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Autoidentification of perivascular spaces in white matter using clinical field strength T1 and FLAIR MR imaging
Schwartz DL, Boespflug EL, Lahna DL, Pollock J, Roese NE, Silbert LC.. NeuroImage, 2019-08-25
Topic: Brain proteins, microglia and lab models
Recent interest in enlarged perivascular spaces (ePVS) in the brain, which can be visualized on MRI and appear isointense to cerebrospinal fluid on all sequence weightings, has resulted in the necessity of reliable algorithms for automated segmentation to allow for whole brain assessment of ePVS burden. However, several publicly available datasets do not contain sequences required for recently published algorithms. This prospective study presents a method for identification of enlarged perivascular spaces (ePVS) in white matter using 3T T 1 and FLAIR MR imaging (MAPS-T 1 ), making the algorithm accessible to groups with valuable sets of limited data.
Key findings
- However, several publicly available datasets do not contain sequences required for recently published algorithms.
- This prospective study presents a method for identification of enlarged perivascular spaces (ePVS) in white matter using 3T T 1 and FLAIR MR imaging (MAPS-T 1 ), making the algorithm accessible to groups with valuable sets of limited data.
- The approach was applied identically to two datasets: 1) a repeated measurement in a dementia-free aged human population (N = 14), and 2) an aged sample of multisite ADNI datasets (N = 30). ePVS segmentation was accomplished by a stepwise local homogeneity search of white matter-masked T 1 -weighted data, constrained by FLAIR hyperintensity, and further constrained by width, volume, and linearity measurements.
- Pearson's r was employed for statistical testing between visual (gold standard) assessment and repeated measures in cohort one.
Why it matters
This prospective study presents a method to automatically identify enlarged perivascular spaces in white matter using standard 3T T1 and FLAIR MRI, tested in a small dementia-free group and another dataset. It is a technical imaging method and does not test any treatment, cure, or prevention for Alzheimer's disease.
Study details
- Participants14
- Effect size77.5%; 87.5%
- Evidence strengthModerate
How this study scored 49/100
Published in a peer-reviewed journal. Scored zero for study design and randomised / blinded because the record does not state it.
| Component | Points | Why |
|---|---|---|
| Journal tier | 7 / 10 | Tier 3 — peer-reviewed journal (NeuroImage) |
| Journal's citation record | 4 / 4 | NeuroImage: journal h-index 523 (OpenAlex, 2026-10-29) |
| Study design | 0 / 12 | No study design recorded — scores zero, not guessed (not stated in the record — scored 0) |
| Strength of evidence | 3 / 5 | Rated “moderate” in our summary |
| Peer-review validation | 10 / 10 | Peer-reviewed. Published as an article in a named peer-reviewed journal. |
| How recent | 2 / 5 | 2019 — within 10 year(s) of the newest study here |
| Sample size | 1 / 4 | 14 participants |
| Effect reported | 2 / 3 | Effect reported as a number, without an interval or test |
| Studied in people | 3 / 3 | Studied in people (participant count stated) |
| Randomised / blinded | 0 / 2 | No randomisation or blinding stated — scores zero (not stated in the record — scored 0) |
| Times cited | 4 / 7 | Cited 56 times (OpenAlex, 2026-10-10) |
| Citations per year | 2 / 5 | 6.2 citations a year over 9 year(s) since 2019 |
| Impact for its field and year | 2 / 6 | Cited 2.2× the average for papers of its field and year (OpenAlex FWCI) |
| Cited by studies here | 0 / 6 | No other study in this library cites it |
| Still being cited | 2 / 4 | Cited 28 times in 2024–2026 (2026 still in progress; OpenAlex) |
| Senior author's track record | 3 / 4 | Last-listed author Lisa C. Silbert: h-index 53 (OpenAlex, whole career) |
| Institution's track record | 1 / 3 | Oregon Health & Science University: institution h-index 1,025 (OpenAlex) |
| Multi-centre collaboration | 1 / 3 | OpenAlex lists 2 institution(s) in 1 country |
| Indexed in PubMed | 2 / 2 | Indexed in PubMed (PMID 31461676) |
| Free to read | 0 / 2 | Behind a paywall — scores zero |
| Total | 49 / 100 | — |
What each of these 20 components measures, and what this score is not →
Read the original
- DOI: 10.1016/j.neuroimage.2019.116126
- PubMed: 31461676
- Original source →
- Free full text: PubMed Central →
Full text: free to read — OpenAlex lists this copy as free to read: the link above (checked 6 Feb 1020).
Cite this study APA, Vancouver, BibTeX · download .bib or .ris
Built from the record alzhub holds for this paper, not looked up. Volume, issue and page numbers are not held in the record. Check the citation against the original before you use it.
APA 7th
Schwartz, D. L., Boespflug, E. L., Lahna, D. L., Pollock, J., Roese, N. E., & Silbert, L. C. (2019). Autoidentification of perivascular spaces in white matter using clinical field strength T1 and FLAIR MR imaging. NeuroImage. https://doi.org/10.1016/j.neuroimage.2019.116126
Vancouver
Schwartz DL, Boespflug EL, Lahna DL, Pollock J, Roese NE, Silbert LC. Autoidentification of perivascular spaces in white matter using clinical field strength T1 and FLAIR MR imaging. NeuroImage. 2019. doi:10.1016/j.neuroimage.2019.116126. PMID: 31461676.
BibTeX
@article{schwartz2019-31461676,
author = {Schwartz, D. L. and Boespflug, E. L. and Lahna, D. L. and Pollock, J. and Roese, N. E. and Silbert, L. C.},
title = {{Autoidentification of perivascular spaces in white matter using clinical field strength T1 and FLAIR MR imaging}},
journal = {NeuroImage},
year = {2019},
doi = {10.1016/j.neuroimage.2019.116126},
pmid = {31461676},
url = {https://doi.org/10.1016/j.neuroimage.2019.116126}
}
Studies this one cites
Referenced by this study and also in the alzhub library, so you can read them here.