ACQUISITION-AWARE PATCH AGGREGATION FOR 3D BRAIN MRI REPORT GENERATION

A brain MRI examination provides rich diagnostic information through multiple contrasts and series, but at the cost of substantial volumetric data. Leveraging such volumetric information for radiology report generation poses a challenge, as a vision–language model must accommodate the extensive volumetric information within a limited number of visual tokens. Existing approaches typically sample a subset of slices or compress visual representations before language generation. While necessary for tractability, these strategies may overlook where clinically relevant evidence lies: sampling can omit images containing important findings, while compression can obscure fine-grained local information. To address this, we introduce MR-Weave, a 3D brain MRI report generation model that weaves complementary evidence across acquisitions into a unified representation within a fixed visual token budget. MR-Weave groups features from all acquisitions by depth and performs acquisition-aware patch aggregation, adaptively weighting acquisition features at each patch position. We further introduce axial geometry encoding, which augments the fused tokens with physical position and scale information to support location- and size-aware descriptions. We evaluate MR-Weave on 2,000 studies from the co-registered release of MR-RATE using an LLM-as-a-judge protocol across five clinical dimensions: detection, faithfulness, disease labeling, localization, and severity. MR-Weave achieves the highest Overall score among the compared methods, outperforming the strongest baseline by 19%.