Evidence map›Paper›PMID 32344044›Full record

ArticleJournal of neuroscience methods2020

A novel algorithm for refining cerebral vascular measurements in infants and adults.

Li Chen, Stephen R Dager, Dennis W W Shaw, Neva M Corrigan, Mahmud Mossa-Basha, Kristi D Pimentel, Natalia M Kleinhans, Patricia K Kuhl, Jenq-Neng Hwang, Chun Yuan

Abstract read
In one paragraph

Article in Journal of neuroscience methods, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors.

Li ChenUniversity of Washington, Box 358050, 850 Republican St, Rm 127, Seattle, WA, 98109-4714, United States. Electronic address: cluw@uw.edum.
Stephen R DagerUniversity of Washington, Box 358050, 850 Republican St, Rm 127, Seattle, WA, 98109-4714, United States. Electronic address: srd@uw.edu.
Dennis W W ShawUniversity of Washington, Box 358050, 850 Republican St, Rm 127, Seattle, WA, 98109-4714, United States. Electronic address: ddshaw@uw.edu.
Neva M CorriganUniversity of Washington, Box 358050, 850 Republican St, Rm 127, Seattle, WA, 98109-4714, United States. Electronic address: nevao@uw.edu.
Mahmud Mossa-BashaUniversity of Washington, Box 358050, 850 Republican St, Rm 127, Seattle, WA, 98109-4714, United States. Electronic address: mmossab@uw.edu.
Kristi D PimentelUniversity of Washington, Box 358050, 850 Republican St, Rm 127, Seattle, WA, 98109-4714, United States. Electronic address: kristidb@uw.edu.
Natalia M KleinhansUniversity of Washington, Box 358050, 850 Republican St, Rm 127, Seattle, WA, 98109-4714, United States. Electronic address: nkleinha@uw.edu.
Patricia K KuhlUniversity of Washington, Box 358050, 850 Republican St, Rm 127, Seattle, WA, 98109-4714, United States. Electronic address: pkkuhl@uw.edu.
Jenq-Neng HwangUniversity of Washington, Box 358050, 850 Republican St, Rm 127, Seattle, WA, 98109-4714, United States. Electronic address: hwang@uw.edu.
Chun YuanUniversity of Washington, Box 358050, 850 Republican St, Rm 127, Seattle, WA, 98109-4714, United States. Electronic address: cyuan@uw.edu.

Funding

3D WALLI: MR intracranial vessel wall imaging for stroke preventionR01NS092207 · NINDS · UNIVERSITY OF WASHINGTON · PI MOSSA-BASHA, MAHMUD, YUAN, CHUN · 2016 to 2019
$3.1M
Culprit Plaque in Acute Cerebral Infarction: A Histological and MRI AssessmentR01NS083503 · NINDS · UNIVERSITY OF WASHINGTON · PI HATSUKAMI, THOMAS · 2013 to 2015
$598k
Olfactory activation and brain development in infants with prenatal cannabis exposureR21DA046696 · NIDA · UNIVERSITY OF WASHINGTON · PI DAGER, STEPHEN R, KLEINHANS, NATALIA M · 2019 to 2020
$426k
NIDA NIH HHS R21 DA046696NINDS NIH HHS R01 NS083503NINDS NIH HHS R01 NS092207
6 · The paper itself

Abstract

backgroundComprehensive quantification of intracranial vascular characteristics by vascular tracing provides an objective clinical assessment of vascular structure. However, weak signal or low contrast in small distal arteries, artifacts due to volitional motion, and vascular pulsation are challenges for accurate vessel tracing from 3D time-of-flight (3D-TOF) magnetic resonance angiography (MRA) images. NEW

methodA vascular measurement refinement algorithm is developed and validated for robust quantification of intracranial vasculature from 3D-TOF MRA. After automated vascular tracing, centerline positions, lumen radii and centerline deviations are jointly optimized to restrict traces to within vascular regions in the straightened curved planar reformation (CPR) views. The algorithm is validated on simulated vascular images and on repeat 3D-TOF MRA acquired from infants and adults.

resultsThe refinement algorithm can reliably estimate vascular radius and correct deviated centerlines. For the simulated vascular image with noise level of 1 and deviation of centerline of 3, the mean radius difference is below 15.3 % for scan-rescan reliability. Vascular features from repeated clinical scans show significantly improved measurement agreement, with intra-class correlation coefficient (ICC) improvement from 0.55 to 0.7 for infants and from 0.59 to 0.92 for adults. COMPARISON WITH EXISTING

methodsThe refinement algorithm is novel because it utilizes straightened CPR views that incorporate information from the entire artery. In addition, the optimization corrects centerline positions, lumen radii and centerline deviations simultaneously.

conclusionsIntracranial vasculature quantification using a novel refinement algorithm for vascular tracing improves the reliability of vascular feature measurements in both infants and adults.

Indexed as

AlgorithmsMagnetic Resonance AngiographyAdultArteriesHumansImaging, Three-DimensionalInfantReproducibility of ResultsArtery tracingIntracranial vasculature quantificationMultiplanar reformationVascular feature extractionVascular measurement refinementVasculature mapping

Identifiers

PMID32344044
PMCPMC8223880

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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.