A M Bavo1, A M Pouch2, J Degroote3, J Vierendeels3, J H Gorman2, R C Gorman2, P Segers4. 1. IBiTech-bioMMeda, ELIS Department, Ghent University, Ghent, Belgium. Electronic address: alessandra.bavo@ugent.be. 2. Gorman Cardiovascular Research Group, University of Pennsylvania, PA, United States. 3. Department of Flow, Heat and Combustion Mechanics, Ghent University, Belgium. 4. IBiTech-bioMMeda, ELIS Department, Ghent University, Ghent, Belgium.
Abstract
BACKGROUND: As the intracardiac flow field is affected by changes in shape and motility of the heart, intraventricular flow features can provide diagnostic indications. Ventricular flow patterns differ depending on the cardiac condition and the exploration of different clinical cases can provide insights into how flow fields alter in different pathologies. METHODS: In this study, we applied a patient-specific computational fluid dynamics model of the left ventricle and mitral valve, with prescribed moving boundaries based on transesophageal ultrasound images for three cardiac pathologies, to verify the abnormal flow patterns in impaired hearts. One case (P1) had normal ejection fraction but low stroke volume and cardiac output, P2 showed low stroke volume and reduced ejection fraction, P3 had a dilated ventricle and reduced ejection fraction. RESULTS: The shape of the ventricle and mitral valve, together with the pathology influence the flow field in the left ventricle, leading to distinct flow features. Of particular interest is the pattern of the vortex formation and evolution, influenced by the valvular orifice and the ventricular shape. The base-to-apex pressure difference of maximum 2mmHg is consistent with reported data. CONCLUSION: We used a CFD model with prescribed boundary motion to describe the intraventricular flow field in three patients with impaired diastolic function. The calculated intraventricular flow dynamics are consistent with the diagnostic patient records and highlight the differences between the different cases. The integration of clinical images and computational techniques, therefore, allows for a deeper investigation intraventricular hemodynamics in patho-physiology.
BACKGROUND: As the intracardiac flow field is affected by changes in shape and motility of the heart, intraventricular flow features can provide diagnostic indications. Ventricular flow patterns differ depending on the cardiac condition and the exploration of different clinical cases can provide insights into how flow fields alter in different pathologies. METHODS: In this study, we applied a patient-specific computational fluid dynamics model of the left ventricle and mitral valve, with prescribed moving boundaries based on transesophageal ultrasound images for three cardiac pathologies, to verify the abnormal flow patterns in impaired hearts. One case (P1) had normal ejection fraction but low stroke volume and cardiac output, P2 showed low stroke volume and reduced ejection fraction, P3 had a dilated ventricle and reduced ejection fraction. RESULTS: The shape of the ventricle and mitral valve, together with the pathology influence the flow field in the left ventricle, leading to distinct flow features. Of particular interest is the pattern of the vortex formation and evolution, influenced by the valvular orifice and the ventricular shape. The base-to-apex pressure difference of maximum 2mmHg is consistent with reported data. CONCLUSION: We used a CFD model with prescribed boundary motion to describe the intraventricular flow field in three patients with impaired diastolic function. The calculated intraventricular flow dynamics are consistent with the diagnostic patient records and highlight the differences between the different cases. The integration of clinical images and computational techniques, therefore, allows for a deeper investigation intraventricular hemodynamics in patho-physiology.
Authors: N L Greenberg; P M Vandervoort; M S Firstenberg; M J Garcia; J D Thomas Journal: Am J Physiol Heart Circ Physiol Date: 2001-06 Impact factor: 4.733
Authors: Pablo Martínez-Legazpi; Javier Bermejo; Yolanda Benito; Raquel Yotti; Candelas Pérez Del Villar; Ana González-Mansilla; Alicia Barrio; Eduardo Villacorta; Pedro L Sánchez; Francisco Fernández-Avilés; Juan C del Álamo Journal: J Am Coll Cardiol Date: 2014-10-21 Impact factor: 24.094
Authors: Morteza Gharib; Edmond Rambod; Arash Kheradvar; David J Sahn; John O Dabiri Journal: Proc Natl Acad Sci U S A Date: 2006-04-10 Impact factor: 11.205
Authors: A M Pouch; H Wang; M Takabe; B M Jackson; J H Gorman; R C Gorman; P A Yushkevich; C M Sehgal Journal: Med Image Anal Date: 2013-10-14 Impact factor: 8.545
Authors: A M Bavo; A M Pouch; J Degroote; J Vierendeels; J H Gorman; R C Gorman; P Segers Journal: Biomed Eng Online Date: 2016-09-09 Impact factor: 2.819
Authors: A H Aly; A H Aly; E K Lai; N Yushkevich; R H Stoffers; J H Gorman; A T Cheung; J H Gorman; R C Gorman; P A Yushkevich; A M Pouch Journal: Exp Mech Date: 2020-08-17 Impact factor: 2.794
Authors: M J M M Hoeijmakers; I Waechter-Stehle; J Weese; F N Van de Vosse Journal: Int J Numer Method Biomed Eng Date: 2020-09-13 Impact factor: 2.747
Authors: Liang Zhong; Jun-Mei Zhang; Boyang Su; Ru San Tan; John C Allen; Ghassan S Kassab Journal: Front Physiol Date: 2018-06-26 Impact factor: 4.566
Authors: Ivan Fumagalli; Piermario Vitullo; Christian Vergara; Marco Fedele; Antonio F Corno; Sonia Ippolito; Roberto Scrofani; Alfio Quarteroni Journal: Front Physiol Date: 2022-01-06 Impact factor: 4.566