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Coronary artery disease is defined by the existence of atherosclerotic plaque on the arterial wall, which can cause blood flow impairment, or plaque rupture, and ultimately lead to myocardial ischemia. Intravascular ultrasound (IVUS) imaging can provide a detailed characterization of lumen and vessel features, and so plaque burden, in coronary vessels. Prediction of the regions in a vascular segment where plaque burden can either increase (progression) or decrease (regression) following a certain therapy, has remained an elusive major milestone in cardiology. Studies like IBIS-4 showed an association between plaque burden regression and high-intensity rosuvastatin therapy over 13 months. Nevertheless, it has not been possible to predict if a patient would respond in a favorable/adverse fashion to such a treatment. This work aims to (i) Develop a framework that processes lumen and vessel cross-sectional contours and extracts geometric descriptors from baseline and follow-up IVUS pullbacks; and to (ii) Develop, train, and validate a machine learning model based on baseline/follow-up IVUS datasets that predicts future percent of atheroma volume changes in coronary vascular segments using only baseline information, i.e. geometric features and clinical data. This is a post hoc analysis, revisiting the IBIS-4 study. We employed 140 arteries, from 81 patients, for which expert delineation of lumen and vessel contours were available at baseline and 13-month follow-up. Contour data from baseline and follow-up pullbacks were co-registered and then processed to extract several frame-wise features, e.g. areas, plaque burden, eccentricity, etc. Each pullback was divided into regions of interest (ROIs), following different criteria. Frame-wise features were condensed into region-wise markers using tools from statistics, signal processing, and information theory. Finally, a stratified 5-fold cross-validation strategy (20 repetitions) was used to train/validate an XGBoost regression models. A feature selection method before the model training was also applied. When the models were trained/validated on ROI defined by the difference between follow-up and baseline plaque burden, the average accuracy and Mathews correlation coefficient were 0.70 and 0.41 respectively. Using a ROI partition criterion based only on the baseline's plaque burden resulted in averages of 0.60 accuracy and 0.23 Mathews correlation coefficient. An XGBoost model was capable of predicting plaque progression/regression changes in coronary vascular segments of patients treated with rosuvastatin therapy in 13 months. The proposed method, first of its kind, successfully managed to address the problem of stratification of patients at risk of coronary plaque progression, using IVUS images and standard patient clinical data.

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BACKGROUND: Isolate features of the coronary anatomy have been associated with the pathophysiology of atherosclerotic disease. Computational methods have been described to allow precise quantification of the complex three-dimensional (3D) coronary geometry. The present study tested whether quantitative parameters that describe the spatial 3D coronary geometry is associated with the extension and composition of the underlying coronary artery disease (CAD). METHODS: Patients with CAD scheduled for percutaneous intervention were investigated with coronary computed tomography angiography (CCTA), and invasive coronary angiography, and virtual histology intravascular ultrasound (IVUS-VH). For all target vessels, 3D centerlines were extracted from CCTA images and processed to quantify 23 geometric indexes, grouped into 3 main categories as follows: (i) length-based; (ii) curvature-based, torsion-based, and curvature/torsion-combined; (iii) vessel path-based. The geometric variables were compared with IVUS-VH parameters assessing the extent and composition of coronary atherosclerosis. RESULTS: A total of 36 coronary patients (99 vessels) comprised the study population. From the 23 geometric indexes, 18 parameters were significantly (p < 0.05) associated with at least 1 IVUS-VH parameter at a univariate analysis. All three main geometric categories provided parameters significantly related with atherosclerosis variables. The 3D geometric indexes were associated with the degree of atherosclerotic extension, as well as with plaque composition. Geometric features remained significantly associated with all IVUS-VH parameters even after multivariate adjustment for clinical characteristics. CONCLUSIONS: Quantitative 3D vessel morphology emerges as a relevant factor associated with atherosclerosis in patients with established CAD.

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Introduction: Pulmonary fibrosis is a destructive, progressive disease that dramatically reduces life quality of patients, ultimately leading to death. Therapeutic regimens for pulmonary fibrosis have shown limited benefits, hence justifying the efforts to evaluate the outcome of alternative treatments. Methods: Using a mouse model of bleomycin (BLM)-induced lung fibrosis, in the current work we asked whether treatment with pro-resolution molecules, such as pro-resolving lipid mediators (SPMs) could ameliorate pulmonary fibrosis. To this end, we injected aspirin-triggered resolvin D1 (7S,8R,17R-trihydroxy-4Z,9E,11E,13Z,15E19Z-docosahexaenoic acid; ATRvD1; i.v.) 7 and 10 days after BLM (intratracheal) challenge and samples were two weeks later. Results and discussion: Assessment of outcome in the lung tissues revealed that ATRvD1 partially restored lung architecture, reduced leukocyte infiltration, and inhibited formation of interstitial edema. In addition, lung tissues from BLM-induced mice treated with ATRvD1 displayed reduced levels of TNF-α, MCP-1, IL-1-ß, and TGF-ß. Of further interest, ATRvD1 decreased lung tissue expression of MMP-9, without affecting TIMP-1. Highlighting the beneficial effects of ATRvD1, we found reduced deposition of collagen and fibronectin in the lung tissues. Congruent with the anti-fibrotic effects that ATRvD1 exerted in lung tissues, α-SMA expression was decreased, suggesting that myofibroblast differentiation was inhibited by ATRvD1. Turning to culture systems, we next showed that ATRvD1 impaired TGF-ß-induced fibroblast differentiation into myofibroblast. After showing that ATRvD1 hampered extracellular vesicles (EVs) release in the supernatants from TGF-ß-stimulated cultures of mouse macrophages, we verified that ATRvD1 also inhibited the release of EVs in the bronco-alveolar lavage (BAL) fluid of BLM-induced mice. Motivated by studies showing that BLM-induced lung fibrosis is linked to angiogenesis, we asked whether ATRvD1 could blunt BLM-induced angiogenesis in the hamster cheek pouch model (HCP). Indeed, our intravital microscopy studies confirmed that ATRvD1 abrogates BLM-induced angiogenesis. Collectively, our findings suggest that treatment of pulmonary fibrosis patients with ATRvD1 deserves to be explored as a therapeutic option in the clinical setting.

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The geometry of coronary arteries is believed to play the role as an atherosclerotic risk factor on its own. The full characterization of the normal coronary network has been reported in the literature. Reports on the integration of geometry and functional data for normal coronary vessels started to proliferate more recently. In this work, we analyze and integrate the geometric data retrieved from angiography images of the left main coronary bifurcation in angiographically normal patients and hemodynamic data generated from blood flow models to analyze the role of allometric laws and the connection between flow distribution and wall shear stress loads on the left anterior descending and left circumflex arteries. This in-silico study contributes to the characterization of normal coronary anatomy and its impact on the hemodynamic shear stresses acting over the vessel wall, shedding light on the impact of geometry-based versus simulation-based hypotheses to define boundary conditions for numerical simulations. We discuss the role of the wall shear stress corresponding to scenarios adopted by the scientific community and the ones proposed in this study. For the simulation-based hypothesis, we propose an iterative strategy to define boundary conditions at the main left coronary bifurcation, such that wall shear stresses are matched between the left descending and left circumflex arteries. From this study, we conclude that a one-fits-all power law exponent of 7/3 results in an good trade-off between computational cost and wall shear stress balance between daughter vessels.

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In this work, we present a novel modeling framework to investigate the effects of collateral circulation into the coronary blood flow physiology. A prototypical model of the coronary tree, integrated with the concept of Collateral Flow Index (CFI), is employed to gain insight about the role of model parameters associated with the collateral circuitry, which results in physically-realizable solutions for specific CFI data. Then, we discuss the mathematical feasibility of pressure-derived CFI, anatomical implications and practical considerations involving the estimation of model parameters in collateral connections. A sensitivity analysis is carried out, and the investigation of the impact of the collateral circulation on FFR values is also addressed.

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The characterization of vascular geometry is a fundamental step towards the correct interpretation of coronary artery disease. In this work, we report a comprehensive comparison of the geometry featured by coronary vessels as obtained from coronary computed tomography angiography (CCTA) and the combination of intravascular ultrasound (IVUS) with bi-plane angiography (AX) modalities. We analyzed 34 vessels from 28 patients with coronary disease, which were deferred to CCTA and IVUS procedures. We discuss agreement and discrepancies between several geometric indexes extracted from vascular geometries. Such an analysis allows us to understand to which extent the coronary vascular geometry can be reliable in the interpretation of geometric risk factors, and as a surrogate to characterize coronary artery disease.

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Aims: Assessment of minimum lumen areas in intravascular ultrasound (IVUS) pullbacks is time-consuming and demands adequately trained personnel. In this work, we introduce a novel and fully automated pipeline to segment the lumen boundary in IVUS datasets. Methods and results: First, an automated gating is applied to select end-diastolic frames and bypass saw-tooth artefacts. Second, within a machine learning (ML) environment, we automatically segment the lumen boundary using a multi-frame (MF) convolutional neural network (MFCNN). Finally, we use the theory of Gaussian processes (GPs) to regress the final lumen boundary. The dataset consisted of 85 IVUS pullbacks (52 patients). The dataset was partitioned at the pullback-level using 73 pullbacks for training (20 586 frames), 6 pullbacks for validation (1692 frames), and 6 for testing (1692 frames). The degree of overlapping, between the ground truth and ML contours, median (interquartile range, IQR) systematically increased from 0.896 (0.874-0.933) for MF1 to 0.925 (0.911-0.948) for MF11. The median (IQR) of the distance error was also reduced from 3.83 (2.94-4.98)% for MF1 to 3.02 (2.25-3.95)% for MF11-GP. The corresponding median (IQR) in the lumen area error remained between 5.49 (2.50-10.50)% for MF1 and 5.12 (2.15-9.00)% for MF11-GP. The dispersion in the relative distance and area errors consistently decreased as we increased the number of frames, and also when the GP regressor was coupled to the MFCNN output. Conclusion: These results demonstrate that the proposed ML approach is suitable to effectively segment the lumen boundary in IVUS scans, reducing the burden of costly and time-consuming manual delineation.

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OBJECTIVES: To evaluate the diagnostic performance of a novel computational algorithm based on three-dimensional intravascular ultrasound (IVUS) imaging in estimating fractional flow reserve (IVUSFR ), compared to gold-standard invasive measurements (FFRINVAS ). BACKGROUND: IVUS provides accurate anatomical evaluation of the lumen and vessel wall and has been validated as a useful tool to guide percutaneous coronary intervention. However, IVUS poorly represents the functional status (i.e., flow-related information) of the imaged vessel. METHODS: Patients with known or suspected stable coronary disease scheduled for elective cardiac catheterization underwent FFRINVAS measurement and IVUS imaging in the same procedure to evaluate intermediate lesions. A processing methodology was applied on IVUS to generate a computational mesh condensing the geometric characteristics of the vessel. Computation of IVUSFR was obtained from patient-level morphological definition of arterial districts and from territory-specific boundary conditions. FFRINVAS measurements were dichotomized at the 0.80 threshold to define hemodynamically significant lesions. RESULTS: A total of 24 patients with 34 vessels were analyzed. IVUSFR significantly correlated (r = 0.79; P < 0.001) and showed good agreement with FFRINVAS , with a mean difference of -0.008 ± 0.067 (P = 0.47). IVUSFR presented an overall accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of 91%, 89%, 92%, 80%, and 96%, respectively, to detect significant stenosis. CONCLUSION: The computational processing of IVUSFR is a new method that allows the evaluation of the functional significance of coronary stenosis in an accurate way, enriching the anatomical information of grayscale IVUS.

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