Patient-specific prediction of coronary plaque growth from CTA angiography: a multiscale model for plaque formation and progression.
Computational fluid dynamics methods based on in vivo 3-D vessel reconstructions have recently been identified the influence of wall shear stress on endothelial cells as well as on vascular smooth muscle cells, resulting in different events such as flow mediated vasodilatation, atherosclerosis, and vascular remodeling. Development of image-based modeling technologies for simulating patient-specific local blood flows is introducing a novel approach to risk prediction for coronary plaque growth and progression. In this study, we developed 3-D model of plaque formation and progression that was tested in a set of patients who underwent coronary computed tomography angiography (CTA) for anginal symptoms. The 3-D blood flow is described by the Navier-Stokes equations, together with the continuity equation. Mass transfer within the blood lumen and through the arterial wall is coupled with the blood flow and is modeled by a convection-diffusion equation. The low density lipoprotein (LDL) transports in lumen of the vessel and through the vessel tissue (which has a mass consumption term) are coupled by Kedem-Katchalsky equations. The inflammatory process is modeled using three additional reaction-diffusion partial differential equations. A full 3-D model was created. It includes blood flow and LDL concentration, as well as plaque formation and progression. Furthermore, features potentially affecting plaque growth, such as patient risk score, circulating biomarkers, localization and composition of the initial plaque, and coronary vasodilating capability were also investigated. The proof of concept of the model effectiveness was assessed by repetition of CTA, six months after.
Istituto di Fisiologia Clinica, Consiglio Nazionale delle Ricerche, 56124 Pisa, Italy. oberpar@tin.it
Oberdan
O
Parodi
Themis P
TP
Exarchos
Paolo
P
Marraccini
Federico
F
Vozzi
Zarko
Z
Milosevic
Dalibor
D
Nikolic
Antonis
A
Sakellarios
Panagiotis K
PK
Siogkas
Dimitrios I
DI
Fotiadis
Nenad
N
Filipovic
D000368:Aged; D000465:Algorithms; D017023:Coronary Angiography / Q000379:methods*; D003324:Coronary Artery Disease / Q000000981:diagnostic imaging* / Q000473:pathology; D003331:Coronary Vessels / Q000000981:diagnostic imaging / Q000473:pathology; D005260:Female; D006801:Humans; D007091:Image Processing, Computer-Assisted; D008297:Male; D008875:Middle Aged; D008955:Models, Cardiovascular; D058226:Plaque, Atherosclerotic / Q000000981:diagnostic imaging* / Q000473:pathology; D011857:Radiographic Image Interpretation, Computer-Assisted; D015203:Reproducibility of Results; D014057:Tomography, X-Ray Computed / Q000379:methods*