Identifying Local Arterial Stiffness to Assess the Risk of Rupture of Ascending Thoracic Aortic Aneurysms

HSPB6 Deficiency Promotes the Development of Aortic Dissection and Rupture

To better understand the pathogenesis of acute type A aortic dissection, high-sensitivity liquid chromatography-tandem mass spectrometry/mass spectrometry (LC-MS/MS)-based proteomics and phosphoproteomics approaches were used to identify differential proteins. Heat shock protein family B (small) member 6 (HSPB6) in aortic dissection was significantly reduced in human and mouse aortic dissection samples by real-time PCR, western blotting, and immunohistochemical staining techniques. Using an HSPB6-knockout mouse, we investigated the potential role of HSPB6 in β-aminopropionitrile monofumarate-induced aortic dissection. We found increased mortality and increased probability of ascending aortic dissection after HSPB6 knockout compared with wild-type mice. Mechanistically, our data suggest that HSPB6 deletion promoted vascular smooth muscle cell apoptosis. More importantly, HSPB6 deletion attenuated cofilin activity, leading to excessive smooth muscle cell stiffness and eventually resulting in the development of aortic dissection and rupture. Our data suggest that excessive stiffness of vascular smooth muscle cells caused by HSPB6 deficiency is a new pathogenetic mechanism leading to aortic dissection.

Mechanics of ascending aortic aneurysms based on a modulus of elasticity dependent on aneurysm diameter and pressure

The mechanical stresses and strains are examined, in ascending thoracic aortic aneurysm (aTAA) models, in a patient-specific aTAA as well as in healthy thoracic aortic models, via Finite Element Analysis. The aneurysms are assumed spherical, 1.5 mm thick, with diameters between 47 mm and 80 mm, eccentrically positioned. The geometry and wall thickness distribution of the aorta along its length are based on open literature data for an average patient age of 66.25 years, accounting for the Body Surface Area (BSA) parameter. The vessel wall material is assumed isotropic and incompressible, with its Young's modulus varying with the aneurysm diameter and the applied intraluminal pressure (120 mmHg to 240 mmHg). In the aTAAs, peak stresses were found to increase nonlinearly with aneurysm diameter (for a given pressure) tending to reach a plateau, appearing at the proximal area of the aneurysm, whereas lower stresses were found at its distal part and even smaller at the aneurysm maximum diameter. Regarding the patient-specific aTAA model, the peak stresses appeared at the distal part of the aneurysm where a tear of the intima layer was detected during surgical intervention. Peak strains exhibited for each pressure a maximum at a certain aneurysm diameter beyond which they dropped so that essentially the vessel wall's distensibility was thus reduced. Examining more than 100 geometry cases and employing a failure stress criterion, the rupture diameter thresholds were estimated to be 65, 52.5, 50 and 47.5 mm for a pressure of 120, 160, 200 and 240 mmHg respectively.

Statistical shape representation of the thoracic aorta: accounting for major branches of the aortic arch

Statistical shape modeling (SSM) is an emerging tool for risk assessment of thoracic aortic aneurysm. However, the head branches of the aortic arch are often excluded in SSM. We introduced an SSM strategy based on principal component analysis that accounts for aortic branches and applied it to a set of patient scans. Computational fluid dynamics were performed on the reconstructed geometries to identify the extent to which branch model accuracy affects the calculated wall shear stress (WSS) and pressure. Surface-averaged and location-specific values of pressure did not change significantly, but local WSS error was high near branches when inaccurately modeled.

An image-based approach for the estimation of arterial local stiffness in vivo

The analysis of mechanobiology of arterial tissues remains an important topic of research for cardiovascular pathologies evaluation. In the current state of the art, the gold standard to characterize the tissue mechanical behavior is represented by experimental tests, requiring the harvesting of ex-vivo specimens. In recent years though, image-based techniques for the in vivo estimation of arterial tissue stiffness were presented. The aim of this study is to define a new approach to provide local distribution of arterial stiffness, estimated as the linearized Young's Modulus, based on the knowledge of in vivo patient-specific imaging data. In particular, the strain and stress are estimated with sectional contour length ratios and a Laplace hypothesis/inverse engineering approach, respectively, and then used to calculate the Young's Modulus. After describing the method, this was validated by using a set of Finite Element simulations as input. In particular, idealized cylinder and elbow shapes plus a single patient-specific geometry were simulated. Different stiffness distributions were tested for the simulated patient-specific case. After the validation from Finite Element data, the method was then applied to patient-specific ECG-gated Computed Tomography data by also introducing a mesh morphing approach to map the aortic surface along the cardiac phases. The validation process revealed satisfactory results. In the simulated patient-specific case, root mean square percentage errors below 10% for the homogeneous distribution and below 20% for proximal/distal distribution of stiffness. The method was then successfully used on the three ECG-gated patient-specific cases. The resulting distributions of stiffness exhibited significant heterogeneity, nevertheless the resulting Young's moduli were always contained within the 1-3 MPa range, which is in line with literature.

Ascending aortic geometry and its relationship to the biomechanical properties of aortic tissue

Objective

The objective of this study was to evaluate the relationship between ascending aortic geometry and biomechanical properties.

Methods

Preoperative computed tomography scans from ascending aortic aneurysm patients were analyzed using a center line technique (n = 68). Aortic length was measured from annulus to innominate artery, and maximal diameter from this segment was recorded. Biaxial tensile testing of excised tissue was performed to derive biomechanical parameters energy loss (efficiency in performing the Windkessel function) and modulus of elasticity (stiffness). Delamination testing (simulation of dissection) was performed to derive delamination strength (strength between tissue layers).

Results

Aortic diameter weakly correlated with energy loss (r ² = 0.10; P < .01), but not with modulus of elasticity (P = .13) or delamination strength (P = .36). Aortic length was not associated with energy loss (P = .87), modulus of elasticity (P = .13) or delamination strength (P = .90). Using current diameter guidelines, aortas >55 mm (n = 33) demonstrated higher energy loss than those <55 mm (n = 35; P = .05), but no difference in modulus of elasticity (P = .25) or delamination strength (P = .89). A length cutoff of 110 mm was proposed as an indication for repair. Aortas >110 mm (n = 37) did not exhibit a difference in energy loss (P = .40), modulus of elasticity (P = .69), or delamination strength (P = .68) compared with aortas <110 mm (n = 31). Aortas above diameter and length thresholds (n = 21) showed no difference in energy loss (P = .35), modulus of elasticity (P = .55), or delamination strength (P = .61) compared with smaller aortas (n = 47).

Conclusions

Aortic geometry poorly reflects the mechanical properties of aortic tissue. Weak association between energy loss and diameter supports intervention at larger diameters. Further research into markers that better capture aortic biomechanics is needed.

Dependency of energy loss on strain rate, strain magnitude and preload: Towards development of a novel biomarker for aortic aneurysm dissection risk

Dissection is the most common mode of failure for ascending aortic aneurysms. Currently, failure risk is assessed by measuring aortic diameter, which is insufficient as it misses many dissection patients. This motivated the search for a new biomarker that captures intrinsic tissue material properties related to failure. Energy loss is promising in this regard as it is correlated with microstructure degradation and failure of aneurysms. However, for energy loss to be used clinically, its dependency on in vivo loading conditions, which vary from patient-to-patient, must be determined. In this study, the sensitivity of energy loss to physiological strain rate, magnitude, and preload was examined. Energy loss was found to be relatively insensitive to loading conditions while maintaining a significant correlation with delamination strength as a surrogate for dissection except at low strains. These results can be used for clinical translation of in vivo measurements of energy loss to evaluate aortic dissection risk.

Coupling hemodynamics with mechanobiology in patient-specific computational models of ascending thoracic aortic aneurysms

BACKGROUND AND OBJECTIVE

The prevention of ascending thoracic aortic aneurysms (ATAAs), which affect thousands of persons every year worldwide, remains a major issue. ATAAs may be caused by anything that weakens the aortic wall. Altered hemodynamics, which concerns a majority of patients with bicuspid aortic valves, has been shown to be related to such weakening and to contribute to ATAA development and progression. However the underlying mechanisms remain unclear and computational modeling in this field could help significantly to elucidate how hemodynamics and mechanobiology interact in ATAAs.

METHODS

Accordingly, we propose a numerical framework combining computational fluid dynamics and 4D flow magnetic resonance imaging (MRI) coupled with finite element (FE) analyses to simulate growth and remodeling (G&R) occurring in patient-specific aortas in relation with altered hemodynamics. The geometries and the blood velocities obtained from 4D flow MRI are used as boundary conditions for CFD simulations. CFD simulations provide an estimation of the wall shear stress (WSS) and relative residence time (RRT) distribution across the luminal surface of the wall. An initial insult is then applied to the FE model of the aortic wall, assuming that the magnitude of the insult correlates spatially with the normalized RRT distribution obtained from CFD simulations. G&R simulations are then performed. The material behavior of each Gauss point in these FE models is evolved continuously to compensate for the deviation of the actual wall stress distribution from the homeostatic state after the initial insult. The whole approach is illustrated on two healthy and two diseased subjects. The G&R parameters are calibrated against previously established statistical models of ATAA growth rates.

RESULTS

Among the variety of results provided by G&R simulations, the analysis focused especially on the evolution of the wall stiffness, which was shown to be a major risk factor for ATAAs. It was shown that the G&R parameters, such as for instance the rate of collagen production or cell mechanosensitivity, play a critical role in ATAA progression and remodeling.

CONCLUSIONS

These preliminary findings show that patient-specific computational modeling coupling hemodynamics with mechanobiology is a promising approach to explore aneurysm progression.

Patient-specific computational evaluation of stiffness distribution in ascending thoracic aortic aneurysm

Quantifying local aortic stiffness properties in vivo is acknowledged as essential to assess the severity of an ascending thoracic aortic aneurysm (ATAA). Recently, the LESI (local extensional stiffness identification) methodology has been established to quantify non-invasively local stiffness properties of ATAAs using electrocardiographic-gated computed tomography (ECG-gated CT) scans. The aim of the current study was to determine the most sensitive markers of local ATAA stiffness estimation with the hypothesis that direct measures of local ATAA stiffness could better detect the high-risk patients. A cohort of 30 patients (12 BAV and 18 TAV) referred for aortic size evaluation by ECG-gated CT were recruited. For each patient, the extensional stiffness Q was evaluated by the LESI methodology whilst computational flow analyses were also performed to derive hemodynamics markers such as the wall shear stress (WSS). A strong positive correlation was found between the extensional stiffness and the aortic pulse pressure (R = 0.644 and p < 0.001). Interestingly, a significant positive correlation was also found between the extensional stiffness and patients age for BAV ATAAs (R = 0.619 and p = 0.032), but not for TAV ATAAs (R = -0.117 and p = 0.645). No significant correlation was found between the extensional stiffness and WSS evaluated locally. There was no significant difference either in the extensional stiffness between BAV ATAAs and TAV ATAAs (Q = 3.6 ± 2.5 MPa.mm for BAV ATAAs vs Q = 5.3 ± 3.1 MPa.mm for TAV ATAAs, p = 0.094). Future work will focus on relating the extensional stiffness to the patient-specific rupture risk of ATAAs on larger cohorts to confirm the promising interest of the LESI methodology.

Regulation of SMC traction forces in human aortic thoracic aneurysms

Smooth muscle cells (SMCs) usually express a contractile phenotype in the healthy aorta. However, aortic SMCs have the ability to undergo profound changes in phenotype in response to changes in their extracellular environment, as occurs in ascending thoracic aortic aneurysms (ATAA). Accordingly, there is a pressing need to quantify the mechanobiological effects of these changes at single cell level. To address this need, we applied Traction Force Microscopy (TFM) on 759 cells coming from three primary healthy (AoPrim) human SMC lineages and three primary aneurysmal (AnevPrim) human SMC lineages, from age and gender matched donors. We measured the basal traction forces applied by each of these cells onto compliant hydrogels of different stiffness (4, 8, 12, 25 kPa). Although the range of force generation by SMCs suggested some heterogeneity, we observed that: 1. the traction forces were significantly larger on substrates of larger stiffness; 2. traction forces in AnevPrim were significantly higher than in AoPrim cells. We modelled computationally the dynamic force generation process in SMCs using the motor-clutch model and found that it accounts well for the stiffness-dependent traction forces. The existence of larger traction forces in the AnevPrim SMCs were related to the larger size of cells in these lineages. We conclude that phenotype changes occurring in ATAA, which were previously known to reduce the expression of elongated and contractile SMCs (rendering SMCs less responsive to vasoactive agents), tend also to induce stronger SMCs. Future work aims at understanding the causes of this alteration process in aortic aneurysms.

Estimating aortic thoracic aneurysm rupture risk using tension-strain data in physiological pressure range: an in vitro study

Previous studies have shown that the rupture properties of an ascending thoracic aortic aneurysm (ATAA) are strongly correlated with the pre-rupture response features. In this work, we present a two-step machine learning method to predict where the rupture is likely to occur in ATAA and what safety reserve the structure may have. The study was carried out using ATAA specimens from 15 patients who underwent surgical intervention. Through inflation test, full-field deformation data and post-rupture images were collected, from which the wall tension and surface strain distributions were computed. The tension-strain data in the pressure range of 9-18 kPa were fitted to a third-order polynomial to characterize the response properties. It is hypothesized that the region where rupture is prone to initiate is associated with a high level of tension buildup. A machine learning method is devised to predict the peak risk region. The predicted regions were found to match the actual rupture sites in 13 samples out of the total 15. In the second step, another machine learning model is utilized to predict the tissue's rupture strength in the peak risk region. Results suggest that the ATAA rupture risk can be reasonably predicted using tension-strain response in the physiological range. This may open a pathway for evaluating the ATAA rupture propensity using information of in vivo response.

Prediction of local strength of ascending thoracic aortic aneurysms

Knowledges of both local stress and strength are needed for a reliable evaluation of the rupture risk for ascending thoracic aortic aneurysm (ATAA). In this study, machine learning is applied to predict the local strength of ATAA tissues based on tension-strain data collected through in vitro inflation tests on tissue samples. Inputs to machine learning models are tension, strain, slope, and curvature values at two points on the low strain region of the tension-strain curve. The models are trained using data from locations where the tissue ruptured, and subsequently applied to data from intact sites to predict the local rupture strength. The predicted strengths are compared with the known strength at rupture sites as well as the highest tension the tissues experienced at the intact sites. A local rupture index, which is the ratio of the end tension to the predicted rupture strength, is computed. The 'hot spots' of the rupture index are found to match the rupture sites better than those of the peak tension. The study suggests that the strength of ATAA tissue could be reliably predicted from early phase response features defined in this work.

Computational Study of Growth and Remodeling in Ascending Thoracic Aortic Aneurysms Considering Variations of Smooth Muscle Cell Basal Tone

In this paper, we investigate the progression of Ascending Thoracic Aortic Aneurysms (ATAA) using a computational model of Growth and Remodeling (G&R) taking into account the composite (elastin, four collagen fiber families and Smooth Muscle Cells—SMCs) and multi-layered (media and adventitia) nature of the aorta. The G&R model, which is based on the homogenized Constrained Mixture theory, is implemented as a UMAT in the Abaqus finite-element package. Each component of the mixture is assigned a strain energy density function: nearly-incompressible neo-Hookean for elastin and Fung-type for collagen and SMCs. Active SMCs tension is additionally considered, through a length-tension relationship having a classic inverted parabola shape, in order to investigate its effects on the progression of ATAA in a patient-specific model. A sensitivity analysis is performed to evaluate the potential impact of variations in the parameters of the length-tension relationships. These variations reflect in variations of SMCs normal tone during ATAA progression, with active stress contributions ranging between 30% (best case scenario) and 0% (worst case scenario) of the total wall circumferential stress. Low SMCs active stress in the worst case scenarios, in fact, affect the rates of collagen deposition by which the elastin loss is gradually compensated by collagen deposition in the simulated ATAA progression, resulting eventually in larger aneurysm diameters. The types of length-tension relationships leading to a drop of SMCs active stress in our simulations reveal a critical condition which could also result in SMCs apoptosis.

Patient-Specific CT-Based Fluid-Structure-Interaction Aorta Model to Quantify Mechanical Conditions for the Investigation of Ascending Aortic Dilation in TOF Patients

Background

Some adult patients with Tetralogy of Fallot (TOF) were found to simultaneously develop ascending aortic dilation. Severe aortic dilation would lead to several aortic diseases, including aortic aneurysm and dissection, which seriously affect patients' living quality and even cause patients' death. Current practice guidelines of aortic-dilation-related diseases mainly focus on aortic diameter, which has been found not always a good indicator. Therefore, it may be clinically useful to identify some other factors that can potentially better predict aortic response to dilation.

Methods

20 TOF patients scheduled for TOF repair surgery were recruited in this study and were divided into dilated and nondilated groups according to the Z scores of ascending aorta diameters. Patient-specific aortic CT images, pressure, and flow rates were used in the construction of computational biomechanical models.

Results

Simulation results demonstrated a good coincidence between numerical mean flow rate at inlet and the one obtained from color Doppler ultrasonography, which implied that computational models were able to simulate the movement of the aorta and blood inside accurately. Our results indicated that aortic stress can effectively differentiate patients of the dilated group from the ones of the nondilated group. Mean ascending aortic stress-P1 (maximal principal stress) from the dilated group was 54% higher than that from the nondilated group (97.97 kPa vs. 63.47 kPa, p value = 0.044) under systolic pressure. Velocity magnitude in the aorta and aortic wall displacement of the dilated group were also greater than those of the nondilated group with p value < 0.1.

Conclusion

Computational modeling and ascending aortic biomechanical factors may be used as a potential tool to identify and analyze aortic response to dilation. Large-scale clinical studies are needed to validate these preliminary findings.

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

Chronic infusion of angiotensin-II in atheroprone (ApoE-/-) mice provides a reproducible model of dissection in the suprarenal abdominal aorta, often with a false lumen and intramural thrombus that thickens the wall. Such lesions exhibit complex morphologies, with different regions characterized by localized changes in wall composition, microstructure, and properties. We sought to quantify the multiaxial mechanical properties of murine dissecting aneurysm samples by combining in vitro extension-distension data with full-field multimodality measurements of wall strain and thickness to inform an inverse material characterization using the virtual fields method. A key advance is the use of a digital volume correlation approach that allows for characterization of properties not only along and around the lesion, but also across its wall. Specifically, deformations are measured at the adventitial surface by tracking motions of a speckle pattern using a custom panoramic digital image correlation technique while deformations throughout the wall and thrombus are inferred from optical coherence tomography. These measurements are registered and combined in 3D to reconstruct the reference geometry and compute the 3D finite strain fields in response to pressurization. Results reveal dramatic regional variations in material stiffness and strain energy, which reflect local changes in constituent area fractions obtained from histology but emphasize the complexity of lesion morphology and damage within the dissected wall. This is the first point-wise biomechanical characterization of such complex, heterogeneous arterial segments. Because matrix remodeling is critical to the formation and growth of these lesions, we submit that quantification of regional material properties will increase the understanding of pathological mechanical mechanisms underlying aortic dissection.

Recent Advances in Biomechanical Characterization of Thoracic Aortic Aneurysms

Thoracic aortic aneurysm (TAA) is a focal enlargement of the thoracic aorta, but the etiology of this disease is not fully understood. Previous work suggests that various genetic syndromes, congenital defects such as bicuspid aortic valve, hypertension, and age are associated with TAA formation. Though occurrence of TAAs is rare, they can be life-threatening when dissection or rupture occurs. Prevention of these adverse events often requires surgical intervention through full aortic root replacement or implantation of endovascular stent grafts. Currently, aneurysm diameters and expansion rates are used to determine if intervention is warranted. Unfortunately, this approach oversimplifies the complex aortopathy. Improving treatment of TAAs will likely require an increased understanding of the biological and biomechanical factors contributing to the disease. Past studies have substantially contributed to our knowledge of TAAs using various ex vivo, in vivo, and computational methods to biomechanically characterize the thoracic aorta. However, any singular approach typically focuses on only material properties of the aortic wall, intra-aneurysmal hemodynamics, or in vivo vessel dynamics, neglecting combinatorial factors that influence aneurysm development and progression. In this review, we briefly summarize the current understanding of TAA causes, treatment, and progression, before discussing recent advances in biomechanical studies of TAAs and possible future directions. We identify the need for comprehensive approaches that combine multiple characterization methods to study the mechanisms contributing to focal weakening and rupture. We hope this summary and analysis will inspire future studies leading to improved prediction of thoracic aneurysm progression and rupture, improving patient diagnoses and outcomes.

Identification of in vivo nonlinear anisotropic mechanical properties of ascending thoracic aortic aneurysm from patient-specific CT scans

Accurate identification of in vivo nonlinear, anisotropic mechanical properties of the aortic wall of individual patients remains to be one of the critical challenges in the field of cardiovascular biomechanics. Since only the physiologically loaded states of the aorta are given from in vivo clinical images, inverse approaches, which take into account of the unloaded configuration, are needed for in vivo material parameter identification. Existing inverse methods are computationally expensive, which take days to weeks to complete for a single patient, inhibiting fast feedback for clinicians. Moreover, the current inverse methods have only been evaluated using synthetic data. In this study, we improved our recently developed multi-resolution direct search (MRDS) approach and the computation time cost was reduced to 1~2 hours. Using the improved MRDS approach, we estimated in vivo aortic tissue elastic properties of two ascending thoracic aortic aneurysm (ATAA) patients from pre-operative gated CT scans. For comparison, corresponding surgically-resected aortic wall tissue samples were obtained and subjected to planar biaxial tests. Relatively close matches were achieved for the in vivo-identified and ex vivo-fitted stress-stretch responses. It is hoped that further development of this inverse approach can enable an accurate identification of the in vivo material parameters from in vivo image data.