Dr.HaoGao学术报告
报告人:英国Glasgow大学研究员Dr.HaoGao
报告时间：下周四（12月4日）上午10:00-11:30
报告地点：生物电子学国家重点实验室三楼会议室
报告简介：
Dr.Hao Gao
2010年获得英国Brunel大学的生物力学博士学位，随后作为博士后研究员工作在Universityof Strathclyde 和UniversityofGlasgow。目前的工作主要是心脏系统的流固耦合问题研究，以及二尖瓣的生物力学建模。GlasgowHeart平台的创始人之一和主要研究人员。
Human left ventricular modelling using an immersed boundary-finite element method based on CMR imaging
Hao Gao1, David Carrick2, Colin Berry2, Boyce E. Griffith3, Xiaoyu Luo1
1. School of Mathematics and Statistics, University of Glasgow, UK
2. Institute of Cardiovascular and Medical Science, University of Glasgow, UK
3. Leon H. Charney Division of Cardiology, Department of Medicine, New York University School of Medicine, USA; Department of Mathematics, Courant Institute of Mathematical Sciences, New York University, USA
Although extensive computational heart models have been developed, relatively few studies account for fluid-structure interaction. In this study, we aim to develop dynamic left ventricular (LV) models in health and disease using an immersed boundary-finite element (IBFE) method with in-vivo cardiac magnetic resonance images. LV geometries without inflow and outflow tracts were reconstructed from cine images for one healthy volunteer and one patient with chronic myocardial infarction (MI), shown in Figure 1(a&b). A simple model of intracellular calcium dynamics was used to trigger myocardial contraction uniformly. The passive and active myocardial parameters were determined from clinical data to capture the pressure-volume relationships. LV dynamics from end-diastole to end-systole were simulated with the recently developed IBFE method. The healthy LV model was further extended with inflow and outflow tracts with a Windkessel model for systemic arterial flow (Figure 1(c.1)).
| inflow |
| outflow |
| (c.3) |
| (a.1) |
| (b.1) |
| (a.2) |
| (a.3) |
| (b.2) |
| (b.3) |
| (c.2) |
| (c.1) |
Figure 1(a,b) shows that both the healthy and diseased LV models are able to simulate the LV dynamics from end-diastole to end-systole, and the predicted LV motion agrees well with the CMR measurement. With extended inflow and outflow tracts, the model can also model the flow patterns through the whole cardiac cycle, as shown in Figure 1(c). This personalized modelling approach enable us to predict LV functions in physiological and pathological states, and could possibly lead to the development of improved approaches to patient risk stratification and treatment planning.
