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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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Related Experiment Video

Updated: Jun 3, 2025

Author Spotlight: Integrated Photoacoustic, Ultrasound, and Angiographic Tomography (PAUSAT) for NonInvasive Whole-Brain Imaging of Ischemic Stroke

Author Spotlight: Integrated Photoacoustic, Ultrasound, and Angiographic Tomography (PAUSAT) for NonInvasive Whole-Brain Imaging of Ischemic Stroke

Published on: June 2, 2023

Artificial intelligence and stroke imaging.

Jane Rondina¹, Parashkev Nachev

¹High Dimensional Neurology Group, UCL Queen Square Institute of Neurology, University College London, Russell Square House, Bloomsbury, London, UK.

Current Opinion in Neurology

|January 6, 2025

Summary

This summary is machine-generated.

Artificial intelligence (AI) can improve stroke diagnosis by analyzing complex imaging data. Deep generative models show promise for overcoming current challenges in real-world clinical applications.

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Last Updated: Jun 3, 2025

Author Spotlight: Integrated Photoacoustic, Ultrasound, and Angiographic Tomography (PAUSAT) for NonInvasive Whole-Brain Imaging of Ischemic Stroke

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Non-invasive Imaging and Analysis of Cerebral Ischemia in Living Rats Using Positron Emission Tomography with 18F-FDG

Published on: December 28, 2014

Area of Science:

Neuroscience
Medical Imaging
Artificial Intelligence

Background:

Stroke diagnosis is complex due to intricate neural substrates and neurovascular interactions.
High-resolution imaging is crucial for characterizing brain structure, microstructure, and function.
Personalized stroke care requires sophisticated models to interpret complex imaging data.

Purpose of the Study:

To explore the role of artificial intelligence (AI) in enhancing stroke diagnosis and personalized care.
To address the complexity of stroke manifestations through advanced imaging and AI models.

Main Methods:

Utilizing advances in machine vision and deep learning for higher fidelity predictive and descriptive tools.
Integrating rich imaging information (parenchymal, microstructural, functional, vascular) into AI models.

Main Results:

Deep learning models offer improved analysis of complex imaging data for stroke.
Clinical application is currently limited by data noise, incompleteness, bias, and scale.

Conclusions:

AI holds significant potential for revolutionizing stroke imaging and care.
Deep generative models present a promising avenue to overcome current limitations and drive innovation in AI for stroke.