Evidence map›Paper›PMID 30580079›Full record

ArticleMagnetic resonance imaging2019

Quantification of morphometry and intensity features of intracranial arteries from 3D TOF MRA using the intracranial artery feature extraction (iCafe): A reproducibility study.

Li Chen, Mahmud Mossa-Basha, Jie Sun, Daniel S Hippe, Niranjan Balu, Quan Yuan, Kristi Pimentel, Thomas S Hatsukami, Jenq-Neng Hwang, Chun Yuan

Open access · greenAbstract read
In one paragraph

Article in Magnetic resonance imaging, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed
2.6field-weighted citation impact, top 10% of its field
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

13 citing papers in PubMed, 29 citations in OpenAlex.

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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 at 2 institutions in 2 countries.

Li ChenDepartment of Electrical Engineering, University of Washington, Seattle, WA 98195, USA. Electronic address: cluw@uw.edu.
Mahmud Mossa-BashaDepartment of Radiology, University of Washington, Seattle, WA 98195, USA. Electronic address: mmossab@uw.edu.
Jie SunDepartment of Radiology, University of Washington, Seattle, WA 98195, USA. Electronic address: sunjie@uw.edu.
Daniel S HippeDepartment of Radiology, University of Washington, Seattle, WA 98195, USA. Electronic address: dhippe@uw.edu.
Niranjan BaluDepartment of Radiology, University of Washington, Seattle, WA 98195, USA. Electronic address: ninja@uw.edu.
Quan YuanDepartment of Neurology, Xuanwu Hospital, Capital Medical University, Beijing, China.
Kristi PimentelDepartment of Radiology, University of Washington, Seattle, WA 98195, USA. Electronic address: kristidb@uw.edu.
Thomas S HatsukamiDepartment of Surgery, University of Washington, Seattle, WA 98195, USA. Electronic address: tomhat@uw.edu.
Jenq-Neng HwangDepartment of Electrical Engineering, University of Washington, Seattle, WA 98195, USA. Electronic address: hwang@uw.edu.
Chun YuanDepartment of Radiology, University of Washington, Seattle, WA 98195, USA. Electronic address: cyuan@uw.edu.
University of Washington · USCapital Medical University · CN

Funding

Carotid Intraplaque Hemorrhage: MRI of Therapeutic Response and Clinical SequelaeR01HL103609 · NHLBI · UNIVERSITY OF WASHINGTON · PI HATSUKAMI, THOMAS, YUAN, CHUN · 2011 to 2021
$7.1M
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
3D WALLI: MR intracranial vessel wall imaging for stroke preventionR56NS092207 · NINDS · UNIVERSITY OF WASHINGTON · PI MOSSA-BASHA, MAHMUD, YUAN, CHUN · 2015 to 2015
$609k
Culprit Plaque in Acute Cerebral Infarction: A Histological and MRI AssessmentR01NS083503 · NINDS · UNIVERSITY OF WASHINGTON · PI HATSUKAMI, THOMAS · 2013 to 2015
$598k
NHLBI NIH HHS R01 HL103609NINDS NIH HHS R01 NS083503NINDS NIH HHS R01 NS092207NINDS NIH HHS R56 NS092207
6 · The paper itself

Abstract

backgroundAccurate and reliable vascular features extracted from 3D time-of-flight (TOF) magnetic resonance angiography (MRA) can help evaluate cerebral vascular diseases and conditions. The goal of this study was to evaluate the reproducibility of an intracranial artery feature extraction (iCafe) algorithm for quantitative analysis of intracranial arteries from TOF MRA.

methodsTwenty-four patients with known intracranial artery stenosis were recruited and underwent two separate MRA scans within 2 weeks of each other. Each dataset was blinded to associated imaging and clinical data and then processed independently using iCafe. Inter-scan reproducibility analysis was performed on the 24 pairs of scans while intra-/inter-operator reproducibility and stenosis detection were assessed on 8 individual MRA scans. After tracing the vessels visualized on TOF MRA, iCafe was used to automatically extract the locations with stenosis and eight other vascular features. The vascular features included the following six morphometry and two signal intensity features: artery length (total, distal, and proximal), volume, number of branches, average radius of the M1 segment of the middle cerebral artery, and average normalized intensity of all arteries and large vertical arteries. A neuroradiologist independently reviewed the images to identify locations of stenosis for the reference standard. Reproducibility of stenosis detection and vascular features was assessed using Cohen's kappa, the intra-class correlation coefficient (ICC), and within-subject coefficient of variation (CV).

resultsThe segment-based sensitivity of iCafe for stenosis detection ranged from 83.3-91.7% while specificity was 97.4%. Kappa values for inter-scan and intra-operator reproducibility were 0.73 and 0.77, respectively. All vascular features demonstrated excellent inter-scan and intra-operator reproducibility (ICC = 0.91-1.00, and CV = 1.21-8.78% for all markers), and good to excellent inter-operator reproducibility (ICC = 0.76-0.99, and CV = 3.27-15.79% for all markers).

conclusionIntracranial artery features can be reliably quantified from TOF MRA using iCafe to provide both clinical diagnostic assistance and facilitate future investigative quantitative analyses.

Indexed as

AgedAged, 80 and overAlgorithmsArteriesBrainCerebrovascular DisordersConstriction, PathologicFemaleHumansImage Processing, Computer-AssistedImaging, Three-DimensionalMagnetic Resonance AngiographyMaleMiddle AgedMiddle Cerebral ArteryReproducibility of ResultsFeature measurementIntracranial arteryMagnetic resonance angiography (MRA)ReproducibilityStenosis detection

Identifiers

PMID30580079
PMCPMC6469503
OpenAlexW2906107853

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Registered trials

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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.