  Automatic radiology report generation can significantly benefit the
labor-intensive process of report writing by radiologists, especially for 3D
radiographs like CT scans, which are crucial for broad clinical diagnostics yet
underexplored compared to 2D radiographs. Existing methods often handle 3D
volumes either slice-wise or with aggressive downsampling due to current GPU
memory limitations, which results in a loss of the inherent 3D nature and
critical details. To overcome these issues, we introduce a novel framework that
efficiently and effectively generates radiology reports for high-resolution
(HR) 3D volumes, based on large language models (LLMs). Specifically, our
framework utilizes low-resolution (LR) visual tokens as queries to mine
information from HR tokens, preserving detailed HR information while reducing
computational costs by only processing HR informed LR visual queries. Further
benefiting the field, we curate and release BIMCV-RG, a new dataset with 5,328
HR 3D volumes and paired reports, establishing the first benchmarks for report
generation from 3D HR medical images. Our method consistently surpasses
existing methods on this benchmark across three different settings:
normal-resolution, high-resolution inputs, and zero-shot domain transfer, all
at an acceptable computational cost, trainable on a single A100-80G.
