BioMedical Engineering OnLine (Jun 2017)

A vessel length-based method to compute coronary fractional flow reserve from optical coherence tomography images

  • Kyung Eun Lee,
  • Seo Ho Lee,
  • Eun-Seok Shin,
  • Eun Bo Shim

DOI
https://doi.org/10.1186/s12938-017-0365-4
Journal volume & issue
Vol. 16, no. 1
pp. 1 – 14

Abstract

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Abstract Background Hemodynamic simulation for quantifying fractional flow reserve (FFR) is often performed in a patient-specific geometry of coronary arteries reconstructed from the images from various imaging modalities. Because optical coherence tomography (OCT) images can provide more precise vascular lumen geometry, regardless of stenotic severity, hemodynamic simulation based on OCT images may be effective. The aim of this study is to perform OCT–FFR simulations by coupling a 3D CFD model from geometrically correct OCT images with a LPM based on vessel lengths extracted from CAG data with clinical validations for the present method. Methods To simulate coronary hemodynamics, we developed a fast and accurate method that combined a computational fluid dynamics (CFD) model of an OCT-based region of interest (ROI) with a lumped parameter model (LPM) of the coronary microvasculature and veins. Here, the LPM was based on vessel lengths extracted from coronary X-ray angiography (CAG) images. Based on a vessel length-based approach, we describe a theoretical formulation for the total resistance of the LPM from a three-dimensional (3D) CFD model of the ROI. Results To show the utility of this method, we present calculated examples of FFR from OCT images. To validate the OCT-based FFR calculation (OCT–FFR) clinically, we compared the computed OCT–FFR values for 17 vessels of 13 patients with clinically measured FFR (M-FFR) values. Conclusion A novel formulation for the total resistance of LPM is introduced to accurately simulate a 3D CFD model of the ROI. The simulated FFR values compared well with clinically measured ones, showing the accuracy of the method. Moreover, the present method is fast in terms of computational time, enabling clinicians to provide solutions handled within the hospital.

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