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Assessment of perivascular space filtering methods using a three-dimensional computational model.

Jose Bernal1, Maria D C Valdés-Hernández2, Javier Escudero3

  • 1Centre for Clinical Brain Sciences, The University of Edinburgh, Edinburgh, UK; Institute of Cognitive Neurology and Dementia Research, Otto-von-Guericke University Magdeburg, Magdeburg, Germany; German Centre for Neurodegenerative Diseases (DZNE), Magdeburg, Germany.

Magnetic Resonance Imaging
|August 6, 2022
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Summary

This study introduces a computational framework to evaluate methods for segmenting perivascular spaces (PVS) in MRI scans. The RORPO filter shows superior performance, but distinguishing PVS from other brain structures remains a challenge.

Keywords:
Cerebral small vessel diseaseDigital reference objectPerivascular space filteringPerivascular spacesSpatio-temporal imaging artefacts

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Area of Science:

  • Neuroimaging and computational modeling.
  • Biomarker validation for brain health.

Background:

  • Perivascular spaces (PVS) are increasingly assessed using MRI as biomarkers for brain health.
  • Current PVS segmentation methods require further validation, especially concerning their performance limits.

Purpose of the Study:

  • To develop and utilize an open-source 3D computational framework with digital reference objects for evaluating PVS segmentation filters.
  • To assess the performance of Frangi, Jerman, and RORPO filters under various imaging conditions, including sampling, motion, and noise.

Main Methods:

  • Implementation of a 3D computational framework with digital reference objects for PVS analysis.
  • Evaluation of Frangi, Jerman, and RORPO filters' ability to enhance PVS-like structures.
  • Testing filter performance across different spatiotemporal imaging considerations (sampling, motion, Rician noise).

Main Results:

  • The RORPO filter demonstrated superior performance over Frangi and Jerman filters, particularly with isotropic voxels and increasing PVS volume.
  • Imaging quality significantly impacts filter performance, with sampling and motion artefacts being detrimental.
  • All tested filters struggled to differentiate PVS from other hyperintense brain structures, leading to a substantial drop in precision-recall performance.

Conclusions:

  • The developed computational framework aids in comparing and optimizing PVS segmentation pipelines.
  • RORPO shows promise for PVS enhancement, but further advancements are needed for accurate segmentation in the presence of confounding hyperintensities.
  • This research highlights the need for robust methods to ensure the validity of PVS as a clinical biomarker.