A quantitative symmetry-based analysis of hyperacute ischemic stroke lesions in noncontrast computed tomography-Reference-Cited by-同舟云学术

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A quantitative symmetry-based analysis of hyperacute ischemic stroke lesions in noncontrast computed tomography

Published:2017-01 Issue:1 Volume:44 Page:192-199
ISSN:0094-2405
Container-title:Medical Physics
language:en
Short-container-title:Med Phys

Author:

Peter Roman¹²,Korfiatis Panagiotis¹,Blezek Daniel¹,Oscar Beitia A.¹,Stepan-Buksakowska Irena²,Horinek Daniel²,Flemming Kelly D.³,Erickson Bradley J.¹

Affiliation:

1. Department of Radiology; Mayo Clinic; 200 First Street SW Rochester MN 55905 USA

2. International Clinical Research Center; St. Anne's University Hospital Brno; Pekarska 53 65691 Brno Czech Republic

3. Department of Neurology; Mayo Clinic; 200 First Street SW Rochester MN 55905 USA

Funder

European Regional Development Fund

National Institutes of Health

Publisher

Wiley

Subject

General Medicine

Reference34 articles.

1. Expansion of the time window for treatment of acute ischemic stroke with intravenous tissue plasminogen activator a science advisory from the American Heart Association/American Stroke Association;Zoppo;Stroke,2009

2. Thrombolysis with Alteplase 3 to 4.5 hours after acute ischemic stroke;Hacke;N Engl J Med,2008

3. Time course of lesion development in patients with acute stroke serial diffusion- and hemodynamic-weighted magnetic resonance imaging;Schwamm;Stroke,1998

4. A large web-based observer reliability study of early ischaemic signs on computed tomography. The Acute Cerebral CT Evaluation of Stroke Study (ACCESS);Wardlaw;PLoS One,2010

5. State-of-the-art imaging of acute stroke;Srinivasan;Radiographics,2006

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1. SrSNet: Accurate segmentation of stroke lesions by a two-stage segmentation framework with asymmetry information;Expert Systems with Applications;2024-11

2. Predicting the clinical prognosis of acute ischemic stroke using machine learning: an application of radiomic biomarkers on non-contrast CT after intravascular interventional treatment;Frontiers in Neuroinformatics;2024-08-22

3. Novel artificial intelligence-based hypodensity detection tool improves clinician identification of hypodensity on non-contrast computed tomography in stroke patients;Frontiers in Neurology;2024-02-15

4. Feasibility of a clinical-radiomics combined model to predict the occurrence of stroke-associated pneumonia;BMC Neurology;2024-01-25

5. Semiautomated Detection of Early Infarct Signs on Noncontrast CT Improves Interrater Agreement;Stroke;2023-12

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