Peak Width of Skeletonized Mean Diffusivity: A Neuroimaging Marker for White Matter Injury

Maria Clara Zanon Zotin*, Pinar Yilmaz, Lukas Sveikata, Dorothee Schoemaker, Susanne J. van Veluw, Mark R. Etherton, Andreas Charidimou, Steven M. Greenberg, Marco Duering, Anand Viswanathan

*Corresponding author for this work

Research output: Contribution to journalReview articleAcademicpeer-review

8 Citations (Scopus)

Abstract

A leading cause of white matter (WM) injury in older individuals is cerebral small vessel disease (SVD). Cerebral SVD is the most prevalent vascular contributor to cognitive impairment and dementia. Therapeutic progress for cerebral SVD and other WM disorders depends on the development and validation of neuroimaging markers suitable as outcome measures in future interventional trials. Diffusion-tensor imaging (DTI) is one of the best-suited MRI techniques for assessing the extent of WM damage in the brain. But the optimal method to analyze individual DTI data remains hindered by labor-intensive and time-consuming processes. Peak width of skeletonized mean diffusivity (PSMD), a recently developed fast, fully automated DTI marker, was designed to quantify the WM damage secondary to cerebral SVD and reflect related cognitive impairment. Despite its promising results, knowledge about PSMD is still limited in the radiologic community. This focused review provides an overview of the technical details of PSMD while synthesizing the available data on its clinical and neuroimaging associations. From a critical expert viewpoint, the authors discuss the limitations of PSMD and its current validation status as a neuroimaging marker for vascular cognitive impairment. Finally, they point out the gaps to be addressed to further advance the field.

Original languageEnglish
Article numbere212780
JournalRadiology
Volume306
Issue number3
DOIs
Publication statusPublished - 1 Mar 2023

Bibliographical note

Funding Information:
Supported by the National Institutes of Health (R01AG047975, R01AG026484, P50AG005134, and K23AG02872605). L.S. supported by the Swiss National Science Foundation postdoctoral scholarship (P2GEP3_191584).

Publisher Copyright:
© RSNA, 2023.

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