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AgentMRI: A Vison Language Model-Powered AI System for Self-regulating MRI Reconstruction with Multiple Degradations

  • 0Department of Computer and Information Science, University of Massachusetts Dartmouth, North Dartmouth, Dartmouth, 02747, MA, USA.
Journal of Imaging Informatics in Medicine +

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July 22, 2025

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July 22, 2025

View abstract on PubMed

Summary

This summary is machine-generated.

AgentMRI, an AI system using vision language models, autonomously reconstructs magnetic resonance imaging (MRI) scans. It detects and corrects image degradations without human input, achieving high accuracy in tests.

Area Of Science

  • Medical Imaging
  • Artificial Intelligence
  • Machine Learning

Background

  • Artificial intelligence (AI)-driven autonomous agents are revolutionizing various fields by unifying reasoning, decision-making, and task execution.
  • In medical imaging, AI agents offer potential workflow improvements by minimizing human intervention and enhancing image quality.
  • Current magnetic resonance imaging (MRI) reconstruction methods often require manual post-processing or rely on static correction models.

Purpose Of The Study

  • To introduce AgentMRI, an AI-driven system utilizing vision language models (VLMs) for fully autonomous MRI reconstruction.
  • To develop a system capable of dynamically detecting MRI corruption and selecting appropriate correction models without manual intervention.
  • To establish a scalable and multimodal AI framework for autonomous MRI processing.

Main Methods

  • AgentMRI employs a multi-query VLM strategy for robust, consensus-based corruption detection and confidence-weighted inference.
  • The system automatically selects appropriate deep learning models for MRI reconstruction, motion correction, and denoising.
  • Evaluation was conducted using a comprehensive brain MRI dataset in both zero-shot and fine-tuned settings.

Main Results

  • AgentMRI achieved 73.6% accuracy in zero-shot settings and 95.1% accuracy in fine-tuned settings for MRI reconstruction.
  • Experimental results demonstrate the system's ability to execute the reconstruction process accurately without human intervention.
  • The framework successfully eliminated the need for manual intervention in MRI post-processing.

Conclusions

  • AgentMRI represents a significant advancement toward fully autonomous and intelligent MR image reconstruction systems.
  • The system offers a scalable and multimodal AI framework for autonomous MRI processing, reducing reliance on human input.
  • This AI-driven approach has the potential to transform medical imaging workflows by optimizing efficiency and image quality.

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