Tracer kinetic modelling in MRI: estimating perfusion and capillary permeability

Abstract

The tracer-kinetic models developed in the early 1990s for dynamic contrast-enhanced MRI (DCE-MRI) have since become a standard in numerous applications. At the same time, the development of MRI hardware has led to increases in image quality and temporal resolution that reveal the limitations of the early models. This in turn has stimulated an interest in the development and application of a second generation of modelling approaches. They are designed to overcome these limitations and produce additional and more accurate information on tissue status. In particular, models of the second generation enable separate estimates of perfusion and capillary permeability rather than a single parameter K(trans) that represents a combination of the two. A variety of such models has been proposed in the literature, and development in the field has been constrained by a lack of transparency regarding terminology, notations and physiological assumptions. In this review, we provide an overview of these models in a manner that is both physically intuitive and mathematically rigourous. All are derived from common first principles, using concepts and notations from general tracer-kinetic theory. Explicit links to their historical origins are included to allow for a transfer of experience obtained in other fields (PET, SPECT, CT). A classification is presented that reveals the links between all models, and with the models of the first generation. Detailed formulae for all solutions are provided to facilitate implementation. Our aim is to encourage the application of these tools to DCE-MRI by offering researchers a clearer understanding of their assumptions and requirements.

UMMPerfusion: an Open Source Software Tool Towards Quantitative MRI Perfusion Analysis in Clinical Routine

“…This is desirable, i.e., when studying brain barrier leakage [51], tumors [52], or kidney filtration [4,11,18]. Such tasks can be performed when applying compartment models [6,19]. Future research will be directed towards implementing a one-and two-compartment model as reported in the literature [5,6,8].…”

“…Such tasks can be performed when applying compartment models [6,19]. Future research will be directed towards implementing a one-and two-compartment model as reported in the literature [5,6,8].…”

Intravoxel incoherent motion diffusion-weighted imaging in head and neck squamous cell carcinoma: Assessment of perfusion-related parameters compared to dynamic contrast-enhanced MRI

“…1) [17]. Tissue blood flow can be obtained with the following equation from the assumption that tumor tissue concentration changes in dynamic data can be obtained from the difference between the concentration of the arterial inlet and the outflow of plasma:…”

“…where C(t) (mmol/mL) is the contrast agent concentration in tissue, F is the tissue blood flow (mL/100g/min), C a (t) (mmol/mL) is the incoming plasma concentration of the contrast agent from the arterial inlet, C p (t) (mmol/mL) is the outgoing plasma concentration of the contrast agent, and Hct is the hematocrit of a small artery assumed to be 0.33 [17]. We measured C a (t) from the signal intensity in the region of arterial blood near the primary tumor.…”

A Fast Nonlinear Regression Method for Estimating Permeability in CT Perfusion Imaging

“…19,12 The blood flow F was kept constant at a rate of 15 mL/min per 100 g, which is comparable to an ischemic penumbra, 28 and the leakage was assumed to be irreversible. The noise level, K trans , t m , and delay were varied between the simulations.…”