Multimodality Imaging-Based Characterization of Regional Material Properties in a Murine Model of Aortic Dissection

Central Artery Hemodynamics in Angiotensin II-Induced Hypertension and Effects of Anesthesia

Systemic hypertension is a strong risk factor for cardiovascular, neurovascular, and renovascular diseases. Central artery stiffness is both an initiator and indicator of hypertension, thus revealing a critical relationship between the wall mechanics and hemodynamics. Mice have emerged as a critical animal model for studying effects of hypertension and much has been learned. Regardless of the specific mouse model, data on changes in cardiac function and hemodynamics are necessarily measured under anesthesia. Here, we present a new experimental-computational workflow to estimate awake cardiovascular conditions from anesthetized data, which was then used to quantify effects of chronic angiotensin II-induced hypertension relative to normotension in wild-type mice. We found that isoflurane anesthesia had a greater impact on depressing hemodynamics in angiotensin II-infused mice than in controls, which led to unexpected results when comparing anesthetized results between the two groups of mice. Through comparison of the awake simulations, however, in vivo relevant effects of angiotensin II-infusion on global and regional vascular structure, properties, and hemodynamics were found to be qualitatively consistent with expectations. Specifically, we found an increased in vivo vascular stiffness in the descending thoracic aorta and suprarenal abdominal aorta, leading to increases in pulse pressure in the distal aorta. These insights allow characterization of the impact of regionally varying vascular remodeling on hemodynamics and mouse-to-mouse variations due to induced hypertension.

MicroBundleCompute: Automated segmentation, tracking, and analysis of subdomain deformation in cardiac microbundles

Advancing human induced pluripotent stem cell derived cardiomyocyte (hiPSC-CM) technology will lead to significant progress ranging from disease modeling, to drug discovery, to regenerative tissue engineering. Yet, alongside these potential opportunities comes a critical challenge: attaining mature hiPSC-CM tissues. At present, there are multiple techniques to promote maturity of hiPSC-CMs including physical platforms and cell culture protocols. However, when it comes to making quantitative comparisons of functional behavior, there are limited options for reliably and reproducibly computing functional metrics that are suitable for direct cross-system comparison. In addition, the current standard functional metrics obtained from time-lapse images of cardiac microbundle contraction reported in the field (i.e., post forces, average tissue stress) do not take full advantage of the available information present in these data (i.e., full-field tissue displacements and strains). Thus, we present "MicroBundleCompute," a computational framework for automatic quantification of morphology-based mechanical metrics from movies of cardiac microbundles. Briefly, this computational framework offers tools for automatic tissue segmentation, tracking, and analysis of brightfield and phase contrast movies of beating cardiac microbundles. It is straightforward to implement, runs without user intervention, requires minimal input parameter setting selection, and is computationally inexpensive. In this paper, we describe the methods underlying this computational framework, show the results of our extensive validation studies, and demonstrate the utility of exploring heterogeneous tissue deformations and strains as functional metrics. With this manuscript, we disseminate "MicroBundleCompute" as an open-source computational tool with the aim of making automated quantitative analysis of beating cardiac microbundles more accessible to the community.

The impact of thickness heterogeneity on soft tissue biomechanics: a novel measurement technique and a demonstration on heart valve tissue

The mechanical properties of soft tissues are driven by their complex, heterogeneous composition and structure. Interestingly, studies of soft tissue biomechanics often ignore spatial heterogeneity. In our work, we are therefore interested in exploring the impact of tissue heterogeneity on the mechanical properties of soft tissues. Therein, we specifically focus on soft tissue heterogeneity arising from spatially varying thickness. To this end, our first goal is to develop a non-destructive measurement technique that has a high spatial resolution, provides continuous thickness maps, and is fast. Our secondary goal is to demonstrate that including spatial variation in thickness is important to the accuracy of biomechanical analyses. To this end, we use mitral valve leaflet tissue as our model system. To attain our first goal, we identify a soft tissue-specific contrast protocol that enables thickness measurements using a Keyence profilometer. We also show that this protocol does not affect our tissues' mechanical properties. To attain our second goal, we conduct virtual biaxial, bending, and buckling tests on our model tissue both ignoring and considering spatial variation in thickness. Thereby, we show that the assumption of average, homogeneous thickness distributions significantly alters the results of biomechanical analyses when compared to including true, spatially varying thickness distributions. In conclusion, our work provides a novel measurement technique that can capture continuous thickness maps non-invasively, at high resolution, and in a short time. Our work also demonstrates the importance of including heterogeneous thickness in biomechanical analyses of soft tissues.

Evolving Mural Defects, Dilatation, and Biomechanical Dysfunction in Angiotensin II-Induced Thoracic Aortopathies

BACKGROUND

Thoracic aortopathy associates with extracellular matrix remodeling and altered biomechanical properties. We sought to quantify the natural history of thoracic aortopathy in a common mouse model and to correlate measures of wall remodeling such as aortic dilatation or localized mural defects with evolving microstructural composition and biomechanical properties of the wall.

METHODS

We combined a high-resolution multimodality imaging approach (panoramic digital image correlation and optical coherence tomography) with histopathologic examinations and biaxial mechanical testing to correlate spatially, for the first time, macroscopic mural defects and medial degeneration within the ascending aorta with local changes in aortic wall composition and mechanical properties.

RESULTS

Findings revealed strong correlations between local decreases in elastic energy storage and increases in circumferential material stiffness with increasing proximal aortic diameter and especially mural defect size. Mural defects tended to exhibit a pronounced biomechanical dysfunction that is driven by an altered organization of collagen and elastic fibers.

CONCLUSIONS

While aneurysmal dilatation is often observed within particular segments of the aorta, dissection and rupture initiate as highly localized mechanical failures. We show that wall composition and material properties are compromised in regions of local mural defects, which further increases the dilatation and overall structural vulnerability of the wall. Identification of therapies focused on promoting robust collagen accumulation may protect the wall from these vulnerabilities and limit the incidence of dissection and rupture.

Imaging Techniques for Aortic Aneurysms and Dissections in Mice: Comparisons of Ex Vivo, In Situ, and Ultrasound Approaches

Aortic aneurysms and dissections are life-threatening conditions that have a high risk for lethal bleeding and organ malperfusion. Many studies have investigated the molecular basis of these diseases using mouse models. In mice, ex vivo, in situ, and ultrasound imaging are major approaches to evaluate aortic diameters, a common parameter to determine the severity of aortic aneurysms. However, accurate evaluations of aortic dimensions by these imaging approaches could be challenging due to pathological features of aortic aneurysms. Currently, there is no standardized mode to assess aortic dissections in mice. It is important to understand the characteristics of each approach for reliable evaluation of aortic dilatations. In this review, we summarize imaging techniques used for aortic visualization in recent mouse studies and discuss their pros and cons. We also provide suggestions to facilitate the visualization of mouse aortas.

Simulating progressive intramural damage leading to aortic dissection using DeepONet: an operator-regression neural network

Aortic dissection progresses mainly via delamination of the medial layer of the wall. Notwithstanding the complexity of this process, insight has been gleaned by studying in vitro and in silico the progression of dissection driven by quasi-static pressurization of the intramural space by fluid injection, which demonstrates that the differential propensity of dissection along the aorta can be affected by spatial distributions of structurally significant interlamellar struts that connect adjacent elastic lamellae. In particular, diverse histological microstructures may lead to differential mechanical behaviour during dissection, including the pressure-volume relationship of the injected fluid and the displacement field between adjacent lamellae. In this study, we develop a data-driven surrogate model of the delamination process for differential strut distributions using DeepONet, a new operator-regression neural network. This surrogate model is trained to predict the pressure-volume curve of the injected fluid and the damage progression within the wall given a spatial distribution of struts, with in silico data generated using a phase-field finite-element model. The results show that DeepONet can provide accurate predictions for diverse strut distributions, indicating that this composite branch-trunk neural network can effectively extract the underlying functional relationship between distinctive microstructures and their mechanical properties. More broadly, DeepONet can facilitate surrogate model-based analyses to quantify biological variability, improve inverse design and predict mechanical properties based on multi-modality experimental data.

Uncertainty quantification in subject-specific estimation of local vessel mechanical properties

Quantitative estimation of local mechanical properties remains critically important in the ongoing effort to elucidate how blood vessels establish, maintain, or lose mechanical homeostasis. Recent advances based on panoramic digital image correlation (pDIC) have made high-fidelity 3D reconstructions of small-animal (e.g., murine) vessels possible when imaged in a variety of quasi-statically loaded configurations. While we have previously developed and validated inverse modeling approaches to translate pDIC-measured surface deformations into biomechanical metrics of interest, our workflow did not heretofore include a methodology to quantify uncertainties associated with local point estimates of mechanical properties. This limitation has compromised our ability to infer biomechanical properties on a subject-specific basis, such as whether stiffness differs significantly between multiple material locations on the same vessel or whether stiffness differs significantly between multiple vessels at a corresponding material location. In the present study, we have integrated a novel uncertainty quantification and propagation pipeline within our inverse modeling approach, relying on empirical and analytic Bayesian techniques. To demonstrate the approach, we present illustrative results for the ascending thoracic aorta from three mouse models, quantifying uncertainties in constitutive model parameters as well as circumferential and axial tangent stiffness. Our extended workflow not only allows parameter uncertainties to be systematically reported, but also facilitates both subject-specific and group-level statistical analyses of the mechanics of the vessel wall.

Multi-view digital image correlation systems for in vitro testing of arteries from mice to humans

BACKGROUND

Digital image correlation (DIC) methods are increasingly used for non-contact optical assessment of geometry and deformation in soft tissue biomechanics, thus providing the full-field strain estimates needed for robust inverse material characterization. Despite the well-known flexibility and ease of use of DIC, issues related to spatial resolution and depth-of-field remain challenging in studies of quasi-cylindrical biological samples such as arteries.

OBJECTIVE

After demonstrating that standard surrounding multi-view DIC systems are inappropriate for such usage, we submit that both the optical setup and the data analysis need to be specifically designed with respect to the size of the arterial sample of interest. Accordingly, we propose novel and optimized DIC systems for two distinct ranges of arterial diameters: less than 2.5 mm (murine arteries) and greater than 10 mm (human arteries).

METHODS

We designed, set up, and validated a four-camera panoramic-DIC system for testing murine arteries and a multi-biprism DIC system for testing human arteries. Both systems enable dynamic 360-deg measurements with refraction correction over the entire surface of submerged samples in their native geometries.

RESULTS

Illustrative results for 3D shape and full-surface deformation fields were obtained for a mouse infrarenal aorta and a latex cylinder of size similar to the human infrarenal aorta.

CONCLUSION

Results demonstrated the feasibility and accuracy of both proposed methods in providing quantitative information on the regional behavior of arterial samples tested in vitro under physiologically relevant loading.

Biomechanical consequences of compromised elastic fiber integrity and matrix cross-linking on abdominal aortic aneurysmal enlargement

Abdominal aortic aneurysms (AAAs) are characterized histopathologically by compromised elastic fiber integrity, lost smooth muscle cells or their function, and remodeled collagen. We used a recently introduced mouse model of AAAs that combines enzymatic degradation of elastic fibers and blocking of lysyl oxidase, and thus matrix cross-linking, to study progressive dilatation of the infrarenal abdominal aorta, including development of intraluminal thrombus. We quantified changes in biomaterial properties and biomechanical functionality within the aneurysmal segment as a function of time of enlargement and degree of thrombosis. Towards this end, we combined multi-modality imaging with state-of-the art biomechanical testing and histology to quantify regional heterogeneities for the first time and we used a computational model of arterial growth and remodeling to test multiple hypotheses, suggested by the data, regarding the degree of lost elastin, accumulation of glycosaminoglycans, and rates of collagen turnover. We found that standard histopathological findings can be misleading, while combining advanced experimental and computational methods revealed that glycosaminoglycan accumulation is pathologic, not adaptive, and that heightened collagen deposition is ineffective if not cross-linked. In conclusion, loss of elastic fiber integrity can be a strong initiator of aortic aneurysms, but it is the rate and effectiveness of fibrillar collagen remodeling that dictates enlargement. STATEMENT OF SIGNIFICANCE: Precise mechanisms by which abdominal aortic aneurysms enlarge remain unclear, but a recent elastase plus β-aminopropionitrile mouse model provides new insight into disease progression. As in the human condition, the aortic degeneration and adverse remodeling are highly heterogeneous in this model. Our multi-modality experiments quantify and contrast the heterogeneities in geometry and biomaterial properties, and our computational modeling shows that standard histopathology can be misleading. Neither accumulating glycosaminoglycans nor frustrated collagen synthesis slow disease progression, thus highlighting the importance of stimulating adaptive collagen remodeling to limit lesion enlargement.

Aortic aneurysms and dissections: Unmet needs from physicians and engineers perspectives

The treatment of aortic disease is complex, requiring cardiothoracic and vascular surgeons to make pre-, post- and intraoperative decisions directly influencing patient survival and well-being. Despite tremendous advancement in vascular surgery and endovascular techniques in the last two decades, along with the abundance of research in the field, many unmet needs and unanswered questions remain. Tight collaboration between engineers and physicians is a keystone in translating new tools, techniques, and devices into practice. Here, we have gathered our perspective, as physicians and engineers, in several pressing issues associated with the diagnosis and treatment of aortic aneurysms and dissection, referring to the current knowledge and practice, signifying unmet needs as well as future directions.

Sensitivity analysis of non-local damage in soft biological tissues

Computational modeling can provide insight into understanding the damage mechanisms of soft biological tissues. Our gradient-enhanced damage model presented in a previous publication has shown advantages in considering the internal length scales and in satisfying mesh independence for simulating damage, growth and remodeling processes. Performing sensitivity analyses for this model is an essential step towards applications involving uncertain patient-specific data. In this paper, a numerical analysis approach is developed. For that we integrate two existing methods, that is, the gradient-enhanced damage model and the surrogate model-based probability analysis. To increase the computational efficiency of the Monte Carlo method in uncertainty propagation for the nonlinear hyperelastic damage analysis, the surrogate model based on Legendre polynomial series is employed to replace the direct FEM solutions, and the sparse grid collocation method (SGCM) is adopted for setting the collocation points to further reduce the computational cost in training the surrogate model. The effectiveness of the proposed approach is illustrated by two numerical examples, including an application of artery dilatation mimicking to the clinical problem of balloon angioplasty.

The Association Between Curvature and Rupture in a Murine Model of Abdominal Aortic Aneurysm and Dissection

BACKGROUND

Mouse models of abdominal aortic aneurysm (AAA) and dissection have proven to be invaluable in the advancement of diagnostics and therapeutics by providing a platform to decipher response variables that are elusive in human populations. One such model involves systemic Angiotensin II (Ang-II) infusion into low density-lipoprotein receptor-deficient (LDLr-/-) mice leading to intramural thrombus formation, inflammation, matrix degradation, dilation, and dissection. Despite its effectiveness, considerable experimental variability has been observed in AAAs taken from our Ang-II infused LDLr-/- mice (n=12) with obvious dissection occurring in 3 samples, outer bulge radii ranging from 0.73 to 2.12 mm, burst pressures ranging from 155 to 540 mmHg, and rupture location occurring 0.05 to 2.53 mm from the peak bulge location.

OBJECTIVE

We hypothesized that surface curvature, a fundamental measure of shape, could serve as a useful predictor of AAA failure at supra-physiological inflation pressures.

METHODS

To test this hypothesis, we fit well-known biquadratic surface patches to 360° micro-mechanical test data and used Spearman's rank correlation (rho) to identify relationships between failure metrics and curvature indices.

RESULTS

We found the strongest associations between burst pressure and the maximum value of the first principal curvature (rho=-0.591, p-val=0.061), the maximum value of Mean curvature (rho=-0.545, p-val=0.087), and local values of Mean curvature at the burst location (rho=-0.864, p-val=0.001) with only the latter significant after Bonferroni correction. Additionally, the surface profile at failure was predominantly convex and hyperbolic (saddle-shaped) as indicated by a negative sign in the Gaussian curvature. Findings reiterate the importance of shape in experimental models of AAA.

Exploring the potential of transfer learning for metamodels of heterogeneous material deformation

From the nano-scale to the macro-scale, biological tissue is spatially heterogeneous. Even when tissue behavior is well understood, the exact subject specific spatial distribution of material properties is often unknown. And, when developing computational models of biological tissue, it is usually prohibitively computationally expensive to simulate every plausible spatial distribution of material properties for each problem of interest. Therefore, one of the major challenges in developing accurate computational models of biological tissue is capturing the potential effects of this spatial heterogeneity. Recently, machine learning based metamodels have gained popularity as a computationally tractable way to overcome this problem because they can make predictions based on a limited number of direct simulation runs. These metamodels are promising, but they often still require a high number of direct simulations to achieve an acceptable performance. Here we show that transfer learning, a strategy where knowledge gained while solving one problem is transferred to solving a different but related problem, can help overcome this limitation. Critically, transfer learning can be used to leverage both low-fidelity simulation data and simulation data that is the outcome of solving a different but related mechanical problem. In this paper, we extend Mechanical MNIST, our open source benchmark dataset of heterogeneous material undergoing large deformation, to include a selection of low-fidelity simulation results that require ≈ 2 - 4 orders of magnitude less CPU time to run. Then, we show that transferring the knowledge stored in metamodels trained on these low-fidelity simulation results can vastly improve the performance of metamodels used to predict the results of high-fidelity simulations. In the most dramatic examples, metamodels trained on 100 high fidelity simulations but pre-trained on 60,000 low-fidelity simulations achieves nearly the same test error as metamodels trained on 60,000 high-fidelity simulations (1 - 1.5% mean absolute percent error). In addition, we show that transfer learning is an effective method for leveraging data from different load cases, and for leveraging low-fidelity two-dimensional simulations to predict the outcomes of high-fidelity three-dimensional simulations. Looking forward, we anticipate that transfer learning will enable us to better capture the influence of tissue spatial heterogeneity on the mechanical behavior of biological materials across multiple different domains.