Standardizing Chinese clinical imaging reports within the Observational Medical Outcomes Partnership (OMOP) framework is hindered by linguistic complexity and output inconsistency in existing methods. We propose DIMAS-OMOP, a Deliberative Intelligence-based Multi-Agent System designed for high-fidelity medical concept mapping toward OMOP standardization. Moving beyond single-model architectures, DIMAS-OMOP employs a hybrid three-stage workflow that integrates traditional natural language processing modules with selective Large Language Model reasoning and Retrieval-Augmented Generation. The core innovation lies in a hierarchical six-agent proposer-skeptic deliberation mechanism, complemented by a dynamic concept resolution approach and a four-dimensional quality control framework. Experimental results on 1,250 imaging reports demonstrate that DIMAS-OMOP achieves 95.2% mapping accuracy, significantly outperforming rule-based methods (+21.8 percentage points) and single-AI baselines (+8.1 percentage points). The system maintains a throughput of 1,200 reports/hour, with the multi-agent deliberation stage alone contributing an 8.9% relative accuracy gain. Furthermore, pilot deployment shows a 160.6% return on investment and a 31.5% increase in workflow efficiency. This study provides a novel, robust methodology for integrating unstructured non-English clinical data into the global Observational Health Data Sciences and Informatics (OHDSI) ecosystem through deliberative intelligence.