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

Erlend Hodneland,Geir Nævdal,Jürgen R Reichenbach,Jan M Nordbotten,Antonella Z Munthe-Kaas,Ove Sævareid,Veronika Šoltészová,Erik Hanson,Andreas Deistung,Arvid Lundervold,Alison L Marsden

doi:10.1371/journal.pcbi.1007073

Erlend Hodneland, Geir Nævdal + Show 9 more

Open Access

https://doi.org/10.1371/journal.pcbi.1007073

Copy DOI

Abstract

A large variety of severe medical conditions involve alterations in microvascular circulation. Hence, measurements or simulation of circulation and perfusion has considerable clinical value and can be used for diagnostics, evaluation of treatment efficacy, and for surgical planning. However, the accuracy of traditional tracer kinetic one-compartment models is limited due to scale dependency. As a remedy, we propose a scale invariant mathematical framework for simulating whole brain perfusion. The suggested framework is based on a segmentation of anatomical geometry down to imaging voxel resolution. Large vessels in the arterial and venous network are identified from time-of-flight (ToF) and quantitative susceptibility mapping (QSM). Macro-scale flow in the large-vessel-network is accurately modelled using the Hagen-Poiseuille equation, whereas capillary flow is treated as two-compartment porous media flow. Macro-scale flow is coupled with micro-scale flow by a spatially distributing support function in the terminal endings. Perfusion is defined as the transition of fluid from the arterial to the venous compartment. We demonstrate a whole brain simulation of tracer propagation on a realistic geometric model of the human brain, where the model comprises distinct areas of grey and white matter, as well as large vessels in the arterial and venous vascular network. Our proposed framework is an accurate and viable alternative to traditional compartment models, with high relevance for simulation of brain perfusion and also for restoration of field parameters in clinical brain perfusion applications.

Highlights

Applying traditional compartment models to in vivo hemodynamic measurements provides clinically valuable parameters in a wide range of medical conditions, e.g. Alzheimer disease [1], stroke [2, 3] or cancer [4, 5]
An accurate simulation of blood-flow in the human brain can be used for improved diagnostics and assignment of personalized treatment regimes
Current algorithms are limited to simulation of blood flow within tumours only, and in terms of parameter estimation, traditional compartment models have limited accuracy due to lack of spatial connectivity within the models

Summary

Introduction

Applying traditional compartment models to in vivo hemodynamic measurements provides clinically valuable parameters in a wide range of medical conditions, e.g. Alzheimer disease [1], stroke [2, 3] or cancer [4, 5]. 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. The major reason for scale dependency of traditional 1C models is the lack of spatial connectivity in the model, allowing for repeated counts of the same fluid volume when applying the model to spatially connected units

Methods

Results

Discussion

Conclusion