Two‐compartment modeling of tissue microcirculation revisited

Abstract

Physiologically well-defined tissue parameters are structurally identifiable and accurately estimable from DCE data by the conceptually modified two-compartment model in combination with the bias correction. The accuracy of the bias-corrected flow is nearly comparable to that of the three other (theoretically unbiased) model parameters. As compared to conventional two-compartment modeling, this feature constitutes a major advantage for tracer kinetic analysis of both preclinical and clinical DCE imaging studies.

Dynamic Electron Paramagnetic Resonance Imaging: Modern Technique for Biodistribution and Pharmacokinetic Imaging

“…This kinetics may be analyzed using mathematical modeling to extract quantitative physiological parameters. The two-compartment model (TCM) is widely accepted, and it is frequently used to fit DCE-MRI data and obtain physiological parameters that are markers for disease. The TCM’s main disadvantage is the assumption that the tissue and tumor relation can be modeled as two well-mixed compartments .…”

Evaluation of pharmacokinetic models for perfusion imaging with dynamic contrast‐enhanced magnetic resonance imaging in porcine skeletal muscle using low‐molecular‐weight contrast agents

“…Thus the equilibrium of capillary CA concentration and CA concentration at the arterial output of the capillary bed in the derivation of the 2CXM is corrected by the factor , so that , where extraction coefficient , PS is the permeability surface product, F A is apparent perfusion, and F C is corrected perfusion. We uniquely determined F C from the numerical estimates computed for F A and PS with the (E)2CXM .…”

Spatially regularized estimation of the tissue homogeneity model parameters in DCE‐MRI using proximal minimization

“…As the calculation of the pharmacokinetic model [4] was the most time-demanding operation, the distribution of the number of model evaluations in each pixel and their total count were analyzed (Figure 4). Theoretically, the number of model evaluations in the regularized method is at least twice the number of iterations times the number of the concentration-time curves, since recalculation of the gradients after each denoising step is needed.…”

“…The estimation problems are often categorized as a priori structural identifiability and a posteriori identifiability. The a priori identifiability is influenced by the nonlinear model structure itself [2][3][4] and by the experimental design -sampling and duration of the experiment. [5][6][7][8][9][10] The a posteriori identifiability includes the errors in the measurement -the signal-to-noise ratio, arterial input function errors, and the inaccuracy of conversion from the T1-weighted image sequence to the concentration-time curves.…”