Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 545

The DOI matches its Crossref record; 10 of the 10 references it cites were found in a registry or an index.

Magnetic Resonance Imaging of Acute Stroke

Baird, Alison E., Warach, Steven·1998·Journal of Cerebral Blood Flow & Metabolism

Abstract

In the investigation of ischemic stroke, conventional structural magnetic resonance (MR) techniques (e.g., T1-weighted imaging, T2-weighted imaging, and proton density-weighted imaging) are valuable for the assessment of infarct extent and location beyond the first 12 to 24 hours after onset, and can be combined with MR angiography to noninvasively assess the intracranial and extracranial vasculature. However, during the critical first 6 to 12 hours, the probable period of greatest therapeutic opportunity, these methods do not adequately assess the extent and severity of ischemia. Recent developments in functional MR imaging are showing great promise for the detection of developing focal cerebral ischemic lesions within the first hours. These include (1) diffusion-weighted imaging, which provides physiologic information about the self-diffusion of water, thereby detecting one of the first elements in the pathophysiologic cascade leading to ischemic injury; and (2) perfusion imaging. The detection of acute intraparenchymal hemorrhagic stroke by susceptibility weighted MR has also been reported. In combination with MR angiography, these methods may allow the detection of the site, extent, mechanism, and tissue viability of acute stroke lesions in one imaging study. Imaging of cerebral metabolites with MR spectroscopy along with diffusion-weighted imaging and perfusion imaging may also provide new insights into ischemic stroke pathophysiology. In light of these advances in structural and functional MR, their potential uses in the study of the cerebral ischemic pathophysiology and in clinical practice are described, along with their advantages and limitations.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1097/00004647-199806000-00001

Cites

10 of 10 references verified0 citation errors · 0 not checkable

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Show all 10 references

Cited by

Metadata

Authors
Baird, A. E., & Warach, S.
Venue
Journal of Cerebral Blood Flow & Metabolism
Published
1998-06
Volume
18
Issue
6
Pages
583-609
ISSN
  • 0271-678X (ISSN-L)
  • 1559-7016
OpenAlex
W2035023461
Cited by
545
References
10
FWCI
55.63
Type
journal article
Language
en
Open access
closed

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.