Abstract
Artificial intelligence (AI)-driven autonomous agents are transforming multiple domains by integrating reasoning, decision-making, and task execution into a unified framework. In medical imaging, such agents have the potential to change workflows by reducing human intervention and optimizing image quality. In this paper, we introduce the AgentMRI. It is an AI-driven system that leverages vision language models (VLMs) for fully autonomous magnetic resonance imaging (MRI) reconstruction in the presence of multiple degradations. Unlike traditional MRI correction or reconstruction methods, AgentMRI does not rely on manual intervention for post-processing or does not rely on fixed correction models. Instead, it dynamically detects MRI corruption and then automatically selects the best correction model for image reconstruction. The framework uses a multi-query VLM strategy to ensure robust corruption detection through consensus-based decision-making and confidence-weighted inference. AgentMRI automatically chooses deep learning models that include MRI reconstruction, motion correction, and denoising models. We evaluated AgentMRI in both zero-shot and fine-tuned settings. Experimental results on a comprehensive brain MRI dataset demonstrate that AgentMRI achieves an average of 73.6% accuracy in zero-shot and 95.1% accuracy for fine-tuned settings. Experiments show that it accurately executes the reconstruction process without human intervention. AgentMRI eliminates manual intervention and introduces a scalable and multimodal AI framework for autonomous MRI processing. This work may build a significant step toward fully autonomous and intelligent MR image reconstruction systems.