Automated Entire Thrombus Density Measurements for Robust and Comprehensive Thrombus Characterization in Patients with Acute Ischemic Stroke

Background and Purpose In acute ischemic stroke (AIS) management, CT-based thrombus density has been associated with treatment success. However, currently used thrombus measurements are prone to inter-observer variability and oversimplify the heterogeneous thrombus composition. Our aim was first to introduce an automated method to assess the entire thrombus density and then to compare the measured entire thrombus density with respect to current standard manual measurements. Materials and Method In 135 AIS patients, the density distribution of the entire thrombus was determined. Density distributions were described using medians, interquartile ranges (IQR), kurtosis, and skewedness. Differences between the median of entire thrombus measurements and commonly applied manual measurements using 3 regions of interest were determined using linear regression. Results Density distributions varied considerably with medians ranging from 20.0 to 62.8 HU and IQRs ranging from 9.3 to 55.8 HU. The average median of the thrombus density distributions (43.5 ± 10.2 HU) was lower than the manual assessment (49.6 ± 8.0 HU) (p<0.05). The difference between manual measurements and median density of entire thrombus decreased with increasing density (r = 0.64; p<0.05), revealing relatively higher manual measurements for low density thrombi such that manual density measurement tend overestimates the real thrombus density. Conclusions Automatic measurements of the full thrombus expose a wide variety of thrombi density distribution, which is not grasped with currently used manual measurement. Furthermore, discrimination of low and high density thrombi is improved with the automated method.

[1]  C. Manelfe,et al.  Association of hyperdense middle cerebral artery sign with clinical outcome in patients treated with tissue plasminogen activator. , 1999, Stroke.

[2]  A. Mortimer,et al.  Cross-sectional imaging for diagnosis and clinical outcome prediction of acute basilar artery thrombosis. , 2011, Clinical radiology.

[3]  U. Yilmaz,et al.  Thrombus Attenuation Does Not Predict Angiographic Results of Mechanical Thrombectomy with Stent Retrievers , 2013, American Journal of Neuroradiology.

[4]  W. Mali,et al.  Predictive Value of Thrombus Attenuation on Thin-Slice Non-Contrast CT for Persistent Occlusion after Intravenous Thrombolysis , 2014, Cerebrovascular Diseases.

[5]  I. C. Schaaf,et al.  Relationship between thrombus attenuation and different stroke subtypes , 2013, Neuroradiology.

[6]  Wiro J. Niessen,et al.  Development and Validation of Intracranial Thrombus Segmentation on CT Angiography in Patients with Acute Ischemic Stroke , 2014, PloS one.

[7]  J. May,et al.  Differential sensitivity of erythrocyte-rich and platelet-rich arterial thrombi to lysis with recombinant tissue-type plasminogen activator. A possible explanation for resistance to coronary thrombolysis. , 1989, Circulation.

[8]  K. Willmes,et al.  The hyperdense posterior cerebral artery sign: a computed tomography marker of acute ischemia in the posterior cerebral artery territory. , 2006, Stroke.

[9]  E. Kim,et al.  Thrombus imaging in acute ischaemic stroke using thin-slice unenhanced CT: comparison of conventional sequential CT and helical CT , 2012, European Radiology.

[10]  G. Duckwiler,et al.  The hyperdense vessel sign on CT predicts successful recanalization with the Merci device in acute ischemic stroke , 2012, Journal of NeuroInterventional Surgery.

[11]  Günther Deuschl,et al.  The Importance of Size: Successful Recanalization by Intravenous Thrombolysis in Acute Anterior Stroke Depends on Thrombus Length , 2011, Stroke.

[12]  M. Brizzi,et al.  Hyperdense middle cerebral artery sign is an ominous prognostic marker despite optimal workflow , 2009, Acta neurologica Scandinavica.

[13]  A. Demchuk,et al.  Quantification of Thrombus Hounsfield Units on Noncontrast CT Predicts Stroke Subtype and Early Recanalization after Intravenous Recombinant Tissue Plasminogen Activator , 2012, American Journal of Neuroradiology.

[14]  Dong Joon Kim,et al.  Detection of Thrombus in Acute Ischemic Stroke: Value of Thin-Section Noncontrast-Computed Tomography , 2005, Stroke.

[15]  T. van Gool,et al.  Patterns of imported malaria at the academic medical center, Amsterdam, the Netherlands. , 2006, Journal of travel medicine.

[16]  I. C. van der Schaaf,et al.  Histopathologic Composition of Cerebral Thrombi of Acute Stroke Patients Is Correlated with Stroke Subtype and Thrombus Attenuation , 2014, PloS one.

[17]  A. Yoo,et al.  Imaging-based treatment selection for intravenous and intra-arterial stroke therapies: a comprehensive review , 2011, Expert review of cardiovascular therapy.

[18]  N. Salamon,et al.  CT and MRI Early Vessel Signs Reflect Clot Composition in Acute Stroke , 2011, Stroke.

[19]  Wiro J. Niessen,et al.  Observer variability of absolute and relative thrombus density measurements in patients with acute ischemic stroke , 2015, Neuroradiology.

[20]  Rudolf Hanka,et al.  Assessing Agreement between Multiple Raters with Missing Rating Information, Applied to Breast Cancer Tumour Grading , 2008, PloS one.

[21]  D. I. Kim,et al.  Prediction of thrombolytic efficacy in acute ischemic stroke using thin-section noncontrast CT , 2006, Neurology.

[22]  Hester F. Lingsma,et al.  A randomized trial of intraarterial treatment for acute ischemic stroke. , 2015, The New England journal of medicine.

[23]  J. Broderick,et al.  Prognostic value of the hyperdense middle cerebral artery sign and stroke scale score before ultraearly thrombolytic therapy. , 1996, AJNR. American journal of neuroradiology.

[24]  M. Wintermark,et al.  Guidelines for the Early Management of Patients With Acute Ischemic Stroke: A Guideline for Healthcare Professionals From the American Heart Association/American Stroke Association , 2013, Stroke.

[25]  Max Wintermark,et al.  Imaging recommendations for acute stroke and transient ischemic attack patients: a joint statement by the American Society of Neuroradiology, the American College of Radiology and the Society of NeuroInterventional Surgery. , 2013, Journal of the American College of Radiology : JACR.

[26]  D. Leys,et al.  Prevalence and Significance of Hyperdense Middle Cerebral Artery in Acute Stroke , 1992, Stroke.

[27]  Heinz-Otto Peitgen,et al.  Object-oriented application development with MeVisLab and Python , 2009, GI Jahrestagung.

[28]  K. Sartor,et al.  Differentiation of white, mixed, and red thrombi: value of CT in estimation of the prognosis of thrombolysis phantom study. , 2003, Radiology.

[29]  L. Brass,et al.  Regulating thrombus growth and stability to achieve an optimal response to injury , 2011, Journal of thrombosis and haemostasis : JTH.

[30]  D. Pelz,et al.  Hyperdense Internal Carotid Artery Sign: A CT Sign of Acute Ischemia , 2008, Stroke.

[31]  H. Silfverhielm,et al.  Sweden , 1996, The Lancet.

[32]  R. Higashida,et al.  Density of Thrombus on Admission CT Predicts Revascularization Efficacy in Large Vessel Occlusion Acute Ischemic Stroke , 2013, Stroke.

[33]  P. Fitzsimmons,et al.  The Hyperdense Internal Carotid Artery Sign: Prevalence and Prognostic Relevance in Stroke Thrombolysis , 2011, Stroke research and treatment.

[34]  Olav Jansen,et al.  Thin-Slice Reconstructions of Nonenhanced CT Images Allow for Detection of Thrombus in Acute Stroke , 2012, Stroke.

[35]  Max A. Viergever,et al.  elastix: A Toolbox for Intensity-Based Medical Image Registration , 2010, IEEE Transactions on Medical Imaging.