The paper introduces BIMCV-R, a groundbreaking dataset for 3D CT text-image retrieval, comprising 8,069 3D CT volumes and over 2 million slices, paired with radiological reports. To address the lack of robust evaluation benchmarks and curated datasets in 3D medical text-image retrieval, the authors develop MedFinder, a dual-stream network architecture that leverages large language models to enhance retrieval capabilities. MedFinder employs a text sampler, visual feature extraction, and similarity matching to achieve effective information retrieval in multimodal and keyword retrieval tasks. The dataset and method are designed to assist clinicians in diagnostic processes and reduce their workload by facilitating the search for similar case studies. The paper also includes a detailed methodology, experimental results, and an ablation study, demonstrating the effectiveness of the proposed approach. The BIMCV-R dataset and MedFinder are available for research and development in 3D medical image analysis technologies.The paper introduces BIMCV-R, a groundbreaking dataset for 3D CT text-image retrieval, comprising 8,069 3D CT volumes and over 2 million slices, paired with radiological reports. To address the lack of robust evaluation benchmarks and curated datasets in 3D medical text-image retrieval, the authors develop MedFinder, a dual-stream network architecture that leverages large language models to enhance retrieval capabilities. MedFinder employs a text sampler, visual feature extraction, and similarity matching to achieve effective information retrieval in multimodal and keyword retrieval tasks. The dataset and method are designed to assist clinicians in diagnostic processes and reduce their workload by facilitating the search for similar case studies. The paper also includes a detailed methodology, experimental results, and an ablation study, demonstrating the effectiveness of the proposed approach. The BIMCV-R dataset and MedFinder are available for research and development in 3D medical image analysis technologies.