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Methods Inf Med 2006; 45(06): 643-650
DOI: 10.1055/s-0038-1634129
Original Article
Schattauer GmbH

Time-variant Analysis of Fast-fMRI and Dynamic Contrast Agent MRI Sequences as Examples of 4-dimensional Image Analysis

L. Leistritz
1   Institute of Medical Statistics, Computer Sciences and Documentation, Friedrich Schiller University, Jena, Germany
,
W. Hesse
1   Institute of Medical Statistics, Computer Sciences and Documentation, Friedrich Schiller University, Jena, Germany
,
T. Wüstenberg
2   Department of Medical Psychology, Georg-August University, Göttingen, Germany
,
C. Fitzek
3   Institute of Diagnostic and Interventional Radiology, Friedrich Schiller University, Jena, Germany
,
J. R. Reichenbach
3   Institute of Diagnostic and Interventional Radiology, Friedrich Schiller University, Jena, Germany
,
H. Witte
1   Institute of Medical Statistics, Computer Sciences and Documentation, Friedrich Schiller University, Jena, Germany
› Author Affiliations
Further Information

Publication History

Received 03 August 2005

accepted 27 March 2006

Publication Date:
08 February 2018 (online)

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Summary

Objectives: Image sequences with time-varying information content need appropriate analysis strategies. The exploration of directed information transfer (interactions) between neuronal assemblies is one of the most important aims of current functional MRI (fMRI) analysis. Additionally, we examined perfusion maps in dynamic contrast agent MRI sequences of stroke patients. In this investigation, the focus centers on distinguishing between brain areas with normal and reduced perfusion on the basis of the dynamics of contrast agent inflow and washout.

Methods: Fast fMRI sequences were analyzed with time-variant Granger causality (tvGC). The tvGC is based on a time-variant autoregressive model and is used for the quantification of the directed information transfer between activated brain areas. Generalized Dynamic Neural Networks (GDNN) with time-variant weights were applied on dynamic contrast agent MRI sequences as a nonlinear operator in order to enhance differences in the signal courses of pixels of normal and injured tissues.

Results: A simple motor task (self-paced finger tapping) is used in an fMRI design to investigate directed interactions between defined brain areas. A significant information transfer can be determined for the direction primary motor cortex to supplementary motor area during a short time period of about five seconds after stimulus. The analysis of dynamic contrast agent MRI sequences demonstrates that the trained GDNN enables a reliable tissue classification. Three classes are of interest: normal tissue, tissue at risk for death, and dead tissue.

Conclusions: The time-variant multivariate analysis of directed information transfer derived from fMRI sequences and the computation of perfusion maps by GDNN demonstrate that dynamic analysis methods are essential tools for 4D image analysis.

Keywords

Time-variant analysis methods - directed information transfer - Granger causality - Dynamic Neural Networks - functional MRI - contrast agent MRI
 

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