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.

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.

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.

Very late vasomotor responses and gene expression with bioresorbable scaffolds and metallic drug-eluting stents.

OBJECTIVES: To investigate the long-term vasomotor response and inflammatory changes in Absorb bioresorbable vascular scaffold (BVS) and metallic drug-eluting stent (DES) implanted artery. BACKGROUND: Clinical evidence has demonstrated that compared to DES, BVS is associated with higher rates of target lesion failure. However, it is not known whether the higher event rates observed with BVS are related to endothelial dysfunction or inflammation associated with polymer degradation. METHODS: Ten Absorb BVS and six Xience V DES were randomly implanted in the main coronaries of six nonatherosclerotic swine. At 4-years, vasomotor response was evaluated in vivo by quantitative coronary angiography response to intracoronary infusion of Ach and ex vivo by the biomechanical response to prostaglandin F2-α (PGF2-α), substance P and bradykinin and gene expression analysis. RESULTS: Absorb BVS implanted arteries showed significantly restored vasoconstrictive responses after Ach compared to in-stent Xience V. The contractility of Absorb BVS treated segments induced by PGF2-α was significantly greater compared to Xience V treated segments and endothelial-dependent vasorelaxation was greater with Absorb BVS compared to Xience V. Gene expression analyses indicated the pro-inflammatory lymphotoxin-beta receptor (LTßR) signaling pathway was significantly upregulated in arteries treated with a metallic stent compared to Absorb BVS treated arterial segments. CONCLUSIONS: At 4 years, arteries treated with Absorb BVS compared with Xience V, demonstrate significantly greater restoration of vasomotor responses. Genetic analysis suggests mechanobiologic reparation of Absorb BVS treated arteries at 4 years as opposed to Xience V treated vessels.

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.

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.

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 optical coherence tomography and intravascular ultrasound imaging to quantify coronary plaque cap thickness and vulnerability: a pilot study.

BACKGROUND: Detecting coronary vulnerable plaques in vivo and assessing their vulnerability have been great challenges for clinicians and the research community. Intravascular ultrasound (IVUS) is commonly used in clinical practice for diagnosis and treatment decisions. However, due to IVUS limited resolution (about 150-200 µm), it is not sufficient to detect vulnerable plaques with a threshold cap thickness of 65 µm. Optical Coherence Tomography (OCT) has a resolution of 15-20 µm and can measure fibrous cap thickness more accurately. The aim of this study was to use OCT as the benchmark to obtain patient-specific coronary plaque cap thickness and evaluate the differences between OCT and IVUS fibrous cap quantifications. A cap index with integer values 0-4 was also introduced as a quantitative measure of plaque vulnerability to study plaque vulnerability. METHODS: Data from 10 patients (mean age: 70.4; m: 6; f: 4) with coronary heart disease who underwent IVUS, OCT, and angiography were collected at Cardiovascular Research Foundation (CRF) using approved protocol with informed consent obtained. 348 slices with lipid core and fibrous caps were selected for study. Convolutional Neural Network (CNN)-based and expert-based data segmentation were performed using established methods previously published. Cap thickness data were extracted to quantify differences between IVUS and OCT measurements. RESULTS: For the 348 slices analyzed, the mean value difference between OCT and IVUS cap thickness measurements was 1.83% (p = 0.031). However, mean value of point-to-point differences was 35.76%. Comparing minimum cap thickness for each plaque, the mean value of the 20 plaque IVUS-OCT differences was 44.46%, ranging from 2.36% to 91.15%. For cap index values assigned to the 348 slices, the disagreement between OCT and IVUS assignments was 25%. However, for the OCT cap index = 2 and 3 groups, the disagreement rates were 91% and 80%, respectively. Furthermore, the observation of cap index changes from baseline to follow-up indicated that IVUS results differed from OCT by 80%. CONCLUSIONS: These preliminary results demonstrated that there were significant differences between IVUS and OCT plaque cap thickness measurements. Large-scale patient studies are needed to confirm our findings.

A Multimodality Image-Based Fluid-Structure Interaction Modeling Approach for Prediction of Coronary Plaque Progression Using IVUS and Optical Coherence Tomography 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 three-dimensional (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 fluid-structure interaction (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 µm, 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 (LS-SVM) 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 nine prediction studies.

Multi-factor decision-making strategy for better coronary plaque burden increase prediction: a patient-specific 3D FSI study using IVUS follow-up data.

Plaque progression and vulnerability are influenced by many risk factors. Our goal is to find a simple method to combine multiple risk factors for better plaque development prediction. Intravascular ultrasound data at baseline and follow-up were acquired from nine patients, and fluid-structure interaction models were constructed to obtain plaque wall stress/strain (PWS/PWSn) and wall shear stress (WSS). Two hundred fifty-four slices with noticeable change in plaque burden were selected for analyses. Data of six key morphological and biomechanical factors were extracted from each slice at baseline to predict plaque development measured by plaque burden increase (PBI) from baseline to follow-up. A multi-factor decision-making strategy was proposed to assign a binary predictive outcome YW (W represents any combination of these six factors) based on simple "threshold value" idea to predict the ground truth YPBI: YPBI = 1 if PBI > 0; YPBI = 0 otherwise. A fivefold cross-validation procedure was employed to identify the optimal predictor among all possible combinations. The results showed that PWS was the best single-factor predictor for PBI with a prediction accuracy of 63.0%. Among all 63 combinations, combining lipid percent, PWS and WSS gave the optimal predictor, achieving a prediction accuracy of 68.1%. This demonstrated that compared to single factor alone, integrating morphological and biomechanical factors would lead to higher prediction accuracy. The simple method could be extended to combine factors from different sources to improve prediction accuracy. Efforts in mechanical analysis and modeling automation are needed to bring this strategy closer to potential clinical applications.

High Coronary Shear Stress in Patients With Coronary Artery Disease Predicts Myocardial Infarction.

BACKGROUND: Coronary lesions with low fractional flow reserve (FFR) that are treated medically are associated with higher revascularization rates. High wall shear stress (WSS) has been linked with increased plaque vulnerability. OBJECTIVES: This study investigated the prognostic value of WSS measured in the proximal segments of lesions (WSSprox) to predict myocardial infarction (MI) in patients with stable coronary artery disease (CAD) and hemodynamically significant lesions. The authors hypothesized that in patients with low FFR and stable CAD, higher WSSprox would predict MI. METHODS: Among 441 patients in the FAME II (Fractional Flow Reserve Versus Angiography for Multivessel Evaluation II) trial with FFR ≤0.80 who were randomized to medical therapy alone, 34 (8%) had subsequent MI within 3 years. Patients with vessel-related MI and adequate angiograms for 3-dimensional reconstruction (n = 29) were propensity matched to a control group with no MI (n = 29) by using demographic and clinical variables. Coronary lesions were divided into proximal, middle, and distal, along with 5-mm upstream and downstream segments. WSS was calculated for each segment. RESULTS: Median age was 62 years, and 46 (79%) were male. In the marginal Cox model, whereas lower FFR showed a trend (hazard ratio: 0.084; p = 0.064), higher WSSprox (hazard ratio: 1.234; p = 0.002, C-index = 0.65) predicted MI. Adding WSSprox to FFR resulted in a significant increase in global chi-square for predicting MI (p = 0.045), a net reclassification improvement of 0.69 (p = 0.005), and an integrated discrimination index of 0.11 (p = 0.010). CONCLUSIONS: In patients with stable CAD and hemodynamically significant lesions, higher WSS in the proximal segments of atherosclerotic lesions is predictive of MI and has incremental prognostic value over FFR.

Low Coronary Wall Shear Stress Is Associated With Severe Endothelial Dysfunction in Patients With Nonobstructive Coronary Artery Disease.

OBJECTIVES: This study investigated the relationship between low wall shear stress (WSS) and severe endothelial dysfunction (EDFx). BACKGROUND: Local hemodynamic forces such as WSS play an important role in atherogenesis through their effect on endothelial cells. The study hypothesized that low WSS independently predicts severe EDFx in patients with coronary artery disease (CAD). METHODS: Forty-four patients with CAD underwent coronary angiography, fractional flow reserve, and endothelial function testing. Segments with >10% vasoconstriction after acetylcholine (Ach) infusion were defined as having severe EDFx. WSS, calculated using 3-dimensional angiography, velocity measurements, and computational fluid dynamics, was defined as low (<1 Pa), intermediate (1 to 2.5 Pa), or high (>2.5 Pa). RESULTS: Median age was 52 years, 73% were women. Mean fractional flow reserve was 0.94 ± 0.06. In 4,510 coronary segments, median WSS was 3.67 Pa. A total of 24% had severe EDFx. A higher proportion of segments with low WSS had severe EDFx (71%) compared with intermediate WSS (22%) or high WSS (23%) (p < 0.001). Segments with low WSS demonstrated greater vasoconstriction in response to Ach than did intermediate or high WSS segments (-10.7% vs. -2.5% vs. +1.3%, respectively; p < 0.001). In a multivariable logistic regression analysis, female sex (odds ratio [OR]: 2.44; p = 0.04), diabetes (OR: 5.01; p = 0.007), and low WSS (OR: 9.14; p < 0.001) were independent predictors of severe EDFx. CONCLUSIONS: In patients with nonobstructive CAD, segments with low WSS demonstrated more vasoconstriction in response to Ach than did intermediate or high WSS segments. Low WSS was independently associated with severe EDFx.

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.

Oscillatory wall shear stress is a dominant flow characteristic affecting lesion progression patterns and plaque vulnerability in patients with coronary artery disease.

Although experimental studies suggest that low and oscillatory wall shear stress (WSS) promotes plaque transformation to a more vulnerable phenotype, this relationship has not been examined in human atherosclerosis progression. Thus, the aim of this investigation was to examine the association between oscillatory WSS, in combination with WSS magnitude, and coronary atherosclerosis progression. We hypothesized that regions of low and oscillatory WSS will demonstrate progression towards more vulnerable lesions, while regions exposed to low and non-oscillatory WSS will exhibit progression towards more stable lesions. Patients (n = 20) with non-flow-limiting coronary artery disease (CAD) underwent baseline and six-month follow-up angiography, Doppler velocity and radiofrequency intravascular ultrasound (VH-IVUS) acquisition. Computational fluid dynamics models were constructed to compute time-averaged WSS magnitude and oscillatory WSS. Changes in VH-IVUS-defined total plaque and constituent areas were quantified in focal regions (i.e. sectors; n = 14 235) and compared across haemodynamic categories. Compared with sectors exposed to low WSS magnitude, high WSS sectors demonstrated regression of total plaque area (p < 0.001) and fibrous tissue (p < 0.001), and similar progression of necrotic core. Sectors subjected to low and oscillatory WSS exhibited total plaque area regression, while low and non-oscillatory WSS sectors demonstrated total plaque progression (p < 0.001). Furthermore, compared with low and non-oscillatory WSS areas, sectors exposed to low and oscillatory WSS demonstrated regression of fibrous (p < 0.001) and fibrofatty (p < 0.001) tissue and similar progression of necrotic core (p = 0.82) and dense calcium (p = 0.40). Herein, we demonstrate that, in patients with non-obstructive CAD, sectors subjected to low and oscillatory WSS demonstrated regression of total plaque, fibrous and fibrofatty tissue, and progression of necrotic core and dense calcium, which suggest a transformation to a more vulnerable phenotype.

High wall shear stress and high-risk plaque: an emerging concept.

In recent years, there has been a significant effort to identify high-risk plaques in vivo prior to acute events. While number of imaging modalities have been developed to identify morphologic characteristics of high-risk plaques, prospective natural-history observational studies suggest that vulnerability is not solely dependent on plaque morphology and likely involves additional contributing mechanisms. High wall shear stress (WSS) has recently been proposed as one possible causative factor, promoting the development of high-risk plaques. High WSS has been shown to induce specific changes in endothelial cell behavior, exacerbating inflammation and stimulating progression of the atherosclerotic lipid core. In line with experimental and autopsy studies, several human studies have shown associations between high WSS and known morphological features of high-risk plaques. However, despite increasing evidence, there is still no longitudinal data linking high WSS to clinical events. As the interplay between atherosclerotic plaque, artery, and WSS is highly dynamic, large natural history studies of atherosclerosis that include WSS measurements are now warranted. This review will summarize the available clinical evidence on high WSS as a possible etiological mechanism underlying high-risk plaque development.

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.

Computational models have been used to calculate plaque stress and strain for plaque progression and rupture investigations. An intravascular ultrasound (IVUS)-based modeling approach is proposed to quantify in vivo vessel material properties for more accurate stress/strain calculations. In vivo Cine IVUS and VH-IVUS coronary plaque data were acquired from one patient with informed consent obtained. Cine IVUS data and 3D thin-slice models with axial stretch were used to determine patient-specific vessel material properties. Twenty full 3D fluid-structure interaction models with ex vivo and in vivo material properties and various axial and circumferential shrink combinations were constructed to investigate the material stiffness impact on stress/strain calculations. The approximate circumferential Young's modulus over stretch ratio interval [1.0, 1.1] for an ex vivo human plaque sample and two slices (S6 and S18) from our IVUS data were 1631, 641, and 346 kPa, respectively. Average lumen stress/strain values from models using ex vivo, S6 and S18 materials with 5 % axial shrink and proper circumferential shrink were 72.76, 81.37, 101.84 kPa and 0.0668, 0.1046, and 0.1489, respectively. The average cap strain values from S18 material models were 150-180 % higher than those from the ex vivo material models. The corresponding percentages for the average cap stress values were 50-75 %. Dropping axial and circumferential shrink consideration led to stress and strain over-estimations. In vivo vessel material properties may be considerably softer than those from ex vivo data. Material stiffness variations may cause 50-75 % stress and 150-180 % strain variations.

Quantification of the focal progression of coronary atherosclerosis through automated co-registration of virtual histology-intravascular ultrasound imaging data.

The goal of this study was to evaluate the accuracy of a novel algorithm that circumferentially co-registers serial virtual histology-intravascular ultrasound (VH-IVUS) data for the focal assessment of coronary atherosclerosis progression. Thirty-three patients with an abnormal non-invasive cardiac stress test or stable angina underwent baseline and follow-up (6 or 12 months) invasive evaluation that included acquisition of VH-IVUS image data. Baseline and follow-up image pairs (n = 4194) were automatically co-registered in the circumferential direction via a multi-variate cross-correlation algorithm. Algorithm stability and accuracy were assessed by comparing results from multiple iterations of the algorithm (iteration 1 vs. iteration 2) and against values determined manually by two expert VH-IVUS readers (algorithm vs. two expert readers). Furthermore, focal plaque progression values were compared between the algorithm and expert readers following co-registration by the independently determined angles. Strong agreement in circumferential co-registration angles were observed across multiple iterations of the algorithm (stability) and between the algorithm and expert readers (accuracy; all concordance correlation coefficients >0.98). Furthermore, circumferential co-registration angles determined by the algorithm were not statistically when compared to values determined by two expert readers (p = 0. 99). Bland-Altman analysis indicated minimal bias when comparing focal VH-IVUS defined plaque progression in corresponding sectors following circumferential co-registration between the algorithm and expert readers. Finally, average differences in changes in total plaque and constituent areas between the algorithm and readers were within the average range of difference between readers (interobserver variability). We present a stable and validated algorithm to automatically circumferentially co-register serial VH-IVUS imaging data for the focal quantification of coronary atherosclerosis progression.

Evaluation of a framework for the co-registration of intravascular ultrasound and optical coherence tomography coronary artery pullbacks.

A growing number of studies have used a combination of intravascular ultrasound (IVUS) and optical coherence tomography (OCT) for the assessment of atherosclerotic plaques. Given their respective strengths these imaging modalities highly complement each other. Correlations of hemodynamics and coronary artery disease (CAD) have been extensively investigated with both modalities separately, though not concurrently due to challenges in image registration. Manual co-registration of these modalities is a time expensive task subject to human error, and the development of an automatic method has not been previously addressed. We developed a framework that uses dynamic time warping for the longitudinal co-registration and dynamic programming for the circumferential co-registration of images and evaluated the methodology in a cohort (n = 12) of patients with moderate CAD. Excellent correlation was seen between the algorithm and two expert readers for longitudinal co-registration (CCC = 0.9964, CCC = 0.9959) and circumferential co-registration (CCC = 0.9688, CCC = 0.9598). The mean error of the circumferential co-registration angle was found to be within 10%. A framework for the co-registration of IVUS and OCT pullbacks has been developed which provides a foundation for comprehensive studies of CAD biomechanics.