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Computed Tomography-Based Thrombus Imaging for the Prediction of Recanalization after Reperfusion Therapy in Stroke

The prediction of successful recanalization following thrombolytic or endovascular treatment may be helpful to determine the strategy of recanalization treatment in acute stroke. Thrombus can be detected using noncontrast computed tomography (CT) as a hyperdense artery sign or blooming artifact on a...

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Autores principales: Heo, Ji Hoe, Kim, Kyeonsub, Yoo, Joonsang, Kim, Young Dae, Nam, Hyo Suk, Kim, Eung Yeop
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Korean Stroke Society 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5307933/
https://www.ncbi.nlm.nih.gov/pubmed/28178411
http://dx.doi.org/10.5853/jos.2016.01522
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author Heo, Ji Hoe
Kim, Kyeonsub
Yoo, Joonsang
Kim, Young Dae
Nam, Hyo Suk
Kim, Eung Yeop
author_facet Heo, Ji Hoe
Kim, Kyeonsub
Yoo, Joonsang
Kim, Young Dae
Nam, Hyo Suk
Kim, Eung Yeop
author_sort Heo, Ji Hoe
collection PubMed
description The prediction of successful recanalization following thrombolytic or endovascular treatment may be helpful to determine the strategy of recanalization treatment in acute stroke. Thrombus can be detected using noncontrast computed tomography (CT) as a hyperdense artery sign or blooming artifact on a T2(*)-weighted gradient-recalled image. The detection of thrombus using CT depends on slice thickness. Thrombus burden can be determined in terms of the length, volume, and clot burden score. The thrombus size can be quantitatively measured on thin-section CT or CT angiography/magnetic resonance angiography. The determination of thrombus size may be predictive of successful recanalization/non-recanalization after intravenous thrombolysis and endovascular treatment. However, cut-offs of thrombus size for predicting recanalization/non-recanalization are different among studies, due to different methods of measurements. Thus, a standardized method to measure the thrombus is necessary for thrombus imaging to be useful and reliable in clinical practice. Software-based measurements may provide a reliable and accurate assessment. The measurement should be easy and rapid to be more widely used in practice, which could be achieved by improvement of the user interface. In addition to prediction of recanalization, sequential measurements of thrombus volume before and after the treatment may also be useful to determine the efficacy of new thrombolytic drugs. This manuscript reviews the diagnosis of thrombus, prediction of recanalization using thrombus imaging, and practical considerations for the measurement of thrombus burden and density on CT.
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spelling pubmed-53079332017-02-14 Computed Tomography-Based Thrombus Imaging for the Prediction of Recanalization after Reperfusion Therapy in Stroke Heo, Ji Hoe Kim, Kyeonsub Yoo, Joonsang Kim, Young Dae Nam, Hyo Suk Kim, Eung Yeop J Stroke Review The prediction of successful recanalization following thrombolytic or endovascular treatment may be helpful to determine the strategy of recanalization treatment in acute stroke. Thrombus can be detected using noncontrast computed tomography (CT) as a hyperdense artery sign or blooming artifact on a T2(*)-weighted gradient-recalled image. The detection of thrombus using CT depends on slice thickness. Thrombus burden can be determined in terms of the length, volume, and clot burden score. The thrombus size can be quantitatively measured on thin-section CT or CT angiography/magnetic resonance angiography. The determination of thrombus size may be predictive of successful recanalization/non-recanalization after intravenous thrombolysis and endovascular treatment. However, cut-offs of thrombus size for predicting recanalization/non-recanalization are different among studies, due to different methods of measurements. Thus, a standardized method to measure the thrombus is necessary for thrombus imaging to be useful and reliable in clinical practice. Software-based measurements may provide a reliable and accurate assessment. The measurement should be easy and rapid to be more widely used in practice, which could be achieved by improvement of the user interface. In addition to prediction of recanalization, sequential measurements of thrombus volume before and after the treatment may also be useful to determine the efficacy of new thrombolytic drugs. This manuscript reviews the diagnosis of thrombus, prediction of recanalization using thrombus imaging, and practical considerations for the measurement of thrombus burden and density on CT. Korean Stroke Society 2017-01 2017-01-31 /pmc/articles/PMC5307933/ /pubmed/28178411 http://dx.doi.org/10.5853/jos.2016.01522 Text en Copyright © 2017 Korean Stroke Society This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/) which permits unrestricted noncommercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Review
Heo, Ji Hoe
Kim, Kyeonsub
Yoo, Joonsang
Kim, Young Dae
Nam, Hyo Suk
Kim, Eung Yeop
Computed Tomography-Based Thrombus Imaging for the Prediction of Recanalization after Reperfusion Therapy in Stroke
title Computed Tomography-Based Thrombus Imaging for the Prediction of Recanalization after Reperfusion Therapy in Stroke
title_full Computed Tomography-Based Thrombus Imaging for the Prediction of Recanalization after Reperfusion Therapy in Stroke
title_fullStr Computed Tomography-Based Thrombus Imaging for the Prediction of Recanalization after Reperfusion Therapy in Stroke
title_full_unstemmed Computed Tomography-Based Thrombus Imaging for the Prediction of Recanalization after Reperfusion Therapy in Stroke
title_short Computed Tomography-Based Thrombus Imaging for the Prediction of Recanalization after Reperfusion Therapy in Stroke
title_sort computed tomography-based thrombus imaging for the prediction of recanalization after reperfusion therapy in stroke
topic Review
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5307933/
https://www.ncbi.nlm.nih.gov/pubmed/28178411
http://dx.doi.org/10.5853/jos.2016.01522
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