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Washout allometric reference method (WARM) for parametric analysis of [(11)C]PIB in human brains.

Rodell A, Aanerud J, Braendgaard H, Gjedde A - Front Aging Neurosci (2013)

Bottom Line: Thus, when flow differences confound conventional measures of [(11)C]PIB binding, the separate estimates of CBF and BP ND provide additional information about possible AD.The results demonstrate the importance of data-driven estimation of CBF and BP ND, as well as reference region detection from the [(11)C]PIB signal.We conclude that the WARM method yields stable measures of BP ND with relative ease, using only integration for noise reduction and no model regression.

View Article: PubMed Central - PubMed

Affiliation: Department of Nuclear Medicine and PET Centre, Aarhus University Hospital Aarhus, Denmark.

ABSTRACT
Rapid clearance and disappearance of a tracer from the circulation challenges the determination of the tracer's binding potentials in brain (BP ND) by positron emission tomography (PET). This is the case for the analysis of the binding of radiolabeled [(11)C]Pittsburgh Compound B ([(11)C]PIB) to amyloid-β (Aβ) plaques in brain of patients with Alzheimer's disease (AD). To resolve the issue of rapid clearance from the circulation, we here introduce the flow-independent Washout Allometric Reference Method (WARM) for the analysis of washout and binding of [(11)C]PIB in two groups of human subjects, healthy aged control subjects (HC), and patients suffering from AD, and we compare the results to the outcome of two conventional analysis methods. We also use the rapid initial clearance to obtain a surrogate measure of the rate of cerebral blood flow (CBF), as well as a method of identifying a suitable reference region directly from the [(11)C]PIB signal. The difference of average absolute CBF values between the AD and HC groups was highly significant (P < 0.003). The CBF measures were not significantly different between the groups when normalized to cerebellar gray matter flow. Thus, when flow differences confound conventional measures of [(11)C]PIB binding, the separate estimates of CBF and BP ND provide additional information about possible AD. The results demonstrate the importance of data-driven estimation of CBF and BP ND, as well as reference region detection from the [(11)C]PIB signal. We conclude that the WARM method yields stable measures of BP ND with relative ease, using only integration for noise reduction and no model regression. The method accounts for relative flow differences in the brain tissue and yields a calibrated measure of absolute CBF directly from the [(11)C]PIB signal. Compared to conventional methods, WARM optimizes the Aβ plaque load discrimination between patients with AD and healthy controls (P = 0.009).

No MeSH data available.


Related in: MedlinePlus

Behavior of the parts of the warm method. The first leftmost panel shows two initial simulated mono-exponential washout curves, a reference curve (kND2 = 0.92) in red, and a binding curve (k2a = 0.98) in blue. The black curve shows the flow normalized version of the reference curve normalized to the ROI curve, with 20% Gaussian noise added. The second panel shows the denominators and nominators from Equation (40) as function of time, i.e., the fraction between these two ideally linear factors form the binding potential. The third panel shows the similar integrated nominator and denominator from Equation (43). The integration stabilizes the result. The fourth panel shows the PBND from Equation (40) in blue, and from Equation (43) in black. The dotted line is the theoretical true value for PBND. The PBND from both Equations (40) and (43) converge toward the true value with increasing tomographic duration.
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Figure 1: Behavior of the parts of the warm method. The first leftmost panel shows two initial simulated mono-exponential washout curves, a reference curve (kND2 = 0.92) in red, and a binding curve (k2a = 0.98) in blue. The black curve shows the flow normalized version of the reference curve normalized to the ROI curve, with 20% Gaussian noise added. The second panel shows the denominators and nominators from Equation (40) as function of time, i.e., the fraction between these two ideally linear factors form the binding potential. The third panel shows the similar integrated nominator and denominator from Equation (43). The integration stabilizes the result. The fourth panel shows the PBND from Equation (40) in blue, and from Equation (43) in black. The dotted line is the theoretical true value for PBND. The PBND from both Equations (40) and (43) converge toward the true value with increasing tomographic duration.

Mentions: Figure 1 illustrates the dynamics of the nominator, the denominator, and the BPND(T) terms of Equations (43) and (40) (in the Theory section) for simulated ROI and reference curves with 20% Gaussian noise added. As seen, the nominator and the denominator from Equation (40) (in panel 2 from the left) are stabilized by the integration in Equation (43) (panel 3 from the left). Panel 4 illustrates the convergence of the BPND(T) estimates toward the theoretical result.


Washout allometric reference method (WARM) for parametric analysis of [(11)C]PIB in human brains.

Rodell A, Aanerud J, Braendgaard H, Gjedde A - Front Aging Neurosci (2013)

Behavior of the parts of the warm method. The first leftmost panel shows two initial simulated mono-exponential washout curves, a reference curve (kND2 = 0.92) in red, and a binding curve (k2a = 0.98) in blue. The black curve shows the flow normalized version of the reference curve normalized to the ROI curve, with 20% Gaussian noise added. The second panel shows the denominators and nominators from Equation (40) as function of time, i.e., the fraction between these two ideally linear factors form the binding potential. The third panel shows the similar integrated nominator and denominator from Equation (43). The integration stabilizes the result. The fourth panel shows the PBND from Equation (40) in blue, and from Equation (43) in black. The dotted line is the theoretical true value for PBND. The PBND from both Equations (40) and (43) converge toward the true value with increasing tomographic duration.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3842163&req=5

Figure 1: Behavior of the parts of the warm method. The first leftmost panel shows two initial simulated mono-exponential washout curves, a reference curve (kND2 = 0.92) in red, and a binding curve (k2a = 0.98) in blue. The black curve shows the flow normalized version of the reference curve normalized to the ROI curve, with 20% Gaussian noise added. The second panel shows the denominators and nominators from Equation (40) as function of time, i.e., the fraction between these two ideally linear factors form the binding potential. The third panel shows the similar integrated nominator and denominator from Equation (43). The integration stabilizes the result. The fourth panel shows the PBND from Equation (40) in blue, and from Equation (43) in black. The dotted line is the theoretical true value for PBND. The PBND from both Equations (40) and (43) converge toward the true value with increasing tomographic duration.
Mentions: Figure 1 illustrates the dynamics of the nominator, the denominator, and the BPND(T) terms of Equations (43) and (40) (in the Theory section) for simulated ROI and reference curves with 20% Gaussian noise added. As seen, the nominator and the denominator from Equation (40) (in panel 2 from the left) are stabilized by the integration in Equation (43) (panel 3 from the left). Panel 4 illustrates the convergence of the BPND(T) estimates toward the theoretical result.

Bottom Line: Thus, when flow differences confound conventional measures of [(11)C]PIB binding, the separate estimates of CBF and BP ND provide additional information about possible AD.The results demonstrate the importance of data-driven estimation of CBF and BP ND, as well as reference region detection from the [(11)C]PIB signal.We conclude that the WARM method yields stable measures of BP ND with relative ease, using only integration for noise reduction and no model regression.

View Article: PubMed Central - PubMed

Affiliation: Department of Nuclear Medicine and PET Centre, Aarhus University Hospital Aarhus, Denmark.

ABSTRACT
Rapid clearance and disappearance of a tracer from the circulation challenges the determination of the tracer's binding potentials in brain (BP ND) by positron emission tomography (PET). This is the case for the analysis of the binding of radiolabeled [(11)C]Pittsburgh Compound B ([(11)C]PIB) to amyloid-β (Aβ) plaques in brain of patients with Alzheimer's disease (AD). To resolve the issue of rapid clearance from the circulation, we here introduce the flow-independent Washout Allometric Reference Method (WARM) for the analysis of washout and binding of [(11)C]PIB in two groups of human subjects, healthy aged control subjects (HC), and patients suffering from AD, and we compare the results to the outcome of two conventional analysis methods. We also use the rapid initial clearance to obtain a surrogate measure of the rate of cerebral blood flow (CBF), as well as a method of identifying a suitable reference region directly from the [(11)C]PIB signal. The difference of average absolute CBF values between the AD and HC groups was highly significant (P < 0.003). The CBF measures were not significantly different between the groups when normalized to cerebellar gray matter flow. Thus, when flow differences confound conventional measures of [(11)C]PIB binding, the separate estimates of CBF and BP ND provide additional information about possible AD. The results demonstrate the importance of data-driven estimation of CBF and BP ND, as well as reference region detection from the [(11)C]PIB signal. We conclude that the WARM method yields stable measures of BP ND with relative ease, using only integration for noise reduction and no model regression. The method accounts for relative flow differences in the brain tissue and yields a calibrated measure of absolute CBF directly from the [(11)C]PIB signal. Compared to conventional methods, WARM optimizes the Aβ plaque load discrimination between patients with AD and healthy controls (P = 0.009).

No MeSH data available.


Related in: MedlinePlus