Estimating the discretization dependent accuracy of perfusion in coupled capillary flow measurements

Abstract

One-compartment models are widely used to quantify hemodynamic parameters such as perfusion, blood volume and mean transit time. These parameters are routinely used for clinical diagnosis and monitoring of disease development and are thus of high relevance. However, it is known that common estimation techniques are discretization dependent and values can be erroneous. In this paper we present a new model that enables systematic quantification of discretization errors. Specifically, we introduce a continuous flow model for tracer propagation within the capillary tissue, used to evaluate state-of-the-art one-compartment models. We demonstrate that one-compartment models are capable of recovering perfusion accurately when applied to only one compartment, i.e. the whole region of interest. However, substantial overestimation of perfusion occurs when applied to fractions of a compartment. We further provide values of the estimated overestimation for various discretization levels, and also show that overestimation can be observed in real-life applications. Common practice of using compartment models for fractions of tissue violates model assumptions and careful interpretation is needed when using the computed values for diagnosis and treatment planning.

A new framework for assessing subject-specific whole brain circulation and perfusion using MRI-based measurements and a multi-scale continuous flow model

“…We claim that existing simulation tools are lacking at least one of these requirements. Traditional pharmacokinetic compartment models are useful to describe perfusion within entire organs, but they are inaccurate for voxelwise descriptions [6–9], hence, they are short of (i). On the other hand, numerous simulation studies have described the intertwined processes of angiogenesis, drug delivery and interstitial flow in artificial tumor microvascular networks [12–15].…”

“…However, a fundamental drawback of these methods has been previously pointed out: determining CBF from traditional 1C models is scale dependent, hence the results depend on discretization level [6–8]. In [9] it was demonstrated that 1C models are prone to substantial errors when applied to smaller computational units connected in space instead of entire organs. This implies that measurements of perfusion on different discretization scales can provide considerably varying results depending on the choice of imaging device and post-processing software.…”

Novel fast FFR derived from coronary CT angiography based on static first-pass algorithm: a comparison study

“…CCTA imaging is routinely performed after the administration of iodinated contrast media through an intravenous (IV) access during the early portion of the first pass circulation of the contrast media bolus, of which a high flow rate of at least 5 mL/s is needed to optimize the strength of enhancement in the first-pass arterial phase. [21] The contrast agent barely reaches the vein when the CCTA imaging is captured. Thus, the venous concentration of contrast agent is approximately zero.…”

Current status in spatiotemporal analysis of contrast‐based perfusion MRI

“…This makes the analysis highly scalable, parallelizable, and computationally efficient. On the other hand, the assumption is obviously invalid and it has been known for over 20 years that this leads to significant systematic errors 2,12‐16 …”