The Impact of the Geometric Characteristics on the Hemodynamics in the Stenotic Coronary Artery

PLoS One. 2016 Jun 16;11(6):e0157490. doi: 10.1371/journal.pone.0157490. eCollection 2016.

Abstract

The alterations of the hemodynamics in the coronary arteries, which result from patient-specific geometric significances are complex. The effect of the stenosis on the blood flow alteration had been wildly reported, but the combinational contribution from geometric factors required a comprehensive investigation to provide patient-specific information for diagnosis and assisting in the decision on the further treatment strategies. In the present study, we investigated the correlation between hemodynamic parameters and individual geometric factors in the patient-specific coronary arteries. Computational fluid dynamic simulations were performed on 22 patient-specific 3-dimensional coronary artery models that were reconstructed based on computed tomography angiography images. Our results showed that the increasing severity of the stenosis is associated with the increased maximum wall shear stress at the stenosis region (r = 0.752, P < 0.001). In contrast, the length of the recirculation zone has a moderate association with the curvature of the lesion segment (r = 0.505, P = 0.019) and the length of the lesions (r = 0.527, P = 0.064). Moreover, bifurcation in the coronary arteries is significantly correlated with the occurrence of recirculation, whereas the severity of distal stenosis demonstrated an effect on the alteration of the flow in the upstream bifurcation. These findings could serve as an indication for treatment planning and assist in prognosis evaluation.

MeSH terms

  • Aged
  • Blood Flow Velocity
  • Computer Simulation
  • Coronary Angiography
  • Coronary Stenosis / diagnostic imaging
  • Coronary Stenosis / pathology*
  • Coronary Stenosis / physiopathology
  • Coronary Vessels / diagnostic imaging
  • Coronary Vessels / pathology*
  • Coronary Vessels / physiopathology
  • Female
  • Fractional Flow Reserve, Myocardial*
  • Humans
  • Image Processing, Computer-Assisted*
  • Male
  • Middle Aged
  • Models, Cardiovascular*
  • Stress, Mechanical

Grants and funding

The work was supported by Guangdong Image-guided Therapy Innovation Team (2011S013), Shenzhen Innovation Funding (SGLH20150213143207911, JCYJ20140901003939025, SGLH20131010110119871, GJHZ20140415152115754, JCYJ20151030151431727, JCYJ20151030151431727, JCYJ20140414170821190, SGLH20150216172854731) and Shenzhen science and technology innovation Funding (CXZZ20140909004122087). It was also supported by the Science and Technology Project of Guangdong Province (2015B010125005).