Text-image alignment for ILD imaging: linking CXR evidence to CT quantification.

Abstract

Assessment of Interstitial Lung Disease (ILD) relies on chest radiographs (CXR) for screening and computed tomography (CT) for definitive quantification. However, current AI pipelines typically treat these modalities in isolation, leading to report hallucinations and cross-modal inconsistencies. To address this fragmentation, we propose a framework (ARCTIC-ILD) that aligns CXR-derived textual evidence with CT-level segmentation and quantification. The system first employs a calibrated CXR evidence extractor to map radiographs to ILD-specific terminology, producing structured findings. These findings condition a terminology-to-mask module that utilizes lightweight cross-attention adapters to generate lobe-aware CT masks and burden estimates. Crucially, an explicit vision-language audit enforces consistency between the generated text and quantitative data. Evaluations on paired CXR-CT cohorts demonstrate that the framework significantly reduces text hallucination and improves phrase-to-mask alignment without incurring additional inference latency. By coupling reporting with quantification under an auditable protocol, this approach aligns with clinical workflows, serving as a robust assistant for triage, structured reporting, and longitudinal follow-up.