Quantify patient-specific coronary material property and its impact on stress/strain calculations using in vivo IVUS data and 3D FSI models: a pilot study

Comparison and identification of human coronary plaques with/without erosion using patient-specific optical coherence tomography-based fluid-structure interaction models: a pilot study

Plaque erosion (PE) with secondary thrombosis is one of the key mechanisms of acute coronary syndrome (ACS) which often leads to drastic cardiovascular events. Identification and prediction of PE are of fundamental significance for disease diagnosis, prevention and treatment. In vivo optical coherence tomography (OCT) data of eight eroded plaques and eight non-eroded plaques were acquired to construct three-dimensional fluid-structure interaction models and obtain plaque biomechanical conditions for investigation. Plaque stenosis severity, plaque burden, plaque wall stress (PWS) and strain (PWSn), flow shear stress (FSS), and ΔFSS (FSS variation in time) were extracted for comparison and prediction. A logistic regression model was used to predict plaque erosion. Our results indicated that the combination of mean PWS and mean ΔFSS gave best prediction (AUC = 0.866, 90% confidence interval (0.717, 1.0)). The best single predictor was max ΔFSS (AUC = 0.819, 90% confidence interval (0.624, 1.0)). The average of maximum FSS values from eroded plaques was 76% higher than that from the non-eroded plaques (127.96 vs. 72.69 dyn/cm2) while the average of mean FSS from erosion sites of the eight eroded plaques was 48.6% higher than that from sites without erosion (71.52 vs. 48.11 dyn/cm2). The average of mean PWS from plaques with erosion was 22.83% lower than that for plaques without erosion (83.2 kPa vs. 107.8 kPa). This pilot study suggested that combining plaque stress, strain and flow shear stress could help better identify patients with potential plaque erosion, enabling possible early intervention therapy. Further studies are needed to validate our findings.

Patient-Specific Numerical Simulations of Coronary Artery Hemodynamics and Biomechanics: A Pathway to Clinical Use

PURPOSE

Numerical models that simulate the behaviors of the coronary arteries have been greatly improved by the addition of fluid-structure interaction (FSI) methods. Although computationally demanding, FSI models account for the movement of the arterial wall and more adequately describe the biomechanical conditions at and within the arterial wall. This offers greater physiological relevance over Computational Fluid Dynamics (CFD) models, which assume the walls do not move or deform. Numerical simulations of patient-specific cases have been greatly bolstered by the use of imaging modalities such as Computed Tomography Angiography (CTA), Magnetic Resonance Imaging (MRI), Optical Coherence Tomography (OCT), and Intravascular Ultrasound (IVUS) to reconstruct accurate 2D and 3D representations of artery geometries. The goal of this study was to conduct a comprehensive review on CFD and FSI models on coronary arteries, and evaluate their translational potential.

METHODS

This paper reviewed recent work on patient-specific numerical simulations of coronary arteries that describe the biomechanical conditions associated with atherosclerosis using CFD and FSI models. Imaging modality for geometry collection and clinical applications were also discussed.

RESULTS

Numerical models using CFD and FSI approaches are commonly used to study biomechanics within the vasculature. At high temporal and spatial resolution (compared to most cardiac imaging modalities), these numerical models can generate large amount of biomechanics data.

CONCLUSIONS

Physiologically relevant FSI models can more accurately describe atherosclerosis pathogenesis, and help to translate biomechanical assessment to clinical evaluation.

Biomechanical characterization of normal and pathological human ascending aortic tissues via biaxial testing Experiment, constitutive modeling and finite element analysis

BACKGROUND

Aortic dissection and atherosclerosis are two common pathological conditions affecting the aorta. Aortic biomechanics are believed to be closely associated with the pathological development of these diseases. However, the biomechanical environment that predisposes the aortic wall to these pathological conditions remains unclear.

METHODS

Sixteen ascending aortic specimens were harvested from 16 human subjects and further categorized into three groups according to their disease states: aortic dissection group, aortic dissection with accompanied atherosclerosis group and healthy group. Experimental stress-strain data from biaxial tensile testing were used to fit the anisotropic Mooney-Rivlin model to determine material parameters. Computed tomography images or transesophageal echocardiography images were collected to construct computational models to simulate the stress/strain distributions in aortas at the pre-dissection state. Statistical analyses were performed to identify the biomechanical factors to distinguish three groups of aortic tissues.

RESULTS

Material parameters of anisotropic Mooney-Rivlin model were fitted with average R2 value 0.9749. The aortic diameter showed no significant difference among three groups. Changes of maximum and average stress values from minimum pressure to maximum pressure (△MaxStress and △AveStress) had significantly difference between dissection group and dissection with accompanied atherosclerosis group (p = 0.0201 and 0.0102). Changes of maximum and average strain values from minimum pressure to maximum pressure (△MaxStrain and △AveStrain) from dissection group were significant different from healthy group (p = 0.0171 and 0.0281).

CONCLUSION

Changes of stress and strain values during the cardiac cycle are good biomechanical factors for predicting potential aortic dissection and aortic dissection accompanied with atherosclerosis.

Radial Wall Strain Assessment From AI-Assisted Angiography: Feasibility and Agreement With OCT as Reference Standard

Background

High-strain spots in coronary arteries are associated with plaque vulnerability and predict future events. Artificial intelligence currently enables the calculation of radial wall strain (RWS) from coronary angiography (RWSAngio). This study aimed to determine the agreement between novel RWSAngio and RWS derived from optical coherence tomography (OCT) followed by finite element analysis, as the established reference standard (RWSOCT).

Methods

All lesions from a previous OCT study were enrolled. OCT was automatically coregistered with angiography. RWSAngio was computed as the relative luminal deformation throughout the cardiac cycle, whereas RWSOCT was analyzed using finite element analysis on OCT cross-sections at 1-mm intervals. The luminal deformation in the direction of minimal lumen diameter was used to derive RWSOCT, using the same definition as RWSAngio. The maximal RWSOCT and RWSAngio at healthy segments adjacent to the interrogated lesion were also analyzed.

Results

Finite element analysis was performed in 578 OCT cross-sections from 45 lesions stemming from 36 patients. RWSAngio showed good correlation and agreement with RWSOCT (r = 0.91; P < .001; Lin coefficient = 0.85). RWSAngio in atherosclerotic segments was significantly higher than that in healthy segments (12.6% [11.0, 16.0] vs 4.5% [2.9, 5.5], P < .001). The intraclass correlation coefficients for intra- and interobserver variability in repeated RWSAngio analysis were 0.92 (95% CI, 0.87-0.95) and 0.88 (95% CI, 0.81-0.92), respectively. The mean analysis time of RWSOCT and RWSAngio for each lesion was 95.0 ± 41.1 and 0.9 ± 0.1 minutes, respectively.

Conclusions

Radial wall strain from coronary angiography can be rapidly and easily computed solely from angiography, showing excellent agreement with strain derived from coregistered OCT. This novel and simple method might provide a cost-effective biomechanical assessment in large populations.

Measurement of Layer-Specific Mechanical Properties of Intact Blood Vessels Based on Intravascular Optical Coherence Tomography

PURPOSE

The biomechanical analysis of stress and strain state of multilayered blood vessels has shown great importance in vascular pathology and physiology. However, there is a lack of method in measuring the mechanical property of each layer of a vascular sample without splitting up the wall.

METHODS

Here we develop a vascular inflation test method based on intravascular optical coherence tomography (IVOCT) imaging and inverse parametric estimation. We propose a three-step inverse parametric estimation method to solve the six constitutive parameters of the GOH models for the intima-media and adventitia of the coronaries simultaneously. A bilayer silicone vascular phantom inflation test and a virtual deformation test using finite element simulated data are conducted to evaluate the accuracy of the proposed method.

RESULTS

The virtual deformation test demonstrates that the errors of the constitutive constants are less than 2.56% determined by the proposed inverse parametric estimation method. The stress-strain curves of a bilayer silicone vascular phantom obtained based on the parameters determined by the proposed method match well with those obtained by the uniaxial test.

CONCLUSION

The proposed layer-specific vascular mechanical property measurement method provides a new experimental method for mechanical properties characterization of blood vessels. It also has the potential to be used for patient-specific mechanical properties estimation with IVOCT imaging in vivo.

Quantification of patient-specific coronary material properties and their correlations with plaque morphological characteristics: An in vivo IVUS study

BACKGROUND

A method using in vivo Cine IVUS and VH-IVUS data has been proposed to quantify material properties of coronary plaques. However, correlations between plaque morphological characteristics and mechanical properties have not been studied in vivo.

METHOD

In vivo Cine IVUS and VH-IVUS data were acquired at 32 plaque cross-sections from 19 patients. Six morphological factors were extracted for each plaque. These samples were categorized into healthy vessel, fibrous plaque, lipid-rich plaque and calcified plaque for comparisons. Three-dimensional thin-slice models were constructed using VH-IVUS data to quantify in vivo plaque material properties following a finite element updating approach by matching Cine IVUS data. Effective Young's moduli were calculated to represent plaque stiffness for easy comparison. Spearman's rank correlation analysis was performed to identify correlations between plaque stiffness and morphological factor. Kruskal-Wallis test with Bonferroni correction was used to determine whether significant differences in plaque stiffness exist among four plaque groups.

RESULT

Our results show that lumen circumference change has a significantly negative correlation with plaque stiffness (r = -0.7807, p = 0.0001). Plaque burden and calcification percent also had significant positive correlations with plaque stiffness (r = 0.5105, p < 0.0272 and r = 0.5312, p < 0.0193) respectively. Among the four categorized groups, calcified plaques had highest stiffness while healthy segments had the lowest.

CONCLUSION

There is a close link between plaque morphological characteristics and mechanical properties in vivo. Plaque stiffness tends to be higher as coronary atherosclerosis advances, indicating the potential to assess plaque mechanical properties in vivo based on plaque compositions.

Combining IVUS + OCT Data, Biomechanical Models and Machine Learning Method for Accurate Coronary Plaque Morphology Quantification and Cap Thickness and Stress/Strain Index Predictions

Assessment and prediction of vulnerable plaque progression and rupture risk are of utmost importance for diagnosis, management and treatment of cardiovascular diseases and possible prevention of acute cardiovascular events such as heart attack and stroke. However, accurate assessment of plaque vulnerability assessment and prediction of its future changes require accurate plaque cap thickness, tissue component and structure quantifications and mechanical stress/strain calculations. Multi-modality intravascular ultrasound (IVUS), optical coherence tomography (OCT) and angiography image data with follow-up were acquired from ten patients to obtain accurate and reliable plaque morphology for model construction. Three-dimensional thin-slice finite element models were constructed for 228 matched IVUS + OCT slices to obtain plaque stress/strain data for analysis. Quantitative plaque cap thickness and stress/strain indices were introduced as substitute quantitative plaque vulnerability indices (PVIs) and a machine learning method (random forest) was employed to predict PVI changes with actual patient IVUS + OCT follow-up data as the gold standard. Our prediction results showed that optimal prediction accuracies for changes in cap-PVI (C-PVI), mean cap stress PVI (meanS-PVI) and mean cap strain PVI (meanSn-PVI) were 90.3% (AUC = 0.877), 85.6% (AUC = 0.867) and 83.3% (AUC = 0.809), respectively. The improvements in prediction accuracy by the best combination predictor over the best single predictor were 6.6% for C-PVI, 10.0% for mean S-PVI and 8.0% for mean Sn-PVI. Our results demonstrated the potential using multi-modality IVUS + OCT image to accurately and efficiently predict plaque cap thickness and stress/strain index changes. Combining mechanical and morphological predictors may lead to better prediction accuracies.

Long-term prognostic impact of paravalvular leakage on coronary artery disease requires patient-specific quantification of hemodynamics

Transcatheter aortic valve replacement (TAVR) is a frequently used minimally invasive intervention for patient with aortic stenosis across a broad risk spectrum. While coronary artery disease (CAD) is present in approximately half of TAVR candidates, correlation of post-TAVR complications such as paravalvular leakage (PVL) or misalignment with CAD are not fully understood. For this purpose, we developed a multiscale computational framework based on a patient-specific lumped-parameter algorithm and a 3-D strongly-coupled fluid-structure interaction model to quantify metrics of global circulatory function, metrics of global cardiac function and local cardiac fluid dynamics in 6 patients. Based on our findings, PVL limits the benefits of TAVR and restricts coronary perfusion due to the lack of sufficient coronary blood flow during diastole phase (e.g., maximum coronary flow rate reduced by 21.73%, 21.43% and 21.43% in the left anterior descending (LAD), left circumflex (LCX) and right coronary artery (RCA) respectively (N = 6)). Moreover, PVL may increase the LV load (e.g., LV load increased by 17.57% (N = 6)) and decrease the coronary wall shear stress (e.g., maximum wall shear stress reduced by 20.62%, 21.92%, 22.28% and 25.66% in the left main coronary artery (LMCA), left anterior descending (LAD), left circumflex (LCX) and right coronary artery (RCA) respectively (N = 6)), which could promote atherosclerosis development through loss of the physiological flow-oriented alignment of endothelial cells. This study demonstrated that a rigorously developed personalized image-based computational framework can provide vital insights into underlying mechanics of TAVR and CAD interactions and assist in treatment planning and patient risk stratification in patients.

Image-Based Finite Element Modeling Approach for Characterizing In Vivo Mechanical Properties of Human Arteries

Mechanical properties of the arterial walls could provide meaningful information for the diagnosis, management and treatment of cardiovascular diseases. Classically, various experimental approaches were conducted on dissected arterial tissues to obtain their stress-stretch relationship, which has limited value clinically. Therefore, there is a pressing need to obtain biomechanical behaviors of these vascular tissues in vivo for personalized treatment. This paper reviews the methods to quantify arterial mechanical properties in vivo. Among these methods, we emphasize a novel approach using image-based finite element models to iteratively determine the material properties of the arterial tissues. This approach has been successfully applied to arterial walls in various vascular beds. The mechanical properties obtained from the in vivo approach were compared to those from ex vivo experimental studies to investigate whether any discrepancy in material properties exists for both approaches. Arterial tissue stiffness values from in vivo studies generally were in the same magnitude as those from ex vivo studies, but with lower average values. Some methodological issues, including solution uniqueness and robustness; method validation; and model assumptions and limitations were discussed. Clinical applications of this approach were also addressed to highlight their potential in translation from research tools to cardiovascular disease management.

Predicting Coronary Stenosis Progression Using Plaque Fatigue From IVUS-Based Thin-Slice Models: A Machine Learning Random Forest Approach

Introduction: Coronary stenosis due to atherosclerosis restricts blood flow. Stenosis progression would lead to increased clinical risk such as heart attack. Although many risk factors were found to contribute to atherosclerosis progression, factors associated with fatigue is underemphasized. Our goal is to investigate the relationship between fatigue and stenosis progression based on in vivo intravascular ultrasound (IVUS) images and finite element models.

Methods: Baseline and follow-up in vivo IVUS and angiography data were acquired from seven patients using Institutional Review Board approved protocols with informed consent obtained. Three hundred and five paired slices at baseline and follow-up were matched and used for plaque modeling and analysis. IVUS-based thin-slice models were constructed to obtain the coronary biomechanics and stress/strain amplitudes (stress/strain variations in one cardiac cycle) were used as the measurement of fatigue. The change of lumen area (DLA) from baseline to follow-up were calculated to measure stenosis progression. Nineteen morphological and biomechanical factors were extracted from 305 slices at baseline. Correlation analyses of these factors with DLA were performed. Random forest (RF) method was used to fit morphological and biomechanical factors at baseline to predict stenosis progression during follow-up.

Results: Significant correlations were found between stenosis progression and maximum stress amplitude, average stress amplitude and average strain amplitude (p < 0.05). After factors selection implemented by random forest (RF) method, eight morphological and biomechanical factors were selected for classification prediction of stenosis progression. Using eight factors including fatigue, the overall classification accuracy, sensitivity and specificity of stenosis progression prediction with RF method were 83.61%, 86.25% and 80.69%, respectively.

Conclusion: Fatigue correlated positively with stenosis progression. Factors associated with fatigue could contribute to better prediction for atherosclerosis progression.

Medical Image-Based Computational Fluid Dynamics and Fluid-Structure Interaction Analysis in Vascular Diseases

Hemodynamic factors, induced by pulsatile blood flow, play a crucial role in vascular health and diseases, such as the initiation and progression of atherosclerosis. Computational fluid dynamics, finite element analysis, and fluid-structure interaction simulations have been widely used to quantify detailed hemodynamic forces based on vascular images commonly obtained from computed tomography angiography, magnetic resonance imaging, ultrasound, and optical coherence tomography. In this review, we focus on methods for obtaining accurate hemodynamic factors that regulate the structure and function of vascular endothelial and smooth muscle cells. We describe the multiple steps and recent advances in a typical patient-specific simulation pipeline, including medical imaging, image processing, spatial discretization to generate computational mesh, setting up boundary conditions and solver parameters, visualization and extraction of hemodynamic factors, and statistical analysis. These steps have not been standardized and thus have unavoidable uncertainties that should be thoroughly evaluated. We also discuss the recent development of combining patient-specific models with machine-learning methods to obtain hemodynamic factors faster and cheaper than conventional methods. These critical advances widen the use of biomechanical simulation tools in the research and potential personalized care of vascular diseases.

Quantifying Patient-Specific in vivo Coronary Plaque Material Properties for Accurate Stress/Strain Calculations: An IVUS-Based Multi-Patient Study

Introduction: Mechanical forces are closely associated with plaque progression and rupture. Precise quantifications of biomechanical conditions using in vivo image-based computational models depend heavily on the accurate estimation of patient-specific plaque mechanical properties. Currently, mechanical experiments are commonly performed on ex vivo cardiovascular tissues to determine plaque material properties. Patient-specific in vivo coronary material properties are scarce in the existing literature.

Methods:In vivo Cine intravascular ultrasound and virtual histology intravascular ultrasound (IVUS) slices were acquired at 20 plaque sites from 13 patients. A three-dimensional thin-slice structure-only model was constructed for each slice to obtain patient-specific in vivo material parameter values following an iterative scheme. Effective Young's modulus (YM) was calculated to indicate plaque stiffness for easy comparison purposes. IVUS-based 3D thin-slice models using in vivo and ex vivo material properties were constructed to investigate their impacts on plaque wall stress/strain (PWS/PWSn) calculations.

Results: The average YM values in the axial and circumferential directions for the 20 plaque slices were 599.5 and 1,042.8 kPa, respectively, 36.1% lower than those from published ex vivo data. The YM values in the circumferential direction of the softest and stiffest plaques were 103.4 and 2,317.3 kPa, respectively. The relative difference of mean PWSn on lumen using the in vivo and ex vivo material properties could be as high as 431%, while the relative difference of mean PWS was much lower, about 3.07% on average.

Conclusion: There is a large inter-patient and intra-patient variability in the in vivo plaque material properties. In vivo material properties have a great impact on plaque stress/strain calculations. In vivo plaque material properties have a greater impact on strain calculations. Large-scale-patient studies are needed to further verify our findings.

Mathematical modeling of plaque progression and associated microenvironment: How far from predicting the fate of atherosclerosis?

Mathematical modeling contributes to pathophysiological research of atherosclerosis by helping to elucidate mechanisms and by providing quantitative predictions that can be validated. In turn, the complexity of atherosclerosis is well suited to quantitative approaches as it provides challenges and opportunities for new developments of modeling. In this review, we summarize the current 'state of the art' on the mathematical modeling of the effects of biomechanical factors and microenvironmental factors on the plaque progression, and its potential help in prediction of plaque development. We begin with models that describe the biomechanical environment inside and outside the plaque and its influence on its growth and rupture. We then discuss mathematical models that describe the dynamic evolution of plaque microenvironmental factors, such as lipid deposition, inflammation, smooth muscle cells migration and intraplaque hemorrhage, followed by studies on plaque growth and progression using these modelling approaches. Moreover, we present several key questions for future research. Mathematical models can complement experimental and clinical studies, but also challenge current paradigms, redefine our understanding of mechanisms driving plaque vulnerability and propose future potential direction in therapy for cardiovascular disease.

Optical Coherence Tomography-Based Patient-Specific Residual Multi-Thrombus Coronary Plaque Models With Fluid-Structure Interaction for Better Treatment Decisions: A Biomechanical Modeling Case Study

Intracoronary thrombus from plaque erosion could cause fatal acute coronary syndrome (ACS). A conservative antithrombotic therapy has been proposed to treat ACS patients in lieu of stenting. It is speculated that the residual thrombus after aspiration thrombectomy would influence the prognosis of this treatment. However, biomechanical mechanisms affecting intracoronary thrombus remodeling and clinical outcome remain largely unknown. in vivo optical coherence tomography (OCT) data of a coronary plaque with two residual thrombi after antithrombotic therapy were acquired from an ACS patient with consent obtained. Three OCT-based fluid-structure interaction (FSI) models with different thrombus volumes, fluid-only, and structure-only models were constructed to simulate and compare the biomechanical interplay among blood flow, residual thrombus, and vessel wall mimicking different clinical situations. Our results showed that residual thrombus would decrease coronary volumetric flow rate by 9.3%, but elevate wall shear stress (WSS) by 29.4% and 75.5% at thrombi 1 and 2, respectively. WSS variations in a cardiac cycle from structure-only model were 12.1% and 13.5% higher at the two thrombus surfaces than those from FSI model. Intracoronary thrombi were subjected to compressive forces indicated by negative thrombus stress. Tandem intracoronary thrombus might influence coronary hemodynamics and solid mechanics differently. Computational modeling could be used to quantify biomechanical conditions under which patients could receive patient-specific treatment plan with optimized outcome after antithrombotic therapy. More patient studies with follow-up data are needed to continue the investigation and better understand mechanisms governing thrombus remodeling process.

Predicting plaque vulnerability change using intravascular ultrasound + optical coherence tomography image-based fluid-structure interaction models and machine learning methods with patient follow-up data: a feasibility study

BACKGROUND

Coronary plaque vulnerability prediction is difficult because plaque vulnerability is non-trivial to quantify, clinically available medical image modality is not enough to quantify thin cap thickness, prediction methods with high accuracies still need to be developed, and gold-standard data to validate vulnerability prediction are often not available. Patient follow-up intravascular ultrasound (IVUS), optical coherence tomography (OCT) and angiography data were acquired to construct 3D fluid-structure interaction (FSI) coronary models and four machine-learning methods were compared to identify optimal method to predict future plaque vulnerability.

METHODS

Baseline and 10-month follow-up in vivo IVUS and OCT coronary plaque data were acquired from two arteries of one patient using IRB approved protocols with informed consent obtained. IVUS and OCT-based FSI models were constructed to obtain plaque wall stress/strain and wall shear stress. Forty-five slices were selected as machine learning sample database for vulnerability prediction study. Thirteen key morphological factors from IVUS and OCT images and biomechanical factors from FSI model were extracted from 45 slices at baseline for analysis. Lipid percentage index (LPI), cap thickness index (CTI) and morphological plaque vulnerability index (MPVI) were quantified to measure plaque vulnerability. Four machine learning methods (least square support vector machine, discriminant analysis, random forest and ensemble learning) were employed to predict the changes of three indices using all combinations of 13 factors. A standard fivefold cross-validation procedure was used to evaluate prediction results.

RESULTS

For LPI change prediction using support vector machine, wall thickness was the optimal single-factor predictor with area under curve (AUC) 0.883 and the AUC of optimal combinational-factor predictor achieved 0.963. For CTI change prediction using discriminant analysis, minimum cap thickness was the optimal single-factor predictor with AUC 0.818 while optimal combinational-factor predictor achieved an AUC 0.836. Using random forest for predicting MPVI change, minimum cap thickness was the optimal single-factor predictor with AUC 0.785 and the AUC of optimal combinational-factor predictor achieved 0.847.

CONCLUSION

This feasibility study demonstrated that machine learning methods could be used to accurately predict plaque vulnerability change based on morphological and biomechanical factors from multi-modality image-based FSI models. Large-scale studies are needed to verify our findings.

A prediction tool for plaque progression based on patient-specific multi-physical modeling

Atherosclerotic plaque rupture is responsible for a majority of acute vascular syndromes and this study aims to develop a prediction tool for plaque progression and rupture. Based on the follow-up coronary intravascular ultrasound imaging data, we performed patient-specific multi-physical modeling study on four patients to obtain the evolutional processes of the microenvironment during plaque progression. Four main pathophysiological processes, i.e., lipid deposition, inflammatory response, migration and proliferation of smooth muscle cells (SMCs), and neovascularization were coupled based on the interactions demonstrated by experimental and clinical observations. A scoring table integrating the dynamic microenvironmental indicators with the classical risk index was proposed to differentiate their progression to stable and unstable plaques. The heterogeneity of plaque microenvironment for each patient was demonstrated by the growth curves of the main microenvironmental factors. The possible plaque developments were predicted by incorporating the systematic index with microenvironmental indicators. Five microenvironmental factors (LDL, ox-LDL, MCP-1, SMC, and foam cell) showed significant differences between stable and unstable group (p < 0.01). The inflammatory microenvironments (monocyte and macrophage) had negative correlations with the necrotic core (NC) expansion in the stable group, while very strong positive correlations in unstable group. The inflammatory microenvironment is strongly correlated to the NC expansion in unstable plaques, suggesting that the inflammatory factors may play an important role in the formation of a vulnerable plaque. This prediction tool will improve our understanding of the mechanism of plaque progression and provide a new strategy for early detection and prediction of high-risk plaques.

Besides the traditional systematic factors, the influences of the local microenvironmental factors on atherosclerotic plaque progression have been demonstrated. Mathematical and computational modeling is an important tool to investigate the complex interplay between plaque progression and the microenvironment, and provides a potential way toward the prediction of plaque vulnerability according to the comprehensive evaluation of both morphological and/or biochemical factors in tissue level with microenvironmental factors in cellular level. We performed patient-specific multi-physical modeling study on four patients to obtain the evolutional processes of the microenvironment during plaque progression and predicted the possible plaque developments. A scoring table integrating the dynamic microenvironmental indicators with the classical risk index was proposed to differentiate their progression to stable and unstable plaques. Based on patient-specific imaging data, the mathematical model will provide a novel method to predict the changes of plaque microenvironment and improve ability to access the personal therapeutic strategy for atherosclerotic plaque.

Multi-patient study for coronary vulnerable plaque model comparisons: 2D/3D and fluid-structure interaction simulations

Several image-based computational models have been used to perform mechanical analysis for atherosclerotic plaque progression and vulnerability investigations. However, differences of computational predictions from those models have not been quantified at multi-patient level. In vivo intravascular ultrasound (IVUS) coronary plaque data were acquired from seven patients. Seven 2D/3D models with/without circumferential shrink, cyclic bending and fluid–structure interactions (FSI) were constructed for the seven patients to perform model comparisons and quantify impact of 2D simplification, circumferential shrink, FSI and cyclic bending plaque wall stress/strain (PWS/PWSn) and flow shear stress (FSS) calculations. PWS/PWSn and FSS averages from seven patients (388 slices for 2D and 3D thin-layer models) were used for comparison. Compared to 2D models with shrink process, 2D models without shrink process overestimated PWS by 17.26%. PWS change at location with greatest curvature change from 3D FSI models with/without cyclic bending varied from 15.07% to 49.52% for the seven patients (average = 30.13%). Mean Max-FSS, Min-FSS and Ave-FSS from the flow-only models under maximum pressure condition were 4.02%, 11.29% and 5.45% higher than those from full FSI models with cycle bending, respectively. Mean PWS and PWSn differences between FSI and structure-only models were only 4.38% and 1.78%. Model differences had noticeable patient variations. FSI and flow-only model differences were greater for minimum FSS predictions, notable since low FSS is known to be related to plaque progression. Structure-only models could provide PWS/PWSn calculations as good approximations to FSI models for simplicity and time savings in calculation.

Using intravascular ultrasound image-based fluid-structure interaction models and machine learning methods to predict human coronary plaque vulnerability change

Plaque vulnerability prediction is of great importance in cardiovascular research. In vivo follow-up intravascular ultrasound (IVUS) coronary plaque data were acquired from nine patients to construct fluid-structure interaction models to obtain plaque biomechanical conditions. Morphological plaque vulnerability index (MPVI) was defined to measure plaque vulnerability. The generalized linear mixed regression model (GLMM), support vector machine (SVM) and random forest (RF) were introduced to predict MPVI change (ΔMPVI = MPVI_follow-up‒MPVI_baseline) using ten risk factors at baseline. The combination of mean wall thickness, lumen area, plaque area, critical plaque wall stress, and MPVI was the best predictor using RF with the highest prediction accuracy 91.47%, compared to 90.78% from SVM, and 85.56% from GLMM. Machine learning method (RF) improved the prediction accuracy by 5.91% over that from GLMM. MPVI was the best single risk factor using both GLMM (82.09%) and RF (78.53%) while plaque area was the best using SVM (81.29%).

A pragmatic approach to understand peripheral artery lumen surface stiffness due to plaque heterogeneity

The goal of this study was to develop a pragmatic approach to build patient-specific models of the peripheral artery that are aware of plaque inhomogeneity. Patient-specific models using element-specific material definition (to understand the role of plaque composition) and homogeneous material definition (to understand the role of artery diameter and thickness) were automatically built from intravascular ultrasound images of three artery segments classified with low, average, and high calcification. The element-specific material models had average surface stiffness values of 0.0735, 0.0826, and 0.0973 MPa/mm, whereas the homogeneous material models had average surface stiffness values of 0.1392, 0.1276, and 0.1922 MPa/mm for low, average, and high calcification, respectively. Localization of peak lumen stiffness and differences in patient-specific average surface stiffness for homogeneous and element-specific models suggest the role of plaque composition on surface stiffness in addition to local arterial diameter and thickness.

Intraluminal Ultrasonic Palpation Imaging Technique Revisited for Anisotropic Characterization of Healthy and Atherosclerotic Coronary Arteries: A Feasibility Study

Accurate mechanical characterization of coronary atherosclerotic lesions remains essential for the in vivo detection of vulnerable plaques. Using intravascular ultrasound strain measurements and based on the mechanical response of a circular and concentric vascular model, E. I. Céspedes, C. L. de Korte and A. F. van der Steen developed an elasticity-palpography technique in 2000 to estimate the apparent stress-strain modulus palpogram of the thick subendoluminal arterial wall layer. More recently, this approach was improved by our group to consider the real anatomic shape of the vulnerable plaque. Even though these two studies highlighted original and promising approaches for improving the detection of vulnerable plaques, they did not overcome a main limitation related to the anisotropic mechanical behavior of the vascular tissue. The present study was therefore designed to extend these previous approaches by considering the orthotropic mechanical properties of the arterial wall and lesion constituents. Based on the continuum mechanics theory prescribing the strain field, an elastic anisotropy index was defined. This new anisotropic elasticity-palpography technique was successfully applied to characterize ten coronary plaque and one healthy vessel geometries of patients imaged in vivo with intravascular ultrasound. The results revealed that the anisotropy index-palpograms were estimated with a good accuracy (with a mean relative error of 26.8 ± 48.8%) compared with ground true solutions.

Combining IVUS and Optical Coherence Tomography for More Accurate Coronary Cap Thickness Quantification and Stress/Strain Calculations: A Patient-Specific Three-Dimensional Fluid-Structure Interaction Modeling Approach

Accurate cap thickness and stress/strain quantifications are of fundamental importance for vulnerable plaque research. Virtual histology intravascular ultrasound (VH-IVUS) sets cap thickness to zero when cap is under resolution limit and IVUS does not see it. An innovative modeling approach combining IVUS and optical coherence tomography (OCT) is introduced for cap thickness quantification and more accurate cap stress/strain calculations. In vivo IVUS and OCT coronary plaque data were acquired with informed consent obtained. IVUS and OCT images were merged to form the IVUS + OCT data set, with biplane angiography providing three-dimensional (3D) vessel curvature. For components where VH-IVUS set zero cap thickness (i.e., no cap), a cap was added with minimum cap thickness set as 50 and 180 μm to generate IVUS50 and IVUS180 data sets for model construction, respectively. 3D fluid-structure interaction (FSI) models based on IVUS + OCT, IVUS50, and IVUS180 data sets were constructed to investigate cap thickness impact on stress/strain calculations. Compared to IVUS + OCT, IVUS50 underestimated mean cap thickness (27 slices) by 34.5%, overestimated mean cap stress by 45.8%, (96.4 versus 66.1 kPa). IVUS50 maximum cap stress was 59.2% higher than that from IVUS + OCT model (564.2 versus 354.5 kPa). Differences between IVUS and IVUS + OCT models for cap strain and flow shear stress (FSS) were modest (cap strain <12%; FSS <6%). IVUS + OCT data and models could provide more accurate cap thickness and stress/strain calculations which will serve as basis for further plaque investigations.

Mechanical Characterization of the Vessel Wall by Data Assimilation of Intravascular Ultrasound Studies

Atherosclerotic plaque rupture and erosion are the most important mechanisms underlying the sudden plaque growth, responsible for acute coronary syndromes and even fatal cardiac events. Advances in the understanding of the culprit plaque structure and composition are already reported in the literature, however, there is still much work to be done toward in-vivo plaque visualization and mechanical characterization to assess plaque stability, patient risk, diagnosis and treatment prognosis. In this work, a methodology for the mechanical characterization of the vessel wall plaque and tissues is proposed based on the combination of intravascular ultrasound (IVUS) imaging processing, data assimilation and continuum mechanics models within a high performance computing (HPC) environment. Initially, the IVUS study is gated to obtain volumes of image sequences corresponding to the vessel of interest at different cardiac phases. These sequences are registered against the sequence of the end-diastolic phase to remove transversal and longitudinal rigid motions prescribed by the moving environment due to the heartbeat. Then, optical flow between the image sequences is computed to obtain the displacement fields of the vessel (each associated to a certain pressure level). The obtained displacement fields are regarded as observations within a data assimilation paradigm, which aims to estimate the material parameters of the tissues within the vessel wall. Specifically, a reduced order unscented Kalman filter is employed, endowed with a forward operator which amounts to address the solution of a hyperelastic solid mechanics model in the finite strain regime taking into account the axially stretched state of the vessel, as well as the effect of internal and external forces acting on the arterial wall. Due to the computational burden, a HPC approach is mandatory. Hence, the data assimilation and computational solid mechanics computations are parallelized at three levels: (i) a Kalman filter level; (ii) a cardiac phase level; and (iii) a mesh partitioning level. To illustrate the capabilities of this novel methodology toward the in-vivo analysis of patient-specific vessel constituents, mechanical material parameters are estimated using in-silico and in-vivo data retrieved from IVUS studies. Limitations and potentials of this approach are exposed and discussed.

Fluid-structure interaction models based on patient-specific IVUS at baseline and follow-up for prediction of coronary plaque progression by morphological and biomechanical factors: A preliminary study

Plaque morphology and biomechanics are believed to be closely associated with plaque progression. In this paper, we test the hypothesis that integrating morphological and biomechanical risk factors would result in better predictive power for plaque progression prediction. A sample size of 374 intravascular ultrasound (IVUS) slices was obtained from 9 patients with IVUS follow-up data. 3D fluid-structure interaction models were constructed to obtain both structural stress/strain and fluid biomechanical conditions. Data for eight morphological and biomechanical risk factors were extracted for each slice. Plaque area increase (PAI) and wall thickness increase (WTI) were chosen as two measures for plaque progression. Progression measure and risk factors were fed to generalized linear mixed models and linear mixed-effect models to perform prediction and correlation analysis, respectively. All combinations of eight risk factors were exhausted to identify the optimal predictor(s) with highest prediction accuracy defined as sum of sensitivity and specificity. When using a single risk factor, plaque wall stress (PWS) at baseline was the best predictor for plaque progression (PAI and WTI). The optimal predictor among all possible combinations for PAI was PWS + PWSn + Lipid percent + Min cap thickness + Plaque Area (PA) + Plaque Burden (PB) (prediction accuracy = 1.5928) while Wall Thickness (WT) + Plaque Wall Strain (PWSn) + Plaque Area (PA) was the best for WTI (1.2589). This indicated that PAI was a more predictable measure than WTI. The combination including both morphological and biomechanical parameters had improved prediction accuracy, compared to predictions using only morphological features.