A multi-agent system for spine MRI report generation from multi-sequence imaging

• URL: http://arxiv.org/abs/2606.08897v1
• Date: Mon, 08 Jun 2026 00:50:07 GMT
• Title: A multi-agent system for spine MRI report generation from multi-sequence imaging
• Abstract summary: Spinal pathology is a leading cause of pain and disability worldwide.<n>Despite recent advances in automated MRI analysis, effectively combining multi-sequence data remains an open challenge.<n>We present SpineAgent, a multi-agent framework for spine MRI report generation built upon a multi-sequence foundation model.
• Score: 13.179616037269952
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Spinal pathology is a leading cause of pain and disability worldwide. Spine MRI is central to clinical evaluation, yet its interpretation remains complex and time-consuming, requiring integration of information across multiple imaging sequences and anatomical regions. Despite recent advances in automated MRI analysis, effectively combining multi-sequence data while preserving sequence-specific diagnostic information remains an open challenge. Here we present SpineAgent, a multi-agent framework for spine MRI report generation built upon a multi-sequence foundation model trained on routine clinical data from 32,047 patients and 453,683 MRI series, comprising a total of 13,441,191 MRI slices. To accommodate diverse modalities of sequences, we first pre-train two DINOv3-based encoders separately on T1- and T2-weighted sequences. We then introduce a continual training strategy that learns a synthesizer to embed images of other sequences using the T1 and T2 encoders, producing patient-level embedding that integrates various signals across MRI sequences. Using these embeddings, SpineAgent achieves state-of-the-art performance, and demonstrates strong generalizability under cross-manufacturer and cross-cohort evaluation. Beyond classification, SpineAgent enables pathology localization by identifying findings-relevant slices and segmenting pathological regions. It also supports multimodal image-report retrieval, providing a solid foundation for scalable and explainable MRI report generation. We further integrate these validated capabilities of SpineAgent into 37 specialized agents. Finally, we incorporate their outputs as structured tokens within a Medical Report Agent trained end-to-end for report generation. Through both automated metrics and expert evaluation by five radiologists, SpineAgent achieves leading performance in spine MRI report generation.

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