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Using dynamic contrast-enhanced magnetic resonance imaging data to constrain a positron emission tomography kinetic model: theory and simulations.

Fluckiger JU, Li X, Whisenant JG, Peterson TE, Gore JC, Yankeelov TE - Int J Biomed Imaging (2013)

Bottom Line: We show how dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) data can constrain a compartmental model for analyzing dynamic positron emission tomography (PET) data.We first develop the theory that enables the use of DCE-MRI data to separate whole tissue time activity curves (TACs) available from dynamic PET data into individual TACs associated with the blood space, the extravascular-extracellular space (EES), and the extravascular-intracellular space (EIS).The parameters returned by this approach may provide new information on the transport of a tracer in a variety of dynamic PET studies.

View Article: PubMed Central - PubMed

Affiliation: Department of Radiology, Northwestern University, Chicago, IL 60611, USA.

ABSTRACT
We show how dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) data can constrain a compartmental model for analyzing dynamic positron emission tomography (PET) data. We first develop the theory that enables the use of DCE-MRI data to separate whole tissue time activity curves (TACs) available from dynamic PET data into individual TACs associated with the blood space, the extravascular-extracellular space (EES), and the extravascular-intracellular space (EIS). Then we simulate whole tissue TACs over a range of physiologically relevant kinetic parameter values and show that using appropriate DCE-MRI data can separate the PET TAC into the three components with accuracy that is noise dependent. The simulations show that accurate blood, EES, and EIS TACs can be obtained as evidenced by concordance correlation coefficients >0.9 between the true and estimated TACs. Additionally, provided that the estimated DCE-MRI parameters are within 10% of their true values, the errors in the PET kinetic parameters are within approximately 20% of their true values. The parameters returned by this approach may provide new information on the transport of a tracer in a variety of dynamic PET studies.

No MeSH data available.


Related in: MedlinePlus

An example of simulated tissue curves and the fits provided by (11). These panels represent 25 voxels with (a) no error in the DCE-MRI parameters, (b) 5% error, (c) 10% error, and (d) 15% error. The AIF used in these simulations was measured from the left ventricle of a mouse, which results in some noise even with no error in the DCE-MRI parameters (see panel (a)).
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fig2: An example of simulated tissue curves and the fits provided by (11). These panels represent 25 voxels with (a) no error in the DCE-MRI parameters, (b) 5% error, (c) 10% error, and (d) 15% error. The AIF used in these simulations was measured from the left ventricle of a mouse, which results in some noise even with no error in the DCE-MRI parameters (see panel (a)).

Mentions: Figure 2 shows the ability of the algorithm to correctly separate the CEES, CEIS, and Cb time courses from one simulated, whole tissue dataset. The parameter values were K1 = 0.3 (mL/min/g), k2 = 0.5 (min−1), k3 = 0.15 (min−1), and k4 = 0.1 (min−1). The solid lines indicate the extracted curves, while the individual points correspond to the true (simulated) data; the filled circles in each panel depict the measured Cb time course used to drive all the simulations. The four panels correspond to time courses extracted when the DCE-MRI parameters have errors of 0%, 5%, 10%, and 15%. In all four panels, the error in CEIS is less than 5%. CEES is extracted with an error of less than 5% when the DCE-MRI parameters have errors of 10% or less (panels (a)–(c)). The maximum error in CEES when the DCE-MRI parameters have errors of 15% is approximately 10%. For the Cb component, the maximum error in the extracted time course increases with the errors in the DCE-MRI parameters.


Using dynamic contrast-enhanced magnetic resonance imaging data to constrain a positron emission tomography kinetic model: theory and simulations.

Fluckiger JU, Li X, Whisenant JG, Peterson TE, Gore JC, Yankeelov TE - Int J Biomed Imaging (2013)

An example of simulated tissue curves and the fits provided by (11). These panels represent 25 voxels with (a) no error in the DCE-MRI parameters, (b) 5% error, (c) 10% error, and (d) 15% error. The AIF used in these simulations was measured from the left ventricle of a mouse, which results in some noise even with no error in the DCE-MRI parameters (see panel (a)).
© Copyright Policy - open-access
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC3814089&req=5

fig2: An example of simulated tissue curves and the fits provided by (11). These panels represent 25 voxels with (a) no error in the DCE-MRI parameters, (b) 5% error, (c) 10% error, and (d) 15% error. The AIF used in these simulations was measured from the left ventricle of a mouse, which results in some noise even with no error in the DCE-MRI parameters (see panel (a)).
Mentions: Figure 2 shows the ability of the algorithm to correctly separate the CEES, CEIS, and Cb time courses from one simulated, whole tissue dataset. The parameter values were K1 = 0.3 (mL/min/g), k2 = 0.5 (min−1), k3 = 0.15 (min−1), and k4 = 0.1 (min−1). The solid lines indicate the extracted curves, while the individual points correspond to the true (simulated) data; the filled circles in each panel depict the measured Cb time course used to drive all the simulations. The four panels correspond to time courses extracted when the DCE-MRI parameters have errors of 0%, 5%, 10%, and 15%. In all four panels, the error in CEIS is less than 5%. CEES is extracted with an error of less than 5% when the DCE-MRI parameters have errors of 10% or less (panels (a)–(c)). The maximum error in CEES when the DCE-MRI parameters have errors of 15% is approximately 10%. For the Cb component, the maximum error in the extracted time course increases with the errors in the DCE-MRI parameters.

Bottom Line: We show how dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) data can constrain a compartmental model for analyzing dynamic positron emission tomography (PET) data.We first develop the theory that enables the use of DCE-MRI data to separate whole tissue time activity curves (TACs) available from dynamic PET data into individual TACs associated with the blood space, the extravascular-extracellular space (EES), and the extravascular-intracellular space (EIS).The parameters returned by this approach may provide new information on the transport of a tracer in a variety of dynamic PET studies.

View Article: PubMed Central - PubMed

Affiliation: Department of Radiology, Northwestern University, Chicago, IL 60611, USA.

ABSTRACT
We show how dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) data can constrain a compartmental model for analyzing dynamic positron emission tomography (PET) data. We first develop the theory that enables the use of DCE-MRI data to separate whole tissue time activity curves (TACs) available from dynamic PET data into individual TACs associated with the blood space, the extravascular-extracellular space (EES), and the extravascular-intracellular space (EIS). Then we simulate whole tissue TACs over a range of physiologically relevant kinetic parameter values and show that using appropriate DCE-MRI data can separate the PET TAC into the three components with accuracy that is noise dependent. The simulations show that accurate blood, EES, and EIS TACs can be obtained as evidenced by concordance correlation coefficients >0.9 between the true and estimated TACs. Additionally, provided that the estimated DCE-MRI parameters are within 10% of their true values, the errors in the PET kinetic parameters are within approximately 20% of their true values. The parameters returned by this approach may provide new information on the transport of a tracer in a variety of dynamic PET studies.

No MeSH data available.


Related in: MedlinePlus