Abstract
Magnetic Resonance Imaging (MRI) reconstruction and motion correction are fundamental inverse problems in medical imaging, traditionally addressed using deep learning models trained for specific corruption types. However, these models often lack generalizability, requiring manual intervention to determine the appropriate correction strategy. Furthermore, single-agent automated AI systems are inherently prone to bias and overfitting, as they rely on a single inference pathway, which may fail in cases of ambiguous or mixed corruption types. To address these limitations, we introduce MRI-AgentNet, a Vision-Language Model (VLM)-powered multi-agent AI system that autonomously detects MRI corruption types and selects the optimal correction model. MRI-AgentNet employs a hierarchical multi-agent framework where multiple AI agents independently assess corruption types. The system refines corruption predictions through structured cross-validation, which ensures consensus before selecting the optimal correction model. This multi-agent system reduces bias by leveraging diverse inference pathways and structured hierarchical validation. To ensure optimal correction, a MetaModel learns a structured mapping between agentinferred corruption labels and the appropriate correction model. Experimental results demonstrate that MRI-AgentNet achieves expert-level accuracy in both corruption detection and correction, which reduces the need for manual intervention by radiologists. By automating the entire MRI corruption analysis and restoration pipeline, MRI-AgentNet enables a fully autonomous, AI-driven correction process. This can enhance the efficiency of MRI reconstruction while reducing operational costs. This work introduces a scalable, multi-agent AI framework that integrates vision-language reasoning, inverse problem-solving, and MRI artifact restoration. It establishes a foundation for fully automated AI-driven medical imaging. Code availability: https://github.com/gulfam7/MRI-AgentNet