Pharmacokinetic modeling of delayed gadolinium enhancement in the myocardium

Knowles, Benjamin R.; Batchelor, Paul; Parish, Victoria; Ginks, Matthew; Plein, Sven; Razavi, Reza; Schaeffter, Tobias

doi:10.1002/mrm.21767

Cited by 28 publications

(31 citation statements)

References 16 publications

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“…However, the reference region approach only applies to highly simplified models without the consideration of a plasma volume fraction [52]. Alternatively, some more recent models consider a directly measured AIF [16][17][18][19]48]. The determination of the AIF, however, demands for a sequence with high temporal and spatial resolution to include not only the tissue of interest but also an afferent artery.…”

Section: Discussionmentioning

confidence: 97%

“…Both ignore intravascular contrast agent in the tissue. Additionally, in contrast to novel models using a measured AIF [16][17][18][19], both induce an uncertainty in DCE-MRI quantification, Approach 1 by using a pre-defined AIF and Approach 2 by modelling the AIF. However, determination of an AIF can be a big challenge, depending on the location of the tissue, and especially in the case of small laboratory animals.…”

Section: Introductionmentioning

confidence: 94%

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Simulation-based comparison of two approaches frequently used for dynamic contrast-enhanced MRI

et al. 2009

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The purpose was to compare two approaches for the acquisition and analysis of dynamic-contrast-enhanced MRI data with respect to differences in the modelling of the arterial input-function (AIF), the dependency of the model parameters on physiological parameters and their numerical stability. Eight hundred tissue concentration curves were simulated for different combinations of perfusion, permeability, interstitial volume and plasma volume based on two measured AIFs and analysed according to the two commonly used approaches. The transfer constants (Approach 1) K (trans) and (Approach 2) k (ep) were correlated with all tissue parameters. K (trans) showed a stronger dependency on perfusion, and k (ep) on permeability. The volume parameters (Approach 1) v (e) and (Approach 2) A were mainly influenced by the interstitial and plasma volume. Both approaches allow only rough characterisation of tissue microcirculation and microvasculature. Approach 2 seems to be somewhat more robust than 1, mainly due to the different methods of CA administration.

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Section: Discussionmentioning

confidence: 97%

Section: Introductionmentioning

confidence: 94%

Simulation-based comparison of two approaches frequently used for dynamic contrast-enhanced MRI

et al. 2009

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“…Detection and quantifi cation of fibrosis with the use of quantitative DCE MR imaging has been previously reported in nononcologic settings ( 22 ). Correlation between DCE MR imaging and the degree of fi brosis depicted at histopathologic examination has been reported for semiquantitative DCE MR imaging measurements in pancreatic and myocardial studies ( 23,24 ).…”

Section: Patientsmentioning

confidence: 95%

Tumoral and Nontumoral Pancreas: Correlation between Quantitative Dynamic Contrast-enhanced MR Imaging and Histopathologic Parameters

Bali¹,

Metens²,

Denolin³

et al. 2011

Radiology

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“…C p (t), C e (t), and C trap (t) represent the gadolinium (Gd) concentration change over time in each compartment, and K 1 , K 2 , K 3 , K 4 represent the transfer rate of Gd between compartments (reprinted with permission from Fig. 1 of Knowles et al [56]). …”

Section: Figure 3 Diagram Of a 3-compartment Modelmentioning

confidence: 99%

Quantification of Absolute Myocardial Blood Flow by Magnetic Resonance Perfusion Imaging

Lee

Johnson

2009

JACC: Cardiovascular Imaging

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By serially imaging the myocardium during the initial transit of gadolinium contrast, magnetic resonance perfusion imaging can accurately assess relative reductions in regional myocardial blood flow and identify hemodynamically significant coronary artery disease. Models can be used to quantify myocardial blood flow (in milliliters/minute/gram) on the basis of dynamic signal changes within the myocardium and left ventricular cavity. Although the mathematical modeling involved in this type of analysis adds complexity, the benefits of absolute blood flow quantification might improve clinical diagnosis and have important implications for cardiovascular research.

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Pharmacokinetic modeling of delayed gadolinium enhancement in the myocardium

Cited by 28 publications

References 16 publications

Simulation-based comparison of two approaches frequently used for dynamic contrast-enhanced MRI

Simulation-based comparison of two approaches frequently used for dynamic contrast-enhanced MRI

Tumoral and Nontumoral Pancreas: Correlation between Quantitative Dynamic Contrast-enhanced MR Imaging and Histopathologic Parameters

Quantification of Absolute Myocardial Blood Flow by Magnetic Resonance Perfusion Imaging

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