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

Impact of residual stress on coronary plaque stress/strain calculations using optical coherence tomography image-based multi-layer models

Introduction

Mechanical stress and strain conditions play an important role in atherosclerosis plaque progression, remodeling and potential rupture and may be used in plaque vulnerability assessment for better clinical diagnosis and treatment decisions. Single layer plaque models without residual stress have been widely used due to unavailability of multi-layer image segmentation method and residual stress data. However, vessel layered structure and residual stress have large impact on stress/strain calculations and should be included in the models.

Methods

In this study, intravascular optical coherence tomography (OCT) data of coronary plaques from 10 patients were acquired and segmented to obtain the three-layer vessel structure using an in-house automatic segmentation algorithm. Multi- and single-layer 3D thin-slice biomechanical plaque models with and without residual stress were constructed to assess the impact of residual stress on stress/strain calculations.

Results

Our results showed that residual stress led to a more uniform stress distribution across the vessel wall, with considerable plaque stress/strain decrease on inner wall and increase on vessel out-wall. Multi-layer model with residual stress inclusion reduced inner wall maximum and mean plaque stresses by 38.57% and 59.70%, and increased out-wall maximum and mean plaque stresses by 572.84% and 432.03%.

Conclusion

These findings demonstrated the importance of multi-layer modeling with residual stress for more accurate plaque stress/strain calculations, which will have great impact in plaque cap stress calculation and plaque rupture risk assessment. Further large-scale studies are needed to validate our findings.

Comparison of multilayer and single-layer coronary plaque models on stress/strain calculations based on optical coherence tomography images

Mechanical stress and strain conditions are closely related to atherosclerotic plaque progression and rupture and have been under intensive investigations in recent years. It is well known that arteries have a three-layer structure: intima, media and adventitia. However, in vivo image-based multilayer plaque models are not available in the current literature due to lack of multilayer image segmentation data. A multilayer segmentation and repairing technique was introduced to segment coronary plaque optical coherence tomography (OCT) image to obtain its three-layer vessel structure. A total of 200 OCT slices from 20 patients (13 male; 7 female) were used to construct multilayer and single-layer 3D thin-slice models to calculate plaque stress and strain and compare model differences. Our results indicated that the average maximum plaque stress values of 20 patients from multilayer and single-layer models were 385.13 ± 110.09 kPa and 270.91 ± 95.86 kPa, respectively. The relative difference was 42.2%, with single-layer stress serving as the base value. The average mean plaque stress values from multilayer and single-layer models were 129.59 ± 32.77 kPa and 93.27 ± 18.20 kPa, respectively, with a relative difference of 38.9%. The maximum and mean plaque strain values obtained from the multilayer models were 11.6% and 19.0% higher than those from the single-layer models. Similarly, the maximum and mean cap strains showed increases of 9.6% and 12.9% over those from the single-layer models. These findings suggest that use of multilayer models could improve plaque stress and strain calculation accuracy and may have large impact on plaque progression and vulnerability investigation and potential clinical applications. Further large-scale studies are needed to validate our findings.

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.

Human Coronary Plaque Optical Coherence Tomography Image Repairing, Multilayer Segmentation and Impact on Plaque Stress/Strain Calculations

Coronary vessel layer structure may have a considerable impact on plaque stress/strain calculations. Most current plaque models use single-layer vessel structures due to the lack of available multilayer segmentation techniques. In this paper, an automatic multilayer segmentation and repair method was developed to segment coronary optical coherence tomography (OCT) images to obtain multilayer vessel geometries for biomechanical model construction. Intravascular OCT data were acquired from six patients (one male; mean age: 70.0) using a protocol approved by the local institutional review board with informed consent obtained. A total of 436 OCT slices were selected in this study. Manually segmented data were used as the gold standard for method development and validation. The edge detection method and cubic spline surface fitting were applied to detect and repair the internal elastic membrane (IEM), external elastic membrane (EEM) and adventitia-periadventitia interface (ADV). The mean errors of automatic contours compared to manually segmented contours were 1.40%, 4.34% and 6.97%, respectively. The single-layer mean plaque stress value from lumen was 117.91 kPa, 10.79% lower than that from three-layer models (132.33 kPa). On the adventitia, the single-layer mean plaque stress value was 50.46 kPa, 156.28% higher than that from three-layer models (19.74 kPa). The proposed segmentation technique may have wide applications in vulnerable plaque research.

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.

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.

The Differentiation in Image Post-processing and 3D Reconstruction During Evaluation of Carotid Plaques From MR and CT Data Sources

Background: Carotid plaque morphology and tissue composition help assess risk stratification of stroke events. Many post-processing image techniques based on CT and MR images have been widely used in related research, such as image segmentation, 3D reconstruction, and computer fluid dynamics. However, the criteria for the 3D numerical model of carotid plaque established by CT and MR angiographic image data remain open to questioning.

Method: We accurately duplicated the geometry and simulated it using computer software to make a 3D numerical model. The initial images were obtained by CTA and TOF-MRA. MIMICS (Materialize’s interactive medical image control system) software was used to process the images to generate three-dimensional solid models of blood vessels and plaques. The subsequent output was exported to the ANSYS software to generate finite element simulation results for the further hemodynamic study.

Results: The 3D models of carotid plaque of TOF-MRA and CTA were simulated by using computer software. CTA has a high-density resolution for carotid plaque, the boundary of the CTA image is obvious, and the main component of which is a calcified tissue. However, the density resolution of TOF-MRA for the carotid plaque and carotid artery was not as good as that of CTA. The results show that there is a large deviation between the TOF-MRA and CTA 3D model of plaque in the carotid artery due to the unclear recognition of plaque boundary during 3D reconstruction, and this can further affect the simulation results of hemodynamics.

Conclusion: In this study, two-dimensional images and three-dimensional models of carotid plaques obtained by two angiographic techniques were compared. The potential of these two imaging methods in clinical diagnosis and fluid dynamics of carotid plaque was evaluated, and the selectivity of image post-processing analysis to original medical image acquisition was revealed.

An investigation into the critical role of fibre orientation in the ultimate tensile strength and stiffness of human carotid plaque caps

The development and subsequent rupture of atherosclerotic plaques in human carotid arteries is a major cause of ischaemic stroke. Mechanical characterization of atherosclerotic plaques can aid our understanding of this rupture risk. Despite this however, experimental studies on human atherosclerotic carotid plaques, and fibrous plaque caps in particular, are very limited. This study aims to provide further insights into atherosclerotic plaque rupture by mechanically testing human fibrous plaque caps, the region of the atherosclerotic lesion most often attributed the highest risk of rupture. The results obtained highlight the variability in the ultimate tensile stress, strain and stiffness experienced in atherosclerotic plaque caps. By pre-screening all samples using small angle light scattering (SALS) to determine the dominant fibre direction in the tissue, along with supporting histological analysis, this work suggests that the collagen fibre alignment in the circumferential direction plays the most dominant role for determining plaque structural stability. The work presented in this study could provide the basis for new diagnostic approaches to be developed, which non-invasively identify carotid plaques at greatest risk of rupture. STATEMENT OF SIGNIFICANCE: Mechanical characterisation of the atherosclerotic plaque cap is of utmost importance for understanding the mechanisms that govern the rupture strength of this tissue in-vivo. Studies has shown that plaque tissue is heterogenous and comprises of many structural components, each of which exhibits a varying mechanical response. However, rupture generally is located to the plaque cap, whereby the stress exerted on this location exceeds its mechanical strength causing failure. This work shows, for the first time, that the underlying collagen fibre architecture of carotid plaque caps governs their strength and stiffness. This study shows that plaque caps with collagen fibres aligned in the predominately circumferential direction experience higher stresses and lower strains before failure while those with predominately axial fibres display the opposite trend. Furthermore, total collagen content was found not to play a dominant role in determining the mechanical response of the tissue. The present study provides critical insights into human atherosclerotic plaque tissue mechanics and offers clinically relevant insights for mechanically sensitive imaging techniques, such as strain-based ultrasound or MRI.