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

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Published on: September 25, 2019

Transform-invariant feature based functional MR image registration and neural activity modelling.

Jiaqi Gong¹, Qi Hao, Fei Hu

¹Department of Electrical and Computer Engineering, The University of Alabama, Tuscaloosa, AL 35487, USA. jgong5@crimson.ua.edu

International Journal of Computational Biology and Drug Design

|August 1, 2013

Summary

This summary is machine-generated.

This study introduces novel methods for analyzing brain activity using functional MRI (fMRI) data. These techniques improve the accuracy of brain image registration and reliably identify neural patterns associated with drinking behaviors.

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High-resolution Functional Magnetic Resonance Imaging Methods for Human Midbrain

High-resolution Functional Magnetic Resonance Imaging Methods for Human Midbrain

Published on: May 10, 2012

Area of Science:

Neuroimaging
Medical Image Analysis
Computational Neuroscience

Background:

Functional magnetic resonance imaging (fMRI) is crucial for understanding brain function.
Accurate registration of fMRI images is essential for analyzing neural activity across subjects and time.
Modeling neural activity requires robust feature representations that are invariant to transformations.

Purpose of the Study:

To develop advanced non-rigid image registration methods for fMRI data.
To propose novel methods for modeling neural activity using transform-invariant features.
To enhance the recognition of neural patterns related to specific behaviors like drinking.

Main Methods:

Utilizing transform-invariant features based on Gaussian Mixture Models (GMM) to improve Iterative Closest Point (ICP) based image registration.
Employing a 3-dimensional Scale-Invariant Feature Transform (SIFT) descriptor to represent neural activity.
Applying these methods to fMRI data from subjects engaged in drinking behaviors (water, glucose).

Main Results:

Improved performance in non-rigid image registration of fMRI data.
Successful recognition of neural activity patterns associated with drinking behaviors.
Demonstrated robustness of the proposed methods against various imaging artifacts.

Conclusions:

The proposed transform-invariant features enhance fMRI image registration accuracy.
3D SIFT descriptors effectively capture and represent neural activity related to drinking.
These methods offer a robust approach for analyzing complex neural data in neuroscience research.