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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: Aug 24, 2025

Three-Dimensional Shape Modeling and Analysis of Brain Structures

Three-Dimensional Shape Modeling and Analysis of Brain Structures

Published on: November 14, 2019

An artificial-intelligence-based age-specific template construction framework for brain structural analysis using

Dongdong Gu¹, Feng Shi¹, Rui Hua¹

¹Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China.

Human Brain Mapping

|October 21, 2022

Summary

This summary is machine-generated.

This study introduces an AI-based framework (ASTC) to create age-specific brain templates from MRI scans. The framework successfully mapped age-related brain changes, aiding longitudinal neuroimaging studies.

Keywords:

artificial intelligence brain template longitudinal template magnetic resonance imaging (MRI)registration statistical analysis

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Area of Science:

Neuroimaging
Cognitive Science
Artificial Intelligence

Background:

Brain template construction is crucial for neurological and cognitive science research.
Standard templates aid in analyzing group structural and functional brain characteristics.
AI offers potential for quantifying age-specific brain variations.

Purpose of the Study:

To present an AI-based age-specific template construction (ASTC) framework.
To analyze longitudinal structural brain variations across different age groups.
To leverage T1-weighted MRIs for age-specific template generation.

Main Methods:

Developed and applied the ASTC framework using T1-weighted MRIs from 646 subjects (18-82 years old).
Constructed 13 longitudinal templates at 5-year intervals.
Performed tissue segmentation and substructure parcellation for analysis.

Main Results:

The ASTC framework successfully generated age-specific brain templates.
Consistent age-related changes in brain structures were identified.
The study demonstrated ASTC's utility in longitudinal neuroimaging.

Conclusions:

ASTC is a capable framework for longitudinal neuroimaging studies.
The framework facilitates the analysis of age-specific brain variations.
AI-driven template construction enhances understanding of brain aging.