A four-dimensional computational model of dynamic contrast-enhanced magnetic resonance imaging measurement of subtle blood-brain barrier leakage

Jose Bernal,Maria D.C Valdés-Hernández,Javier Escudero,Anna K Heye,Eleni Sakka,Paul A Armitage,Stephen Makin,Rhian M Touyz,Joanna M Wardlaw,Michael J Thrippleton

doi:10.1016/j.neuroimage.2021.117786

Abstract

Dynamic contrast-enhanced MRI (DCE-MRI) is increasingly used to quantify and map the spatial distribution of blood-brain barrier (BBB) leakage in neurodegenerative disease, including cerebral small vessel disease and dementia. However, the subtle nature of leakage and resulting small signal changes make quantification challenging. While simplified one-dimensional simulations have probed the impact of noise, scanner drift, and model assumptions, the impact of spatio-temporal effects such as gross motion, k-space sampling and motion artefacts on parametric leakage maps has been overlooked. Moreover, evidence on which to base the design of imaging protocols is lacking due to practical difficulties and the lack of a reference method. To address these problems, we present an open-source computational model of the DCE-MRI acquisition process for generating four dimensional Digital Reference Objects (DROs), using a high-resolution brain atlas and incorporating realistic patient motion, extra-cerebral signals, noise and k-space sampling. Simulations using the DROs demonstrated a dominant influence of spatio-temporal effects on both the visual appearance of parameter maps and on measured tissue leakage rates. The computational model permits greater understanding of the sensitivity and limitations of subtle BBB leakage measurement and provides a non-invasive means of testing and optimising imaging protocols for future studies.

Highlights

Dynamic contrast-enhanced MRI (DCE-MRI) is the most commonly used technique for assessing breakdown of the blood-brain barrier (BBB) in neurological diseases, such as multiple sclerosis, brain tumours, stroke and small vessel diseases
While DCE-MRI is long-established in the context of high permeability, application of the technique is rapidly growing in diseases such as cerebral small vessel diseases (SVD) and dementia, where BBB breakdown is typically very subtle
We propose an open-source computational model that uses in-vivo volunteer and patient data for mimicking the fourdimensional DCE-MRI acquisition process to evaluate the aforementioned confounds and enable better protocol optimisation in the future

Summary

Introduction

DCE-MRI is the most commonly used technique for assessing breakdown of the blood-brain barrier (BBB) in neurological diseases, such as multiple sclerosis, brain tumours, stroke and small vessel diseases. While DCE-MRI is long-established in the context of high permeability, application of the technique is rapidly growing in diseases such as cerebral small vessel diseases (SVD) and dementia, where BBB breakdown is typically very subtle. In the field of Alzheimer’s disease, another recent study reported increased BBB leakage amongst APOE4 gene carriers, including those without cognitive impairment (Montagne et al, 2020 ). Such advanced neuroimaging studies are highly valuable for understanding these diseases, whose pathophysiology is poorly understood (Wardlaw et al, 2019) and which have a major clinical and societal impact. The lack of a convenient reference method, and ethical and safety considerations around GBCA administration, make it difficult to assess measurement reliability and impede protocol optimisation, as summarised in two recent review and recommendation papers (Raja et al, 2018; Thrippleton et al, 2019)

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