<p>Accurate characterization of atherosclerotic plaques in intravascular ultrasound (IVUS) imaging is essential for evaluating coronary artery disease and guiding clinical interventions. Traditional methods rely on handcrafted features and rule-based algorithms, which lack adaptability to diverse lesion morphologies and offer limited explainability. To address these challenges, this work introduces PlaqueCap, a lesion-centered captioning framework that generates clinically meaningful, natural language descriptions directly from IVUS images. A central challenge is ensuring the generated text is grounded in the specific pathology of the lesion. PlaqueCap solves this by performing high-fidelity segmentation to localize the plaque, then using a Lesion Prompt Injection (LPI) module to inject spatial information into a pre-trained vision-language model, focusing on pathological characteristics. Experimental results on a curated IVUS dataset show PlaqueCap achieves accurate lesion localization and classification, producing detailed, clinically interpretable descriptions surpassing baselines in quantitative metrics and expert evaluation. This offers a paradigm for explainable AI in intravascular imaging and automated reporting in interventional cardiology.</p>