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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...

Brain Imaging

Brain Imaging

Brain imaging technologies provide critical insights into both the structure and function of the human brain, enabling medical professionals and researchers to diagnose, study, and treat neurological disorders or psychiatric disorders more effectively.
These technologies include computerized axial tomography (CAT or CT scans), positron-emission tomography (PET scans), magnetic resonance imaging (MRI), functional magnetic resonance imaging (fMRI), and Transcranial Magnetic...

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Updated: Jun 5, 2025

Brain Infarct Segmentation and Registration on MRI or CT for Lesion-symptom Mapping

Brain Infarct Segmentation and Registration on MRI or CT for Lesion-symptom Mapping

Published on: September 25, 2019

A foundation model for generalized brain MRI analysis.

Divyanshu Tak^1,2, Biniam A Garomsa^1,2, Tafadzwa L Chaunzwa^1,2,3

¹Artificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School, Boston, MA, United States.

Medrxiv : the Preprint Server for Health Sciences

|December 16, 2024

Summary

This summary is machine-generated.

A new foundation model, Brain Imaging Adaptive Core (BrainIAC), enhances artificial intelligence (AI) for brain magnetic resonance imaging (MRI). BrainIAC improves disease diagnosis and biomarker discovery, even with limited data.

Keywords:

Artificial Intelligence Brain MRI Deep-Learning Foundation Model Self-supervised learning

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Area of Science:

Neuroimaging
Artificial Intelligence
Machine Learning

Background:

Artificial intelligence (AI) in brain magnetic resonance imaging (MRI) shows promise for disease diagnosis and management.
Current AI models face limitations due to restricted training data and poor generalization across diverse clinical scenarios and patient populations.
Foundation models offer a potential solution by utilizing self-supervised learning, pretraining, and adaptation.

Purpose of the Study:

To introduce Brain Imaging Adaptive Core (BrainIAC), a novel foundation model for brain MRI.
To enable generalized representation learning from unlabeled brain MRI data.
To serve as a foundational model for various downstream AI applications in neuroimaging.

Main Methods:

Developed Brain Imaging Adaptive Core (BrainIAC), a foundation model trained on unlabeled brain MRI data.
Employed self-supervised learning, pretraining, and targeted adaptation strategies.
Validated the model on 48,519 brain MRIs across a wide range of tasks.

Main Results:

BrainIAC demonstrated superior performance compared to localized supervised training and other pretrained models.
The model excelled particularly in low-data regimes and complex tasks where other methods faltered.
Achieved improved performance in scenarios previously considered infeasible due to data limitations.

Conclusions:

BrainIAC represents a significant advancement in AI for brain MRI, overcoming limitations of current task-specific models.
The foundation model facilitates improved biomarker discovery and accelerates AI clinical translation.
BrainIAC's adaptability allows integration into existing imaging pipelines and multimodal frameworks for broader clinical utility.