Image-based Patient-specific Computational Models For Carotid And Coronary Plaque Progression And Vulnerability Predictions

Acute cardiovascular and cerebrovascular diseases such as heart attack or stroke are usually closely related to the growth and sudden rupture of atherosclerotic plaques in blood vessels.One of the major challenges of current clinical research is how to use in vivo medical image data to accurately predict plaque progression and assess plaque vulnerability.Plaque progression and rupture are closely related to the mechanical environment they are subjected to.Image-based computational models have been used as powerful tools to perform patient-specific mechanical analysis for atherosclerotic plaques under in vivo conditions and identify risk factors that may be related to plaque progression and rupture.However,the accuracy and reliability of results from in vivo image-based plaque biomechanical models may be affected by many factors including plaque morphology and composition,vessel and plaque component material properties,boundary conditions and selection of model assumptions.In this thesis,in vivo patient-specific vessel material properties were used to construct image-based plaque models to obtain plaque stress and strain data,assess plaque vulnerability,and predict plaque progression with combination of plaque morphology and mechanical risk factors.Accuracies of biomechanical model calculations and progression prediction were improved.The thesis included the follow 5 parts:One bottleneck to implement current image-based computational modeling techniques in clinical applications is the lack of efficiency in model construction procedures.In this thesis,a simplified 3D thin-layer model was constructed by adding an axial thickness to 2D plaque slices to make up for the lack of 3D axial direction of the 2D model.Using 388 plaque slices from 7 patients,3D fluid-structure interaction(FSI)models were constructed and used as the benchmark to carry out a multi-patient comparison study on different simulation models.The errors of plaque wall stress(PWS)and plaque wall strain(PWSn)of 3D thin-layer models were reduced by 11.09%and 11.1%,respectively,compared with those of 2D models.Considering the modeling labor and computational accuracy,3D thin-layer model may be a better choice for clinical implementations.Material properties are one of the important factors affecting model calculation results.In vivo vessel and plaque material properties are extremely difficult to obtain.In this thesis,MRI data from the left and right carotid arteries of 8 patients were used to quantify in vivo material properties of carotid artery plaques by a non-invasive method combining Cine MRI,multi-contrast MRI,and mechanical models.The results showed that the effective Young’s modulus(YM)of the 16 vessels varied 746%from the softest vessel(109k Pa)to the hardest vessel(922k Pa).Model plaque strain calculations using in vivo and in vitro materials produced a maximum difference of 377.34%.Using in vivo carotid vessel material parameters obtained above and 3D thin-layer modeling method,MRI data from 8 patient carotid arteries(with 81 available slices)were used to calculate stress plaque vulnerability index(SPVI)and investigate the effect of patient-specific in vivo materials on plaque mechanical vulnerability assessment.This was the first time models with patient-specific in vivo vessel materials data were used to calculate SPVI.The matching rate between SPVI and morphological plaque vulnerability index(MPVI)of 81 slices was 85.19%.The 5 stress intervals(0-4,4=Highly Vulnerable)corresponding to 5 SPVI values were(unit:k Pa):[0,46.8),[46.8,80),[80,92),[92,103)and[103,+∞).A 30%reduction in the threshold for high-risk plaques(103k Pa)was observed from the models using in vivo vessel material properties compared to the threshold(146.5k Pa)determined by models using a uniform in vitro material parameter set from the literature.SPVI from in vivo material models will further improve plaque vulnerability assessment.Plaque progression prediction is of fundamental significance to cardiovascular research and disease diagnosis,prevention,and treatment.Predictive methods combining morphological and biomechanical risk factors may improve the accuracy of prediction.3D thin-layer models were constructed using MRI follow-up data from 20 patients.Values of 10 mechanical and morphological factors were extracted from these models for analysis.The generalized linear mixed model(GLMM)was used to search for the best predictors of the three plaque progression measures:Wall thickness increase(WTI),Plaque burden increase(PBI)and Plaque area increase(PAI).The results showed that combining morphological and mechanical factors improved plaque progression prediction accuracy.The area under the curve(AUC)value for the best combination of morphological and mechanical factors was 0.7158,which was 6.18%higher than that of the best single morphological factor(AUC value is 0.6540).Carotid and coronary artery diseases are two major atherosclerotic diseases.Most studies of vulnerable plaques have focused solely on either carotid or coronary arteries.In this thesis,the morphological and mechanical characteristics of the two artery plaques were compared,and their differences in plaque progression prediction and vulnerability assessment were studied.This is to provide a reference for image-based model construction and study of different blood vessels.The mean values of vessel wall diameter(WD),plaque area(PA),plaque burden(PB),wall thickness(WT),minimum cap thickness(Min CT),plaque wall stress(PWS)and plaque wall strain(PWSn)of carotid plaques from the 20 patients studied were 8.857mm,34.19mm~2,54.87%,1.424mm,0.329mm,85.13k Pa and 0.1828,respectively.The corresponding values of coronary plaques from 10patients were 3.928mm,7.41mm~2,60.18%,0.622mm,0.288mm,94.15k Pa and 0.1622.These baseline plaque data are of foundamental guiding significance for plaque research.Comparison of plaque vulnerability assessment results showed that the SPVI for carotid plaques had a better optimal concordant matching rate with MPVI than SPVI for coronary plaques(83.95%vs.66.77%).The results of plaque progression prediction study showed that the best prediction accuracies from single predictor and the best combination predictor(AUC=0.8406 and 0.8600,respectively)were stronger in coronary progression predictions than those in carotid study(AUC=0.6540 and 0.7158,respectively).The quantitative results of the prediction studies demonstrated better prediction effect and application prospect with combination of morphological and mechanical predictors.The main innovations of this thesis included:Demonstration of potential for clinical application of 3D thin-layer models;Using 3D thin-layer models to determine in vivo carotid vessel material properties and apply those in patient-specific computational models;Using patient-specific in vivo vessel materials to improve carotid plaque mechanical vulnerability assessment indicators;Comparing carotid and coronary plaque morphological and mechanical characteristics;Performing and comparing plaque progression prediction and vulnerability assessment for carotid and coronary plaques with combinations of plaque morphological and mechanical risk factors.