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 Ruptures Are Related to High Plaque Stress and Strain Conditions: Direct Verification by Using In Vivo OCT Rupture Data and FSI Models

BACKGROUND

While it has been hypothesized that high plaque stress and strain may be related to plaque rupture, its direct verification using in vivo coronary plaque rupture data and full 3-dimensional fluid-structure interaction models is lacking in the current literature due to difficulty in obtaining in vivo plaque rupture imaging data from patients with acute coronary syndrome. This case-control study aims to use high-resolution optical coherence tomography-verified in vivo plaque rupture data and 3-dimensional fluid-structure interaction models to seek direct evidence for the high plaque stress/strain hypothesis.

METHODS

In vivo coronary plaque optical coherence tomography data (5 ruptured plaques, 5 no-rupture plaques) were acquired from patients using a protocol approved by the local institutional review board with informed consent obtained. The ruptured caps were reconstructed to their prerupture morphology using neighboring plaque cap and vessel geometries. Optical coherence tomography-based 3-dimensional fluid-structure interaction models were constructed to obtain plaque stress, strain, and flow shear stress data for comparative analysis. The rank-sum test in the nonparametric test was used for statistical analysis.

RESULTS

Our results showed that the average maximum cap stress and strain values of ruptured plaques were 142% (457.70 versus 189.22 kPa; P=0.0278) and 48% (0.2267 versus 0.1527 kPa; P=0.0476) higher than that for no-rupture plaques, respectively. The mean values of maximum flow shear stresses for ruptured and no-rupture plaques were 145.02 dyn/cm2 and 81.92 dyn/cm2 (P=0.1111), respectively. However, the flow shear stress difference was not statistically significant.

CONCLUSIONS

This preliminary case-control study showed that the ruptured plaque group had higher mean maximum stress and strain values. Due to our small study size, larger scale studies are needed to further validate our findings.

Considering the Influence of Coronary Motion on Artery-Specific Biomechanics Using Fluid-Structure Interaction Simulation

The endothelium in the coronary arteries is subject to wall shear stress and vessel wall strain, which influences the biology of the arterial wall. This study presents vessel-specific fluid-structure interaction (FSI) models of three coronary arteries, using directly measured experimental geometries and boundary conditions. FSI models are used to provide a more physiologically complete representation of vessel biomechanics, and have been extended to include coronary bending to investigate its effect on shear and strain. FSI both without- and with-bending resulted in significant changes in all computed shear stress metrics compared to CFD (p = 0.0001). Inclusion of bending within the FSI model produced highly significant changes in Time Averaged Wall Shear Stress (TAWSS) + 9.8% LAD, + 8.8% LCx, - 2.0% RCA; Oscillatory Shear Index (OSI) + 208% LAD, 0% LCx, + 2600% RCA; and transverse wall Shear Stress (tSS) + 180% LAD, + 150% LCx and + 200% RCA (all p < 0.0001). Vessel wall strain was homogenous in all directions without-bending but became highly anisotropic under bending. Changes in median cyclic strain magnitude were seen for all three vessels in every direction. Changes shown in the magnitude and distribution of shear stress and wall strain suggest that bending should be considered on a vessel-specific basis in analyses of coronary artery biomechanics.

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.

In Vivo Intravascular Optical Coherence Tomography (IVOCT) Structural and Blood Flow Imaging Based Mechanical Simulation Analysis of a Blood Vessel

INTRODUCTION

Computer modelling of blood vessels based on biomedical imaging provides important hemodynamic and biomechanical information for vascular disease studies and diagnosis. However due to lacking well-defined physiological blood flow profiles, the accuracy of the simulation results is often not guaranteed. Flow velocity profiles of a specific cross section of a blood vessel were obtained by in vivo Doppler intravascular optical coherence tomography (IVOCT) lately. However due to the influence of the catheter, the velocity profile imaged by IVOCT can't be applied to simulation directly.

METHODS

A simulation-experiment combined method to determine the inlet flow boundary based on in vivo porcine carotid Doppler IVOCT imaging is proposed. A single conduit carotid model was created from the 3D IVOCT structural images using an image processing-computer aided design combined method.

RESULTS

With both high- resolution arterial model and near physiological blood flow profile, stress analysis by fluid-structure interaction and computational fluid dynamics were performed. The influence of the catheter to the wall shear stress, the hemodynamics and the stresses of the carotid wall under the measured inlet flow and various outlet pressure boundary conditions, are analyzed.

CONCLUSION

This study provides a solution to the difficulty of getting inlet flow boundary for numerical simulation of arteries. It paves the way for developing IVOCT based vascular stress analysis and imaging methods for the studies and diagnosis of vascular diseases.

Image-based biomechanical modeling for coronary atherosclerotic plaque progression and vulnerability prediction

Atherosclerotic plaque progression and rupture play an important role in cardiovascular disease development and the final drastic events such as heart attack and stroke. Medical imaging and image-based computational modeling methods advanced considerably in recent years to quantify plaque morphology and biomechanical conditions and gain a better understanding of plaque evolution and rupture process. This article first briefly reviewed clinical imaging techniques for coronary thin-cap fibroatheroma (TCFA) plaques used in image-based computational modeling. This was followed by a summary of different types of biomechanical models for coronary plaques. Plaque progression and vulnerability prediction studies based on image-based computational modeling were reviewed and compared. Much progress has been made and a reasonable high prediction accuracy has been achieved. However, there are still some inconsistencies in existing literature on the impact of biomechanical and morphological factors on future plaque behavior, and it is very difficult to perform direct comparison analysis as differences like image modality, biomechanical factors selection, predictive models, and progression/vulnerability measures exist among these studies. Encouraging data and model sharing across the research community would partially resolve these differences, and possibly lead to clearer assertive conclusions. In vivo image-based computational modeling could be used as a powerful tool for quantitative assessment of coronary plaque vulnerability for potential clinical applications.

Using Optical Coherence Tomography and Intravascular Ultrasound Imaging to Quantify Coronary Plaque Cap Stress/Strain and Progression: A Follow-Up Study Using 3D Thin-Layer Models

Accurate plaque cap thickness quantification and cap stress/strain calculations are of fundamental importance for vulnerable plaque research. To overcome uncertainties due to intravascular ultrasound (IVUS) resolution limitation, IVUS and optical coherence tomography (OCT) coronary plaque image data were combined together to obtain accurate and reliable cap thickness data, stress/strain calculations, and reliable plaque progression predictions. IVUS, OCT, and angiography baseline and follow-up data were collected from nine patients (mean age: 69; m: 5) at Cardiovascular Research Foundation with informed consent obtained. IVUS and OCT slices were coregistered and merged to form IVUS + OCT (IO) slices. A total of 114 matched slices (IVUS and OCT, baseline and follow-up) were obtained, and 3D thin-layer models were constructed to obtain stress and strain values. A generalized linear mixed model (GLMM) and least squares support vector machine (LSSVM) method were used to predict cap thickness change using nine morphological and mechanical risk factors. Prediction accuracies by all combinations (511) of those predictors with both IVUS and IO data were compared to identify optimal predictor(s) with their best accuracies. For the nine patients, the average of minimum cap thickness from IVUS was 0.17 mm, which was 26.08% lower than that from IO data (average = 0.23 mm). Patient variations of the individual errors ranged from ‒58.11 to 20.37%. For maximum cap stress between IO and IVUS, patient variations of the individual errors ranged from ‒30.40 to 46.17%. Patient variations of the individual errors of maximum cap strain values ranged from ‒19.90 to 17.65%. For the GLMM method, the optimal combination predictor using IO data had AUC (area under the ROC curve) = 0.926 and highest accuracy = 90.8%, vs. AUC = 0.783 and accuracy = 74.6% using IVUS data. For the LSSVM method, the best combination predictor using IO data had AUC = 0.838 and accuracy = 75.7%, vs. AUC = 0.780 and accuracy = 69.6% using IVUS data. This preliminary study demonstrated improved plaque cap progression prediction accuracy using accurate cap thickness data from IO slices and the differences in cap thickness, stress/strain values, and prediction results between IVUS and IO data. Large-scale studies are needed to verify our findings.

In Vivo Based Fluid-Structure Interaction Biomechanics of the Left Anterior Descending Coronary Artery

A fluid-structure interaction-based biomechanical model of the entire left anterior descending coronary artery is developed from in vivo imaging via the finite element method in this paper. Included in this investigation is ventricle contraction, three-dimensional motion, all angiographically visible side branches, hyper/viscoelastic artery layers, non-Newtonian and pulsatile blood flow, and the out-of-phase nature of blood velocity and pressure. The fluid-structure interaction model is based on in vivo angiography of an elite athlete's entire left anterior descending coronary artery where the influence of including all alternating side branches and the dynamical contraction of the ventricle is investigated for the first time. Results show the omission of side branches result in a 350% increase in peak wall shear stress and a 54% decrease in von Mises stress. Peak von Mises stress is underestimated by up to 80% when excluding ventricle contraction and further alterations in oscillatory shear indices are seen, which provide an indication of flow reversal and has been linked to atherosclerosis localization. Animations of key results are also provided within a video abstract. We anticipate that this model and results can be used as a basis for our understanding of the interaction between coronary and myocardium biomechanics. It is hoped that further investigations could include the passive and active components of the myocardium to further replicate in vivo mechanics and lead to an understanding of the influence of cardiac abnormalities, such as arrythmia, on coronary biomechanical responses.

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.

Patient-Specific CT-Based Fluid-Structure-Interaction Aorta Model to Quantify Mechanical Conditions for the Investigation of Ascending Aortic Dilation in TOF Patients

Background

Some adult patients with Tetralogy of Fallot (TOF) were found to simultaneously develop ascending aortic dilation. Severe aortic dilation would lead to several aortic diseases, including aortic aneurysm and dissection, which seriously affect patients' living quality and even cause patients' death. Current practice guidelines of aortic-dilation-related diseases mainly focus on aortic diameter, which has been found not always a good indicator. Therefore, it may be clinically useful to identify some other factors that can potentially better predict aortic response to dilation.

Methods

20 TOF patients scheduled for TOF repair surgery were recruited in this study and were divided into dilated and nondilated groups according to the Z scores of ascending aorta diameters. Patient-specific aortic CT images, pressure, and flow rates were used in the construction of computational biomechanical models.

Results

Simulation results demonstrated a good coincidence between numerical mean flow rate at inlet and the one obtained from color Doppler ultrasonography, which implied that computational models were able to simulate the movement of the aorta and blood inside accurately. Our results indicated that aortic stress can effectively differentiate patients of the dilated group from the ones of the nondilated group. Mean ascending aortic stress-P1 (maximal principal stress) from the dilated group was 54% higher than that from the nondilated group (97.97 kPa vs. 63.47 kPa, p value = 0.044) under systolic pressure. Velocity magnitude in the aorta and aortic wall displacement of the dilated group were also greater than those of the nondilated group with p value < 0.1.

Conclusion

Computational modeling and ascending aortic biomechanical factors may be used as a potential tool to identify and analyze aortic response to dilation. Large-scale clinical studies are needed to validate these preliminary findings.

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%).

Combining morphological and biomechanical factors for optimal carotid plaque progression prediction: An MRI-based follow-up study using 3D thin-layer models

Plaque progression prediction is of fundamental significance to cardiovascular research and disease diagnosis, prevention, and treatment. Magnetic resonance image (MRI) data of carotid atherosclerotic plaques were acquired from 20 patients with consent obtained. 3D thin-layer models were constructed to calculate plaque stress and strain. Data for ten morphological and biomechanical risk factors were extracted for analysis. Wall thickness increase (WTI), plaque burden increase (PBI) and plaque area increase (PAI) were chosen as three measures for plaque progression. Generalized linear mixed models (GLMM) with 5-fold cross-validation strategy were used to calculate prediction accuracy and identify optimal predictor. The optimal predictor for PBI was the combination of lumen area (LA), plaque area (PA), lipid percent (LP), wall thickness (WT), maximum plaque wall stress (MPWS) and maximum plaque wall strain (MPWSn) with prediction accuracy = 1.4146 (area under the receiver operating characteristic curve (AUC) value is 0.7158), while PA, plaque burden (PB), WT, LP, minimum cap thickness, MPWS and MPWSn was the best for WTI (accuracy = 1.3140, AUC = 0.6552), and a combination of PA, PB, WT, MPWS, MPWSn and average plaque wall strain (APWSn) was the best for PAI with prediction accuracy = 1.3025 (AUC = 0.6657). The combinational predictors improved prediction accuracy by 9.95%, 4.01% and 1.96% over the best single predictors for PAI, PBI and WTI (AUC values improved by 9.78%, 9.45%, and 2.14%), respectively. This suggests that combining both morphological and biomechanical risk factors could lead to better patient screening strategies.

Moderate thickness of lipid core in shoulder region of atherosclerotic plaque determines vulnerable plaque A parametric study

Peak stress in the fibrous cap of atherosclerotic plaque is largely determined by the cap thickness which cannot be accurately estimated in vivo. This parametric study investigates idealized atherosclerotic plaque geometries. Finite element modeling is applied to search for larger morphological features associated with high cap stresses. By varying seven geometrical and two loading parameters, 100 3D model geometries of atherosclerotic plaques in common iliac artery were generated. In each model peak cap stress was calculated, and statistical comparison of the geometries generating the highest and lowest peak cap stresses was performed. The analysis showed that, compared to geometries generating the lowest stresses, those with high peak cap stress had a significantly lower cap thickness, higher stenosis ratio, lower relative lipid core volume, and cap shoulder radius larger than lipid core radius. High cap stress was observed for cap thicknesses up to 0.13 mm. It can be concluded that vulnerable plaques contain thin fibrous cap, large stenosis ratio and only moderate small-radius lipid core which reaches the shoulder region of the fibrous cap.

A Multi-Modality Image-Based FSI Modeling Approach for Prediction of Coronary Plaque Progression Using IVUS and OCT Data with Follow-Up

Medical image resolution has been a serious limitation in plaque progression research. A modeling approach combining intravascular ultrasound (IVUS) and optical coherence tomography (OCT) was introduced and patient follow-up IVUS and OCT data were acquired to construct 3D coronary models for plaque progression investigations. Baseline and follow-up in vivo IVUS and OCT coronary plaque data were acquired from one patient with 105 matched slices selected for model construction. 3D FSI models based on IVUS and OCT data (denoted as IVUS+OCT model) were constructed to obtain stress/strain and wall shear stress (WSS) for plaque progression prediction. IVUS-based IVUS50 and IVUS200 models were constructed for comparison with cap thickness set as 50 and 200 microns, respectively. Lumen area increase (LAI), plaque area increase (PAI) and plaque burden increase (PBI) were chosen to measure plaque progression. The least squares support vector machine method was employed for plaque progression prediction using 19 risk factors. For IVUS+OCT model with LAI, PAI and PBI, the best single predictor was plaque strain, local plaque stress, and minimal cap thickness, with prediction accuracy as 0.766, 0.838 and 0.890, respectively; The prediction accuracy using best combinations of 19 factors was 0.911, 0.881 and 0.905, respectively. Compared to IVUS+OCT model, IVUS50 and IVUS200 models had errors ranging from 1% to 66.5% in quantifying cap thickness, stress, strain and prediction accuracies. WSS showed relatively lower prediction accuracy compared to other predictors in all 9 prediction studies.