  The burgeoning integration of 3D medical imaging into healthcare has led to a
substantial increase in the workload of medical professionals. To assist
clinicians in their diagnostic processes and alleviate their workload, the
development of a robust system for retrieving similar case studies presents a
viable solution. While the concept holds great promise, the field of 3D medical
text-image retrieval is currently limited by the absence of robust evaluation
benchmarks and curated datasets. To remedy this, our study presents a
groundbreaking dataset, {BIMCV-R}, which includes an extensive collection of
8,069 3D CT volumes, encompassing over 2 million slices, paired with their
respective radiological reports. Expanding upon the foundational work of our
dataset, we craft a retrieval strategy, MedFinder. This approach employs a
dual-stream network architecture, harnessing the potential of large language
models to advance the field of medical image retrieval beyond existing
text-image retrieval solutions. It marks our preliminary step towards
developing a system capable of facilitating text-to-image, image-to-text, and
keyword-based retrieval tasks. Our project is available at
\url{https://huggingface.co/datasets/cyd0806/BIMCV-R}.
