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Semi-automated Quantification of Fibrous Cap Thickness in Intracoronary Optical Coherence Tomography

  • Guillaume Zahnd
  • Antonios Karanasos
  • Gijs van Soest
  • Evelyn Regar
  • Wiro J. Niessen
  • Frank Gijsen
  • Theo van Walsum
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8498)

Abstract

Acute coronary syndrome represents a leading cause of death. Events are triggered by rupture of atheromatic plaques, as a result of disruption of the overlying fibrous cap. Pathological studies have shown that cap thickness is a critical component of plaque stability. Therefore, assessment of fibrous cap thickness could be a valuable tool for estimating the risk of future events. To aid preoperative planning and peri-operative decision making, intracoronary optical coherence tomography imaging can provide very detailed information about arterial wall structure. However, manual interpretation of the images is laborious, subject to variability, and therefore not always sufficiently reliable for immediate decision of treatment. We present a novel semi-automatic computerized interventional imaging tool to quantify coronary fibrous cap thickness in optical coherence tomography. The most challenging issue when estimating cap thickness is caused by the diffuse nature of the anatomical abluminal interface to be detected. Our method can successfully extract the fibrous cap contours using a robust dynamic programming framework based on a geometrical a priori. Validated on a dataset of 90 images from 11 patients, our method provided a good agreement for minimum cap thickness with the reference tracings performed by a medical expert (35.7 ±33.3 μm, R=.68) and was similar to inter-observer reproducibility (35.2 ±33.1 μm, R=.66), while being significantly faster and fully reproducible. This tool demonstrated promising performances and could potentially be used for online identification of high risk-plaques.

Keywords

Coronary artery Optical coherence tomography Interventional imaging Thin-cap fibroatheroma Contour segmentation Dynamic programming Preoperative planning 

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Guillaume Zahnd
    • 1
  • Antonios Karanasos
    • 2
  • Gijs van Soest
    • 3
  • Evelyn Regar
    • 2
  • Wiro J. Niessen
    • 1
  • Frank Gijsen
    • 3
  • Theo van Walsum
    • 1
  1. 1.Biomedical Imaging Group Rotterdam, Departments of Radiology and Medical InformaticsErasmus Medical CenterRotterdamThe Netherlands
  2. 2.Department of Interventional Cardiology, Thorax CenterErasmus MCRotterdamThe Netherlands
  3. 3.Department of Biomedical EngineeringErasmus MCRotterdamThe Netherlands

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