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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

Fitted parameters of the first part of the histogram (based on model 2) for the different age groups: amplitudes of both Gaussian curves (a) and peak positions of both Gaussian curves (b) and of the width (FWHM) of the fitted curve (c). In all cases, the parameters are shown for all selected patients (bold line) and for the results of the three subsets of patients.
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fig5: Fitted parameters of the first part of the histogram (based on model 2) for the different age groups: amplitudes of both Gaussian curves (a) and peak positions of both Gaussian curves (b) and of the width (FWHM) of the fitted curve (c). In all cases, the parameters are shown for all selected patients (bold line) and for the results of the three subsets of patients.

Mentions: The results of the fitting of the first part of the histogram using model 2 and the deviations are shown in Supplementary Table 2. The histograms and the fitted curves based on model 2 are shown in Figure 4(a). The difference curves between the histograms of the seven groups and the fitted curves in Figure 4(a) have all a much smaller maximum than those in Figure 3(b) and they do not show a systematic shape as the difference curves in Figure 3(b). The two Gaussian curves that were fitted to the histograms are shown in Figure 4(b). In all seven age classes, a larger Gaussian curve with a higher mean ADC value and a smaller one with a lower mean ADC value were obtained. The obtained values for the fit parameters are shown in Figure 5. In addition to the results from all patients (shown as bold lines), the results from the seven age classes from the three subsets of patients are shown.


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

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

Fitted parameters of the first part of the histogram (based on model 2) for the different age groups: amplitudes of both Gaussian curves (a) and peak positions of both Gaussian curves (b) and of the width (FWHM) of the fitted curve (c). In all cases, the parameters are shown for all selected patients (bold line) and for the results of the three subsets of patients.
© Copyright Policy
Related In: Results  -  Collection

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

fig5: Fitted parameters of the first part of the histogram (based on model 2) for the different age groups: amplitudes of both Gaussian curves (a) and peak positions of both Gaussian curves (b) and of the width (FWHM) of the fitted curve (c). In all cases, the parameters are shown for all selected patients (bold line) and for the results of the three subsets of patients.
Mentions: The results of the fitting of the first part of the histogram using model 2 and the deviations are shown in Supplementary Table 2. The histograms and the fitted curves based on model 2 are shown in Figure 4(a). The difference curves between the histograms of the seven groups and the fitted curves in Figure 4(a) have all a much smaller maximum than those in Figure 3(b) and they do not show a systematic shape as the difference curves in Figure 3(b). The two Gaussian curves that were fitted to the histograms are shown in Figure 4(b). In all seven age classes, a larger Gaussian curve with a higher mean ADC value and a smaller one with a lower mean ADC value were obtained. The obtained values for the fit parameters are shown in Figure 5. In addition to the results from all patients (shown as bold lines), the results from the seven age classes from the three subsets of patients 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