fuSion and rouTing (MUST) module is designed to enhance
image representations by adaptively fusing and routing visual tokens to
metric-specific branches. Extensive experiments on the CAS-3K dataset
demonstrate that CAS-IQA significantly outperforms state-of-the-art IQA methods
by a considerable margin.
