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Magnetic Resonance Imaging

Magnetic Resonance Imaging

Magnetic resonance imaging (MRI) is a noninvasive medical imaging technique based on a phenomenon of nuclear physics discovered in the 1930s, in which matter exposed to magnetic fields and radio waves was found to emit radio signals. In 1970, a physician and researcher named Raymond Damadian noticed that malignant (cancerous) tissue gave off different signals than normal body tissue. He applied for a patent for the first MRI scanning device in clinical use by the early 1980s. The early MRI...

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Related Experiment Video

Updated: Sep 14, 2025

Lesion Explorer: A Video-guided, Standardized Protocol for Accurate and Reliable MRI-derived Volumetrics in Alzheimer's Disease and Normal Elderly

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Published on: April 14, 2014

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

Gulfam Ahmed Sajua¹, Marjan Akhib², Yuchou Chang³

¹Department of Computer and Information Science, University of Massachusetts Dartmouth, North Dartmouth, Dartmouth, 02747, MA, USA.

Journal of Imaging Informatics in Medicine

|July 22, 2025

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.

Keywords:

AI agent Decision-making Large multimodal models MRI motion correction MRI reconstruction Reasoning Vision language models

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Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

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Published on: April 13, 2013

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.