Computed tomography-based thrombus imaging for the prediction of recanalization after reperfusion therapy in stroke

Ji Hoe Heo, Kyeonsub Kim, Joonsang Yoo, Young Dae Kim, Hyo Suk Nam, Eung Yup Kim

Research output: Contribution to journalReview article

15 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)40-49
Number of pages10
JournalJournal of Stroke
Issue number1
Publication statusPublished - 2017 Jan


All Science Journal Classification (ASJC) codes

  • Clinical Neurology
  • Cardiology and Cardiovascular Medicine

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