Quantification of thrombus formation in malapposed coronary stents deployed in vitro through imaging analysis.

Stent thrombosis is a major complication of coronary stent and scaffold intervention. While often unanticipated and lethal, its incidence is low making mechanistic examination difficult through clinical investigation alone. Thus, throughout the technological advancement of these devices, experimental models have been indispensable in furthering our understanding of device safety and efficacy. As we refine model systems to gain deeper insight into adverse events, it is equally important that we continue to refine our measurement methods. We used digital signal processing in an established flow loop model to investigate local flow effects due to geometric stent features and ultimately its relationship to thrombus formation. A new metric of clot distribution on each microCT slice termed normalized clot ratio was defined to quantify this distribution. Three under expanded coronary bare-metal stents were run in a flow loop model to induce clotting. Samples were then scanned in a MicroCT machine and digital signal processing methods applied to analyze geometric stent conformation and spatial clot formation. Results indicated that geometric stent features play a significant role in clotting patterns, specifically at a frequency of 0.6225 Hz corresponding to a geometric distance of 1.606 mm. The magnitude-squared coherence between geometric features and clot distribution was greater than 0.4 in all samples. In stents with poor wall apposition, ranging from 0.27 mm to 0.64 mm maximum malapposition (model of real-world heterogeneity), clots were found to have formed in between stent struts rather than directly adjacent to struts. This early work shows how the combination of tools in the areas of image processing and signal analysis can advance the resolution at which we are able to define thrombotic mechanisms in in vitro models, and ultimately, gain further insight into clinical performance.

Vessel centerline reconstruction from non-isocentric and non-orthogonal paired monoplane angiographic images.

PURPOSE: Three-dimensional reconstruction of a vessel centerline from paired planar coronary angiographic images is critical to reconstruct the complex three-dimensional structure of the coronary artery lumen and the relative positioning of implanted devices. In this study, a new vessel centerline reconstruction method that can utilize non-isocentric and non-orthogonal pairs of angiographic images was developed and tested. METHODS: Our new method was developed in in vitro phantom models of bifurcated coronary artery with and without stent, and then tested in in vivo swine models (twelve coronary arteries). This method was also validated using data from six patients. RESULTS: Our new method demonstrated high accuracy (root mean square error = 0.27 mm or 0.76 pixel), and high reproducibility across a broad imaging angle (20°-130°) and between different cardiac cycles in vitro and in vivo. Use of this method demonstrated that the vessel centerline in the stented segment did not deform significantly over a cardiac cycle in vivo. In addition, the total movement of the isocenter in each image could be accurately estimated in vitro and in vivo. The performance of this new method for patient data was similar to that for in vitro phantom models and in vivo animal models. CONCLUSIONS: We developed a vessel centerline reconstruction method for non-isocentric and non-orthogonal angiographic images. It demonstrated high accuracy and good reproducibility in vitro, in vivo, and in clinical setting, suggesting that our new method is clinically applicable despite the small sample size of clinical data.

Pulsatility and high shear stress deteriorate barrier phenotype in brain microvascular endothelium.

Microvascular endothelial cells at the blood-brain barrier exhibit a protective phenotype, which is highly induced by biochemical and biomechanical stimuli. Amongst them, shear stress enhances junctional tightness and limits transport at capillary-like levels. Abnormal flow patterns can reduce functional features of macrovascular endothelium. We now examine if this is true in brain microvascular endothelial cells. We suggest in this paper a complex response of endothelial cells to aberrant forces under different flow domains. Human brain microvascular endothelial cells were exposed to physiological or abnormal flow patterns. Physiologic shear (10-20 dyn/cm2) upregulates expression of tight junction markers Zona Occludens 1 (1.7-fold) and Claudin-5 (more than 2-fold). High shear stress (40 dyn/cm2) and/or pulsatility decreased their expression to basal levels and altered junctional morphology. We exposed cells to pathological shear stress patterns followed by capillary-like conditions. Results showed reversible recovery on the expression of tight junction markers. Flow protection of barrier phenotype commensurate with junctional signaling pathways decrease (Src, 0.25-fold, ERK, 0.77-fold) when compared to static conditions. This decrease was lost under high shear and pulsatile flow. In conclusion, abnormal shear stress inherent to systemic vascular disease leads to barrier impairment, which could be reverted by hemodynamic interventions.

Constraining OCT with Knowledge of Device Design Enables High Accuracy Hemodynamic Assessment of Endovascular Implants.

BACKGROUND: Stacking cross-sectional intravascular images permits three-dimensional rendering of endovascular implants, yet introduces between-frame uncertainties that limit characterization of device placement and the hemodynamic microenvironment. In a porcine coronary stent model, we demonstrate enhanced OCT reconstruction with preservation of between-frame features through fusion with angiography and a priori knowledge of stent design. METHODS AND RESULTS: Strut positions were extracted from sequential OCT frames. Reconstruction with standard interpolation generated discontinuous stent structures. By computationally constraining interpolation to known stent skeletons fitted to 3D 'clouds' of OCT-Angio-derived struts, implant anatomy was resolved, accurately rendering features from implant diameter and curvature (n = 1 vessels, r2 = 0.91, 0.90, respectively) to individual strut-wall configurations (average displacement error ~15 µm). This framework facilitated hemodynamic simulation (n = 1 vessel), showing the critical importance of accurate anatomic rendering in characterizing both quantitative and basic qualitative flow patterns. Discontinuities with standard approaches systematically introduced noise and bias, poorly capturing regional flow effects. In contrast, the enhanced method preserved multi-scale (local strut to regional stent) flow interactions, demonstrating the impact of regional contexts in defining the hemodynamic consequence of local deployment errors. CONCLUSION: Fusion of planar angiography and knowledge of device design permits enhanced OCT image analysis of in situ tissue-device interactions. Given emerging interests in simulation-derived hemodynamic assessment as surrogate measures of biological risk, such fused modalities offer a new window into patient-specific implant environments.

Vascular Response to Experimental Stent Malapposition and Under-Expansion.

Up to 80% of all endovascular stents have malapposed struts, and while some impose catastrophic events others are inconsequential. Thirteen stents were implanted in coronary arteries of seven healthy Yorkshire pigs, using specially-designed cuffed balloons inducing controlled stent malapposition and under-expansion. Optical coherence tomography (OCT) imaging confirmed that 25% of struts were malapposed (strut-wall distance

The Impact of Blood Rheology on Drug Transport in Stented Arteries: Steady Simulations.

BACKGROUND AND METHODS: It is important to ensure that blood flow is modelled accurately in numerical studies of arteries featuring drug-eluting stents due to the significant proportion of drug transport from the stent into the arterial wall which is flow-mediated. Modelling blood is complicated, however, by variations in blood rheological behaviour between individuals, blood's complex near-wall behaviour, and the large number of rheological models which have been proposed. In this study, a series of steady-state computational fluid dynamics analyses were performed in which the traditional Newtonian model was compared against a range of non-Newtonian models. The impact of these rheological models was elucidated through comparisons of haemodynamic flow details and drug transport behaviour at various blood flow rates. RESULTS: Recirculation lengths were found to reduce by as much as 24% with the inclusion of a non-Newtonian rheological model. Another model possessing the viscosity and density of blood plasma was also implemented to account for near-wall red blood cell losses and yielded recirculation length increases of up to 59%. However, the deviation from the average drug concentration in the tissue obtained with the Newtonian model was observed to be less than 5% in all cases except one. Despite the small sensitivity to the effects of viscosity variations, the spatial distribution of drug matter in the tissue was found to be significantly affected by rheological model selection. CONCLUSIONS/SIGNIFICANCE: These results may be used to guide blood rheological model selection in future numerical studies. The clinical significance of these results is that they convey that the magnitude of drug uptake in stent-based drug delivery is relatively insensitive to individual variations in blood rheology. Furthermore, the finding that flow separation regions formed downstream of the stent struts diminish drug uptake may be of interest to device designers.

Enhancing physiologic simulations using supervised learning on coarse mesh solutions.

Computational modelling of physical and biochemical processes has emerged as a means of evaluating medical devices, offering new insights that explain current performance, inform future designs and even enable personalized use. Yet resource limitations force one to compromise with reduced order computational models and idealized assumptions that yield either qualitative descriptions or approximate, quantitative solutions to problems of interest. Considering endovascular drug delivery as an exemplary scenario, we used a supervised machine learning framework to process data generated from low fidelity coarse meshes and predict high fidelity solutions on refined mesh configurations. We considered two models simulating drug delivery to the arterial wall: (i) two-dimensional drug-coated balloons and (ii) three-dimensional drug-eluting stents. Simulations were performed on computational mesh configurations of increasing density. Supervised learners based on Gaussian process modelling were constructed from combinations of coarse mesh setting solutions of drug concentrations and nearest neighbourhood distance information as inputs, and higher fidelity mesh solutions as outputs. These learners were then used as computationally inexpensive surrogates to extend predictions using low fidelity information to higher levels of mesh refinement. The cross-validated, supervised learner-based predictions improved fidelity as compared with computational simulations performed at coarse level meshes--a result consistent across all outputs and computational models considered. Supervised learning on coarse mesh solutions can augment traditional physics-based modelling of complex physiologic phenomena. By obtaining efficient solutions at a fraction of the computational cost, this framework has the potential to transform how modelling approaches can be applied in the evaluation of medical technologies and their real-time administration in an increasingly personalized fashion.

Impact of flow pulsatility on arterial drug distribution in stent-based therapy.

Drug-eluting stents reside in a dynamic fluid environment where the extent to which drugs are distributed within the arterial wall is critically modulated by the blood flowing through the arterial lumen. Yet several factors associated with the pulsatile nature of blood flow and their impact on arterial drug deposition have not been fully investigated. We employed an integrated framework comprising bench-top and computational models to explore the factors governing the time-varying fluid dynamic environment within the vasculature and their effects on arterial drug distribution patterns. A custom-designed bench-top framework comprising a model of a single drug-eluting stent strut and a poly-vinyl alcohol-based hydrogel as a model tissue bed simulated fluid flow and drug transport under fully apposed strut settings. Bench-top experiments revealed a relative independence between drug distribution and the factors governing pulsatile flow and these findings were validated with the in silico model. Interestingly, computational models simulating suboptimal deployment settings revealed a complex interplay between arterial drug distribution, Womersley number and the extent of malapposition. In particular, for a stent strut offset from the wall, total drug deposition was sensitive to changes in the pulsatile flow environment, with this dependence increasing with greater wall displacement. Our results indicate that factors governing pulsatile luminal flow on arterial drug deposition should be carefully considered in conjunction with device deployment settings for better utilization of drug-eluting stent therapy.

Analysis of drug distribution from a simulated drug-eluting stent strut using an in vitro framework.

The mechanisms of delivery of anti-proliferative drug from a drug-eluting stent are defined by transport forces in the coating, the lumen, and the arterial wall. Dynamic asymmetries in the localized flow about stent struts have previously been shown to contribute to significant heterogeneity in the spatial distribution of drug in in silico three-compartmental models of stent based drug delivery. A novel bench-top experiment has been created to confirm this phenomena. The experiment simulates drug release from a single stent strut, and then allows visualization of drug uptake into both lumen and tissue domains using optical techniques. Results confirm the existence of inhomogeneous and asymmetric arterial drug distributions, with this distribution shown to be sensitive to the flow field surrounding the strut.

Development of an in vitro method for modeling drug release and subsequent tissue drug uptake and deposition in a pulsatile flow network.

A novel benchtop model of drug elution and arterial drug deposition following stent implantation has been developed. The model contains a single drug loaded strut and a compartment simulating the vessel wall, housed in a flow chamber under a pulsatile flow regime. Each component has programmable transport properties that can be implemented into a computational model of drug elution. An initial experiment determining the effects of luminal flow on drug deposition patterns was performed. The results show that spatial distribution of drug correlates with areas of low and recirculating flow surrounding the strut. This spatial distribution of drug was shown to be dependent on both transient release behavior and the local flow field surrounding the strut. Furthermore, these results showed that the novel method could be used to study the effects of luminal flow in the presence of single or multiple struts. The method could also be used to explore more complex drug release strategies.