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Parameterization of the Age-Dependent Whole Brain Apparent Diffusion Coefficient Histogram.

Klose U, Batra M, Nägele T - Biomed Res Int (2015)

Bottom Line: The distribution of apparent diffusion coefficient (ADC) values in the brain can be used to characterize age effects and pathological changes of the brain tissue.This study confirms the strong dependence of the whole brain ADC histograms on the age of the examined subjects.The proposed model can be used to characterize changes of the whole brain ADC histogram in certain diseases under consideration of age effects.

View Article: PubMed Central - PubMed

Affiliation: Department of Diagnostic and Interventional Neuroradiology, University Hospital Tübingen, Tübingen, Germany.

ABSTRACT

Purpose: The distribution of apparent diffusion coefficient (ADC) values in the brain can be used to characterize age effects and pathological changes of the brain tissue. The aim of this study was the parameterization of the whole brain ADC histogram by an advanced model with influence of age considered.

Methods: Whole brain ADC histograms were calculated for all data and for seven age groups between 10 and 80 years. Modeling of the histograms was performed for two parts of the histogram separately: the brain tissue part was modeled by two Gaussian curves, while the remaining part was fitted by the sum of a Gaussian curve, a biexponential decay, and a straight line.

Results: A consistent fitting of the histograms of all age groups was possible with the proposed model.

Conclusions: This study confirms the strong dependence of the whole brain ADC histograms on the age of the examined subjects. The proposed model can be used to characterize changes of the whole brain ADC histogram in certain diseases under consideration of age effects.

No MeSH data available.


Related in: MedlinePlus

Example of acquired data from one patient: b0-images (a) and dw-images (b).
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fig1: Example of acquired data from one patient: b0-images (a) and dw-images (b).

Mentions: All fitting procedures were also performed for these subsets to have a possibility to estimate the stability of the fitting results. Typical images with the applied sequence are shown in Figure 1. All 30 slices of the b0-images (Figure 1(a)) and the dw-images (Figure 1(b)) are shown.


Parameterization of the Age-Dependent Whole Brain Apparent Diffusion Coefficient Histogram.

Klose U, Batra M, Nägele T - Biomed Res Int (2015)

Example of acquired data from one patient: b0-images (a) and dw-images (b).
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4644831&req=5

fig1: Example of acquired data from one patient: b0-images (a) and dw-images (b).
Mentions: All fitting procedures were also performed for these subsets to have a possibility to estimate the stability of the fitting results. Typical images with the applied sequence are shown in Figure 1. All 30 slices of the b0-images (Figure 1(a)) and the dw-images (Figure 1(b)) are shown.

Bottom Line: The distribution of apparent diffusion coefficient (ADC) values in the brain can be used to characterize age effects and pathological changes of the brain tissue.This study confirms the strong dependence of the whole brain ADC histograms on the age of the examined subjects.The proposed model can be used to characterize changes of the whole brain ADC histogram in certain diseases under consideration of age effects.

View Article: PubMed Central - PubMed

Affiliation: Department of Diagnostic and Interventional Neuroradiology, University Hospital Tübingen, Tübingen, Germany.

ABSTRACT

Purpose: The distribution of apparent diffusion coefficient (ADC) values in the brain can be used to characterize age effects and pathological changes of the brain tissue. The aim of this study was the parameterization of the whole brain ADC histogram by an advanced model with influence of age considered.

Methods: Whole brain ADC histograms were calculated for all data and for seven age groups between 10 and 80 years. Modeling of the histograms was performed for two parts of the histogram separately: the brain tissue part was modeled by two Gaussian curves, while the remaining part was fitted by the sum of a Gaussian curve, a biexponential decay, and a straight line.

Results: A consistent fitting of the histograms of all age groups was possible with the proposed model.

Conclusions: This study confirms the strong dependence of the whole brain ADC histograms on the age of the examined subjects. The proposed model can be used to characterize changes of the whole brain ADC histogram in certain diseases under consideration of age effects.

No MeSH data available.


Related in: MedlinePlus