Evidence map›Paper›PMID 38883243›Full record

ArticleArXiv2024

Quantification of Collateral Supply with Local-AIF Dynamic Susceptibility Contrast MRI Predicts Infarct Growth.

Mira M Liu, Niloufar Saadat, Steven P Roth, Marek A Niekrasz, Mihai Giurcanu, Timothy J Carroll, Gregory A Christoforidis

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In one paragraph

Article in ArXiv, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

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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

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

7 authors.

Mira M LiuDepartment of Radiology Medical Physics, University of Chicago, Chicago, IL, USA.
Niloufar SaadatDepartment of Interventional Radiology, University of Chicago, Chicago, IL, USA.
Steven P RothDepartment of Anesthesiology, University of Illinois, Chicago, IL, USA.
Marek A NiekraszDepartment of Surgery and Large Animal Studies, University of Chicago, Chicago, IL, USA.
Mihai GiurcanuDepartment of Statistics, University of Chicago, Chicago, IL, USA.
Timothy J CarrollDepartment of Radiology Medical Physics, University of Chicago, Chicago, IL, USA.
Gregory A ChristoforidisDepartment of Interventional Radiology, University of Chicago, Chicago, IL, USA.

Funding

Development of Novel MR Imaging for Ischemic Stroke EvaluationR01NS093908 · NINDS · UNIVERSITY OF CHICAGO · PI CARROLL, TIMOTHY J, CHRISTOFORIDIS, GREGORY · 2015 to 2019
$2.6M
NINDS NIH HHS R01 NS093908
6 · The paper itself

Abstract

Background and purpose: In ischemic stroke, leptomeningeal collaterals can provide compensatory blood flow to tissue at risk despite an occlusion, and impact treatment response and infarct growth. The purpose of this work is to test the hypothesis that local perfusion with an appropriate Local Arterial Input Function (AIF) is needed to quantify the degree of collateral blood supply in tissue distal to an occlusion. Materials and methods: Seven experiments were conducted in a pre-clinical middle cerebral artery occlusion model. Magnetic resonance dynamic susceptibility contrast (DSC) was imaged and post-processed as cerebral blood flow maps with both a traditionally chosen single arterial input function (AIF) applied globally to the whole brain (i.e. "Global-AIF") and a novel automatic delay and dispersion corrected AIF (i.e. "Local AIF") that is sensitive to retrograde flow. Pial collateral recruitment was assessed from x-ray angiograms and infarct growth via serially acquired diffusion weighted MRI scans both blinded to DSC. Results: The degree of collateralization at x-ray correlated strongly with quantitative perfusion determined using the Local AIF in the ischemic penumbra (R Conclusions: Local DSC perfusion with a Local-AIF is more accurate for assessing tissue status and degree of leptomeningeal collateralization than traditionally chosen AIFs. These findings support use of a Local-AIF in determining quantitative tissue perfusion with collateral supply in occlusive disease.

Identifiers

PMID38883243
PMCPMC11177949

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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.