Multivariate Image Analysis of Magnetic Resonance Images with the Direct Exponential Curve Resolution Algorithm (DECRA): Part 2: Application to Human Brain Images

Owing to the heterogeneity of living tissues, it is challenging to quantify tissue properties using magnetic resonance imaging. Within a single voxel, contributions to the signal may result from several types of1H nuclei with varied chemical (e.g., −CH2−, −OH) and physical environments (e.g., tissue...

Full description

Saved in:
Bibliographic Details
Published in:Journal of magnetic resonance (1997) 1998-06, Vol.132 (2), p.307-315
Main Authors: Antalek, B., Hornak, J.P., Windig, W.
Format: Article
Language:English
Subjects:
Adult
Algorithms
Brain - anatomy & histology
Humans
Image Processing, Computer-Assisted
image segmentation
Magnetic Resonance Imaging
Male
Mathematics
MRI
multispectral tissue classification
spin relaxation rates
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Owing to the heterogeneity of living tissues, it is challenging to quantify tissue properties using magnetic resonance imaging. Within a single voxel, contributions to the signal may result from several types of1H nuclei with varied chemical (e.g., −CH2−, −OH) and physical environments (e.g., tissue density, compartmentalization). Therefore, mixtures of1H environments are prevalent. Furthermore, each unique type of1H environment may possess a unique and characteristic spin–lattice relaxation time (T1) and spin–spin relaxation time (T2). A method for resolving these unique exponentials is introduced in a separate paper (Part 1. Algorithm and Model System) and uses the direct exponential curve resolution algorithm (DECRA). We present results from an analysis of images of the human head comprising brain tissues.
ISSN:1090-7807
1096-0856
DOI:10.1006/jmre.1998.1391