PET2Rep: Towards Vision-Language Model-Drived Automated Radiology Report Generation for Positron Emission Tomography

• URL: http://arxiv.org/abs/2508.04062v1
• Date: Wed, 06 Aug 2025 03:46:51 GMT
• Title: PET2Rep: Towards Vision-Language Model-Drived Automated Radiology Report Generation for Positron Emission Tomography
• Authors: Yichi Zhang, Wenbo Zhang, Zehui Ling, Gang Feng, Sisi Peng, Deshu Chen, Yuchen Liu, Hongwei Zhang, Shuqi Wang, Lanlan Li, Limei Han, Yuan Cheng, Zixin Hu, Yuan Qi, Le Xue,
• Abstract summary: Radiology reports are essential for clinical decision making, yet their manual creation is labor-intensive and time-consuming.<n>Recent advancements of vision-language models (VLMs) have shown strong potential in medical applications.<n>We introduce PET2Rep, a benchmark for evaluation of general and medical VLMs for radiology report generation for PET images.
• Score: 24.091435019102587
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Positron emission tomography (PET) is a cornerstone of modern oncologic and neurologic imaging, distinguished by its unique ability to illuminate dynamic metabolic processes that transcend the anatomical focus of traditional imaging technologies. Radiology reports are essential for clinical decision making, yet their manual creation is labor-intensive and time-consuming. Recent advancements of vision-language models (VLMs) have shown strong potential in medical applications, presenting a promising avenue for automating report generation. However, existing applications of VLMs in the medical domain have predominantly focused on structural imaging modalities, while the unique characteristics of molecular PET imaging have largely been overlooked. To bridge the gap, we introduce PET2Rep, a large-scale comprehensive benchmark for evaluation of general and medical VLMs for radiology report generation for PET images. PET2Rep stands out as the first dedicated dataset for PET report generation with metabolic information, uniquely capturing whole-body image-report pairs that cover dozens of organs to fill the critical gap in existing benchmarks and mirror real-world clinical comprehensiveness. In addition to widely recognized natural language generation metrics, we introduce a series of clinical efficiency metrics to evaluate the quality of radiotracer uptake pattern description in key organs in generated reports. We conduct a head-to-head comparison of 30 cutting-edge general-purpose and medical-specialized VLMs. The results show that the current state-of-the-art VLMs perform poorly on PET report generation task, falling considerably short of fulfilling practical needs. Moreover, we identify several key insufficiency that need to be addressed to advance the development in medical applications.

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