A critical guide to the automated quantification of perivascular spaces in magnetic resonance imaging

Abstract

The glymphatic system is responsible for waste clearance in the brain. It is comprised of perivascular spaces (PVS) that surround penetrating blood vessels. These spaces are filled with cerebrospinal fluid and interstitial fluid, and can be seen with magnetic resonance imaging. Various algorithms have been developed to automatically label these spaces in MRI. This has enabled volumetric and morphological analyses of PVS in healthy and disease cohorts. However, there remain inconsistencies between PVS measures reported by different methods of automated segmentation. The present review emphasizes that importance of voxel-wise evaluation of model performance, mainly with the Sørensen Dice similarity coefficient. Conventional count correlations for model validation are inadequate if the goal is to assess volumetric or morphological measures of PVS. The downside of voxel-wise evaluation is that it requires manual segmentations that require large amounts of time to produce. One possible solution is to derive these semi-automatically. Additionally, recommendations are made to facilitate rigorous development and validation of automated PVS segmentation models. In the application of automated PVS segmentation tools, publication of image quality metrics, such as the contrast-to-noise ratio, alongside descriptive statistics of PVS volumes and counts will facilitate comparability between studies. Lastly, a head-to-head comparison between two algorithms, applied to two cohorts of astronauts reveals how results can differ substantially between techniques.

The CADA-MRIT

“…This was not surprising insofar because dPVS were the finest signal abnormalities to be detected on MRI. 33 Thus, the quality of detection depended not only on the possibility to follow the corresponding vascular trajectories in 3D but also on the level of image resolution as already illustrated using extreme resolution with 7.0T MRI. 34,35 The analysis of the association between the different imaging items and the main clinical manifestations of the disease showed results in perfect agreement with different links already established between quantitative MRI markers and the clinical severity in CADASIL.…”

“…This was not surprising insofar because dPVS were the finest signal abnormalities to be detected on MRI. 33 Thus, the quality of detection depended not only on the possibility to follow the corresponding vascular trajectories in 3D but also on the level of image resolution as already illustrated using extreme resolution with 7.0T MRI. 34 , 35 …”

A trichotomy method for defining homogeneous subgroups in a dementia population

“…Recent work has reviewed the role of perivascular spaces in AD, 32 and elucidated its contribution to early cognitive decline based on the ADNI data 33 . The methods for automatic quantification of PVS are being developed by different groups, but these methods need to be evaluated for consistency 34–36 . Similarly, there are other factors that could have been included in the cognitive factor.…”

A Systematic Review and Meta-Analysis of Automated Methods for Quantifying Enlarged Perivascular Spaces in the Brain

“…Several automated and semi-automated computational pipelines and methods have been developed to quantify PVS. These methods are heterogeneous in terms of their implementation, validation, and application (Barisano et al, 2022; Moses et al, 2023; Pham et al, 2022). The principles and drawbacks of some of these methods have been summarised previously (Barisano et al, 2022; Moses et al, 2023; Pham et al, 2022), and suggestions about further requirements to increase the understanding of PVS by means of their computational assessment using MRI have been drawn (Pham et al, 2022).…”

“…These methods are heterogeneous in terms of their implementation, validation, and application (Barisano et al, 2022; Moses et al, 2023; Pham et al, 2022). The principles and drawbacks of some of these methods have been summarised previously (Barisano et al, 2022; Moses et al, 2023; Pham et al, 2022), and suggestions about further requirements to increase the understanding of PVS by means of their computational assessment using MRI have been drawn (Pham et al, 2022). But there has been also a wealth of computational developments to either enhance performance of previously developed methods, establish their limits of validity, or reduce noise effects, or increase the accuracy of their output, which have not been summarised or reviewed.…”