IVUS-based FSI models for human coronary plaque progression study: components, correlation and predictive analysis

New insights on the association of weight loss with the reduction in carotid intima-media thickness among patients with obesity: an updated systematic review and meta-analysis

OBJECTIVES

Carotid intima-media thickness (CIMT) is a noninvasive marker of atherosclerosis, a typical pathologic process underlying cardiovascular diseases (CVDs). It is essential to explore the relationships between weight loss and the reduction of CIMT.

STUDY DESIGN

This was an updated systematic review and meta-analysis.

METHODS

A systematic literature search was conducted to collect relevant clinical trials. The pooled results of meta-analyses were assessed by weighted mean difference (WMD) and the corresponding 95 % confidence interval (95% CI).

RESULTS

Thirty-three articles involving 2273 participants were collected in this meta-analysis. Among all participants with obesity, the pooled mean of weight loss was -23.26 kg (95% CI: -27.71 to -18.81), and the pooled mean change of CIMT was -0.06 mm (95% CI: -0.08 to -0.04). Compared with Non-surgical interventions, Surgical ones could lead to much higher weight loss (Pbetween groups < 0.001). A more significant CIMT reduction was identified among Surgical intervention patients than among Non-surgical intervention participants (Pbetween groups < 0.001).

CONCLUSIONS

Effective interventions, especially Surgical interventions, could reduce the weight of patients with obesity, followed by the decline of CIMT, which might further disturb atherosclerosis progression and lower CVD risk.

3D reconstruction of coronary artery bifurcations from intravascular ultrasound and angiography

Coronary bifurcation lesions represent a challenging anatomical subset, and the understanding of their 3D anatomy and plaque composition appears to play a key role in devising the optimal stenting strategy. This study proposes a new approach for the 3D reconstruction of coronary bifurcations and plaque materials by combining intravascular ultrasound (IVUS) and angiography. Three patient-specific silicone bifurcation models were 3D reconstructed and compared to micro-computed tomography (µCT) as the gold standard to test the accuracy and reproducibility of the proposed methodology. The clinical feasibility of the method was investigated in three diseased patient-specific bifurcations of varying anatomical complexity. The IVUS-based 3D reconstructed bifurcation models showed high agreement with the µCT reference models, with r2 values ranging from 0.88 to 0.99. The methodology successfully 3D reconstructed all the patient bifurcations, including plaque materials, in less than 60 min. Our proposed method is a simple, time-efficient, and user-friendly tool for accurate 3D reconstruction of coronary artery bifurcations. It can provide valuable information about bifurcation anatomy and plaque burden in the clinical setting, assisting in bifurcation stent planning and education.

Automated Coronary Optical Coherence Tomography Feature Extraction with Application to Three-Dimensional Reconstruction

Coronary optical coherence tomography (OCT) is an intravascular, near-infrared light-based imaging modality capable of reaching axial resolutions of 10–20 µm. This resolution allows for accurate determination of high-risk plaque features, such as thin cap fibroatheroma; however, visualization of morphological features alone still provides unreliable positive predictive capability for plaque progression or future major adverse cardiovascular events (MACE). Biomechanical simulation could assist in this prediction, but this requires extracting morphological features from intravascular imaging to construct accurate three-dimensional (3D) simulations of patients’ arteries. Extracting these features is a laborious process, often carried out manually by trained experts. To address this challenge, numerous techniques have emerged to automate these processes while simultaneously overcoming difficulties associated with OCT imaging, such as its limited penetration depth. This systematic review summarizes advances in automated segmentation techniques from the past five years (2016–2021) with a focus on their application to the 3D reconstruction of vessels and their subsequent simulation. We discuss four categories based on the feature being processed, namely: coronary lumen; artery layers; plaque characteristics and subtypes; and stents. Areas for future innovation are also discussed as well as their potential for future translation.

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.

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.

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.

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.

MRI-based patient-specific human carotid atherosclerotic vessel material property variations in patients, vessel location and long-term follow up

Background

Image-based computational models are widely used to determine atherosclerotic plaque stress/strain conditions and investigate their association with plaque progression and rupture. However, patient-specific vessel material properties are in general lacking in those models, limiting the accuracy of their stress/strain measurements. A noninvasive approach of combining in vivo 3D multi-contrast and Cine magnetic resonance imaging (MRI) and computational modeling was introduced to quantify patient-specific carotid plaque material properties for potential plaque model improvements. Vessel material property variation in patients, along vessel segment, and between baseline and follow up were investigated.

Methods

In vivo 3D multi-contrast and Cine MRI carotid plaque data were acquired from 8 patients with follow-up (18 months) with written informed consent obtained. 3D thin-layer models and an established iterative procedure were used to determine parameter values of the Mooney-Rivlin models for the 81slices from 16 plaque samples. Effective Young’s Modulus (YM) values were calculated for comparison and analysis.

Results

Average Effective Young’s Modulus (YM) and circumferential shrinkage rate (C-Shrink) value of the 81 slices was 411kPa and 5.62%, respectively. Slice YM value varied from 70 kPa (softest) to 1284 kPa (stiffest), a 1734% difference. Average slice YM values by vessel varied from 109 kPa (softest) to 922 kPa (stiffest), a 746% difference. Location-wise, the maximum slice YM variation rate within a vessel was 311% (149 kPa vs. 613 kPa). The average slice YM variation rate for the 16 vessels was 134%. The average variation of YM values for all patients from baseline to follow up was 61.0%. The range of the variation of YM values was [-28.4%, 215%]. For plaque progression study, YM at follow-up showed negative correlation with plaque progression measured by wall thickness increase (WTI) (r = -0.7764, p = 0.0235). Wall thickness at baseline correlated with WTI negatively, with r = -0.5253 (p = 0.1813). Plaque burden at baseline correlated with YM change between baseline and follow-up, with r = 0.5939 (p = 0.1205).

Conclusion

In vivo carotid vessel material properties have large variations from patient to patient, along the diseased segment within a patient, and with time. The use of patient-specific, location specific and time-specific material properties in plaque models could potentially improve the accuracy of model stress/strain calculations.

Morphological and Stress Vulnerability Indices for Human Coronary Plaques and Their Correlations with Cap Thickness and Lipid Percent: An IVUS-Based Fluid-Structure Interaction Multi-patient Study

Plaque vulnerability, defined as the likelihood that a plaque would rupture, is difficult to quantify due to lack of in vivo plaque rupture data. Morphological and stress-based plaque vulnerability indices were introduced as alternatives to obtain quantitative vulnerability assessment. Correlations between these indices and key plaque features were investigated. In vivo intravascular ultrasound (IVUS) data were acquired from 14 patients and IVUS-based 3D fluid-structure interaction (FSI) coronary plaque models with cyclic bending were constructed to obtain plaque wall stress/strain and flow shear stress for analysis. For the 617 slices from the 14 patients, lipid percentage, min cap thickness, critical plaque wall stress (CPWS), strain (CPWSn) and flow shear stress (CFSS) were recorded, and cap index, lipid index and morphological index were assigned to each slice using methods consistent with American Heart Association (AHA) plaque classification schemes. A stress index was introduced based on CPWS. Linear Mixed-Effects (LME) models were used to analyze the correlations between the mechanical and morphological indices and key morphological factors associated with plaque rupture. Our results indicated that for all 617 slices, CPWS correlated with min cap thickness, cap index, morphological index with r = -0.6414, 0.7852, and 0.7411 respectively (p<0.0001). The correlation between CPWS and lipid percentage, lipid index were weaker (r = 0.2445, r = 0.2338, p<0.0001). Stress index correlated with cap index, lipid index, morphological index positively with r = 0.8185, 0.3067, and 0.7715, respectively, all with p<0.0001. For all 617 slices, the stress index has 66.77% agreement with morphological index. Morphological and stress indices may serve as quantitative plaque vulnerability assessment supported by their strong correlations with morphological features associated with plaque rupture. Differences between the two indices may lead to better plaque assessment schemes when both indices were jointly used with further validations from clinical studies.

Cardiovascular diseases are closely related to atherosclerotic plaque progression and rupture. Early detection of vulnerable plaques and prediction of potential plaque rupture and related clinical events are of vital importance. Plaque vulnerability, defined as the likelihood that a plaque would rupture, is difficult to measure due to lack of in vivo plaque rupture data. Morphological and stress-based plaque vulnerability indices were introduced in this paper as alternatives to obtain quantitative vulnerability assessment with potential improvement of patient screening tools. In vivo intravascular ultrasound data were acquired from patients and computational coronary plaque models were constructed to obtain data for analysis and index assignments. For the 617 slices from the 14 patients, morphological and stress indices were assigned to each slice using methods consistent with American Heart Association plaque classification schemes. Correlation analyses were performed for all the morphological and mechanical factors considered. The stress index has 66.77% agreement with morphological index. Morphological and stress indices may serve as quantitative plaque vulnerability assessment supported by their strong correlations with morphological features associated with plaque rupture. Differences between the two indices may lead to better plaque assessment schemes when the complementary indices were jointly used with further validations from clinical studies.

Influence of material property variability on the mechanical behaviour of carotid atherosclerotic plaques: a 3D fluid-structure interaction analysis

Mechanical analysis has been shown to be complementary to luminal stenosis in assessing atherosclerotic plaque vulnerability. However, patient-specific material properties are not available and the effect of material properties variability has not been fully quantified. Media and fibrous cap (FC) strips from carotid endarterectomy samples were classified into hard, intermediate and soft according to their incremental Young's modulus. Lipid and intraplaque haemorrhage/thrombus strips were classified as hard and soft. Idealised geometry-based 3D fluid-structure interaction analyses were performed to assess the impact of material property variability in predicting maximum principal stress (Stress-P1 ) and stretch (Stretch-P1 ). When FC was thick (1000 or 600 µm), Stress-P1 at the shoulder was insensitive to changes in material stiffness, whereas Stress-P1 at mid FC changed significantly. When FC was thin (200 or 65 µm), high stress concentrations shifted from the shoulder region to mid FC, and Stress-P1 became increasingly sensitive to changes in material properties, in particular at mid FC. Regardless of FC thickness, Stretch-P1 at these locations was sensitive to changes in material properties. Variability in tissue material properties influences both the location and overall stress/stretch value. This variability needs to be accounted for when interpreting the results of mechanical modelling.

Intravascular ultrasound observation of the mechanism of no-reflow phenomenon in acute myocardial infarction

Objective

To study the mechanism of the no-reflow phenomenon using coronary angiography (CAG) and intravascular ultrasound (IVUS).

Methods

A total of 120 patients with acute myocardial infarction (AMI) who successfully underwent indwelling intracoronary stent placement by percutaneous coronary intervention (PCI). All patients underwent pre- and post-PCI CAG and pre-IVUS. No-reflow was defined as post-PCI thrombolysis in myocardial infarction (TIMI) grade 0, 1, or 2 flow in the absence of mechanical obstruction. Normal reflow was defined as TIMI grade 3 flow. The pre-operation reference vascular area, minimal luminal cross-sectional area, plaque cross-sectional area, lesion length, plaque volume and plaque traits were measured by IVUS.

Results

The no-reflow group was observed in 14 cases (11.6%) and normal blood-flow group in 106 cases (89.4%) based on CAG results. There was no statistically significant difference in the patients’ medical history, reference vascular area (no-flow vs. normal-flow; 15.5 ± 3.2 vs. 16.2 ± 3.3, p> 0.05) and lesion length (21.9 ± 5.1 vs. 19.5 ± 4.8, p> 0.05) between the two groups. No-reflow patients had a longer symptom onset to reperfusion time compared to normal blood-flow group [(6.6 ± 3.1) h vs (4.3 ± 2.7) h; p< 0.05] and higher incidence of TIMI flow grade< 3 (71.4% vs 49.0%, p< 0.05). By IVUS examination, the no-reflow group had a significantly increased coronary plaque area and plaque volume compared to normal blood-flow group [(13.7 ± 3.0) mm² vs (10.2 ± 2.9) mm²; (285.4 ± 99.8) mm³ vs (189.7 ± 86.4) mm³; p< 0.01]. The presence of IVUS-detected soft plaque (57.1% vs. 24.0%, p< 0.01), eccentric plaque (64.2% vs. 33.7%, p< 0.05), plaque rupture (50.0% vs. 21.2%, p< 0.01), and thrombosis (42.8% vs. 15.3%) were significantly more common in no-reflow group.

Conclusion

There was no obvious relationship between the coronary risk factors and no-reflow phenomenon. The symptom onset to reperfusion time, TIMI flow grade before stent deployment, plaque area, soft plaques, eccentric plaques, plaque rupture and thrombosis may be risk factors for the no-reflow phenomenon after PCI.