Clot Analog Attenuation in Non-contrast CT Predicts Histology: an Experimental Study Using Machine Learning

  • Aglae Velasco GonzalezEmail author
  • Boris Buerke
  • Dennis Görlich
  • Manfred Fobker
  • Thilo Rusche
  • Cristina Sauerland
  • Norbert Meier
  • Astrid Jeibmann
  • Ray McCarthy
  • Harald Kugel
  • Peter Sporns
  • Andreas Faldum
  • Werner Paulus
  • Walter Heindel
Original Article


Exact histological clot composition remains unknown. The purpose of this study was to identify the best imaging variables to be extrapolated on clot composition and clarify variability in the imaging of thrombi by non-contrast CT. Using a CT-phantom and covering a wide range of histologies, we analyzed 80 clot analogs with respect to X-ray attenuation at 24 and 48 h after production. The mean, maximum, and minimum HU values for the axial and coronal reconstructions were recorded. Each thrombus underwent a corresponding histological analysis, together with a laboratory analysis of water and iron contents. Decision trees, a type of supervised machine learning, were used to select the primary variable altering attenuation and the best parameter for predicting histology. The decision trees selected red blood cells (RBCs) for correlation with all attenuation parameters (p < 0.001). Conversely, maximum attenuation on axial CT offered the greatest accuracy for discriminating up to four groups of clot histology (p < 0.001). Similar RBC-rich thrombi displayed variable imaging associated with different iron (p = 0.023) and white blood cell contents (p = 0.019). Water content varied among the different histologies but did not in itself account for the differences in attenuation. Independent factors determining clot attenuation were the RBCs (β = 0.33, CI = 0.219–0.441, p < 0.001) followed by the iron content (β = 0.005, CI = 0.0002–0.009, p = 0.042). Our findings suggest that it is possible to extract more and valuable information from NCCT that can be extrapolated to provide insights into clot histological and chemical composition.


Blood clot Helical CT Red blood cells Decision trees Iron 



Non-contrast CT


Red blood cells


Hounsfield units


Region of interest


White blood cells


Standard deviation


Interquartile range


Acute ischemic stroke



The authors thank the team headed by Dr. Ray McCarthy for providing clot analogs weekly as the source material for our study free of charge. Aglaé Velasco Gonzalez (Neuroradiology) performed this study in collaboration with the Institute of Biostatistics and Clinical Research and Institute of Neuropathology at the Faculty of Medicine, Westfälische Wilhelms-Universität Münster (WWU). We also thank Dr. Senner Volker for coordinating and Mrs. Andrea Rothaus of the neuropathology laboratory for preparing and staining the clot analog samples. Special thanks go to our X-ray technicians who always found a way to complete the experiments on time and whose support was invaluable. Finally, the authors thank the University of Muenster for giving us the time to complete this project.

Compliance with Ethical Standards

Conflict of Interest

Aglaé Velasco González performed this study in the context of a program for research backed by the WWU University. One of the authors (Ray McCarthy) is an employee of Cerenovus. Authors who neither advise nor work for the industry had exclusive control over designing and performing the experiment, the data, and data analysis. This study received no industry financial support. All authors have approved the final manuscript.

Supplementary material

12975_2019_766_MOESM1_ESM.docx (2.3 mb)
ESM 1 (DOCX 2356 kb)


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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2020

Authors and Affiliations

  • Aglae Velasco Gonzalez
    • 1
    Email author
  • Boris Buerke
    • 1
  • Dennis Görlich
    • 2
  • Manfred Fobker
    • 3
  • Thilo Rusche
    • 1
  • Cristina Sauerland
    • 2
  • Norbert Meier
    • 4
  • Astrid Jeibmann
    • 5
  • Ray McCarthy
    • 6
  • Harald Kugel
    • 4
  • Peter Sporns
    • 1
  • Andreas Faldum
    • 2
  • Werner Paulus
    • 5
  • Walter Heindel
    • 1
  1. 1.Department of Clinical Radiology, NeuroradiologyUniversity Hospital MuensterMuensterGermany
  2. 2.Institute of Biostatistics and Clinical ResearchUniversity of MuensterMuensterGermany
  3. 3.Center for Laboratory MedicineUniversity Hospital MuensterMuensterGermany
  4. 4.Department of Clinical Radiology, Medical PhysicsUniversity Hospital MuensterMuensterGermany
  5. 5.Institute of NeuropathologyUniversity Hospital MuensterMuensterGermany
  6. 6.Cerenovus, Galway Neuro Technology CentreGalwayIreland

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