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Related Experiment Video

Updated: Jun 11, 2026

Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases

Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases

Published on: July 28, 2013

Anatomical global spatial normalization.

Jack L Lancaster¹, Matthew D Cykowski, David Reese McKay

¹Research Imaging Center, University of Texas Health Science Center at San Antonio, 8403 Floyd Curl Drive, San Antonio, TX 78229-3900, USA. jlancaster@uthscsa.edu

Neuroinformatics

|June 29, 2010

Summary

This summary is machine-generated.

Anatomical global spatial normalization (aGSN) scales brain images to reduce size variability while preserving mean structure sizes. Using whole brain hemispheres as a reference optimally reduces variance in brain structure volumes.

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Area of Science:

Neuroimaging
Computational anatomy
Medical image analysis

Background:

Brain imaging studies require controlling for anatomical variability.
Existing spatial normalization methods may alter mean structure sizes.
A novel method, anatomical global spatial normalization (aGSN), is proposed.

Purpose of the Study:

To introduce and evaluate aGSN for scaling high-resolution brain images.
To assess aGSN's ability to preserve mean brain structure sizes.
To investigate methods for reducing anatomical variance in neuroimaging.

Main Methods:

Investigated two mean-preserving scaling methods: shape preserving and shape standardizing.
Applied aGSN to 56 brain structures in the LPBA40 adult brain atlas.
Analyzed volume, distance, and area changes in various brain structures post-normalization.

Main Results:

aGSN successfully preserved mean sizes of brain structures while reducing variance.
Scale factors from individual brain structures were also mean-preserving.
Whole brain hemispheres as a reference structure yielded the greatest variance reduction (approx. 32%).

Conclusions:

aGSN effectively controls for brain size variability without altering mean structure sizes.
The choice of reference structure significantly impacts variance reduction.
An analytical method facilitates the integration of aGSN into existing neuroimaging software.