In Vivo Strain Analysis of Dilated Ascending Thoracic Aorta by ECG-Gated CT Angiographic Imaging

The Maastricht Acquisition Platform for Studying Mechanisms of Cell-Matrix Crosstalk (MAPEX): An Interdisciplinary and Systems Approach towards Understanding Thoracic Aortic Disease

Current management guidelines for ascending thoracic aortic aneurysms (aTAA) recommend intervention once ascending or sinus diameter reaches 5-5.5 cm or shows a growth rate of >0.5 cm/year estimated from echo/CT/MRI. However, many aTAA dissections (aTAAD) occur in vessels with diameters below the surgical intervention threshold of <55 mm. Moreover, during aTAA repair surgeons observe and experience considerable variations in tissue strength, thickness, and stiffness that appear not fully explained by patient risk factors. To improve the understanding of aTAA pathophysiology, we established a multi-disciplinary research infrastructure: The Maastricht acquisition platform for studying mechanisms of tissue-cell crosstalk (MAPEX). The explicit scientific focus of the platform is on the dynamic interactions between vascular smooth muscle cells and extracellular matrix (i.e., cell-matrix crosstalk), which play an essential role in aortic wall mechanical homeostasis. Accordingly, we consider pathophysiological influences of wall shear stress, wall stress, and smooth muscle cell phenotypic diversity and modulation. Co-registrations of hemodynamics and deep phenotyping at the histological and cell biology level are key innovations of our platform and are critical for understanding aneurysm formation and dissection at a fundamental level. The MAPEX platform enables the interpretation of the data in a well-defined clinical context and therefore has real potential for narrowing existing knowledge gaps. A better understanding of aortic mechanical homeostasis and its derangement may ultimately improve diagnostic and prognostic possibilities to identify and treat symptomatic and asymptomatic patients with existing and developing aneurysms.

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.

Experimental and Mouse-Specific Computational Models of the Fbln4SMKO Mouse to Identify Potential Biomarkers for Ascending Thoracic Aortic Aneurysm

PURPOSE

To use computational methods to explore geometric, mechanical, and fluidic biomarkers that could correlate with mouse lifespan in the Fbln4SMKO mouse. Mouse lifespan was used as a surrogate for risk of a severe cardiovascular event in cases of ascending thoracic aortic aneurysm.

METHODS

Image-based, mouse-specific fluid-structure-interaction models were developed for Fbln4SMKO mice (n = 10) at ages two and six months. The results of the simulations were used to quantify potential biofluidic biomarkers, complementing the geometrical biomarkers obtained directly from the images.

RESULTS

Comparing the different geometrical and biofluidic biomarkers to the mouse lifespan, it was found that mean oscillatory shear index (OSImin) and minimum time-averaged wall shear stress (TAWSSmin) at six months showed the largest correlation with lifespan (r2 = 0.70, 0.56), with both correlations being positive (i.e., mice with high OSImean and high TAWSSmin tended to live longer). When change between two and six months was considered, the change in TAWSSmin showed a much stronger correlation than OSImean (r2 = 0.75 vs. 0.24), and the correlation was negative (i.e., mice with increasing TAWSSmin over this period tended to live less long).

CONCLUSION

The results highlight potential biomarkers of ATAA outcomes that can be obtained through noninvasive imaging and computational simulations, and they illustrate the potential synergy between small-animal and computational models.

Patient-derived microphysiological model identifies the therapeutic potential of metformin for thoracic aortic aneurysm

Background

Thoracic aortic aneurysm (TAA) is the permanent dilation of the thoracic aortic wall that predisposes patients to lethal events such as aortic dissection or rupture, for which effective medical therapy remains scarce. Human-relevant microphysiological models serve as a promising tool in drug screening and discovery.

Methods

We developed a dynamic, rhythmically stretching, three-dimensional microphysiological model. Using patient-derived human aortic smooth muscle cells (HAoSMCs), we tested the biological features of the model and compared them with native aortic tissues. Drug testing was performed on the individualized TAA models, and the potentially effective drug was further tested using β-aminopropionitrile-treated mice and retrospective clinical data.

Findings

The HAoSMCs on the model recapitulated the expressions of many TAA-related genes in tissue. Phenotypic switching and mitochondrial dysfunction, two disease hallmarks of TAA, were highlighted on the microphysiological model: the TAA-derived HAoSMCs exhibited lower alpha-smooth muscle actin expression, lower mitochondrial membrane potential, lower oxygen consumption rate and higher superoxide accumulation than control cells, while these differences were not evidently reflected in two-dimensional culture flasks. Model-based drug testing demonstrated that metformin partially recovered contractile phenotype and mitochondrial function in TAA patients’ cells. Mouse experiment and clinical investigations also demonstrated better preserved aortic microstructure, higher nicotinamide adenine dinucleotide level and lower aortic diameter with metformin treatment.

Interpretation

These findings support the application of this human-relevant microphysiological model in studying personalized disease characteristics and facilitating drug discovery for TAA. Metformin may regulate contractile phenotypes and metabolic dysfunctions in diseased HAoSMCs and limit aortic dilation.

Funding

This work was supported by grants from National Key R&D Program of China (2018YFC1005002), National Natural Science Foundation of China (82070482, 81771971, 81772007, 51927805, and 21734003), the Science and Technology Commission of Shanghai Municipality (20ZR1411700, 18ZR1407000, 17JC1400200, and 20YF1406900), Shanghai Municipal Science and Technology Major Project (2017SHZDZX01), and Shanghai Municipal Education Commission (Innovation Program 2017-01-07-00-07-E00027). Y.S.Z. was not supported by any of these funds; instead, the Brigham Research Institute is acknowledged.

Biomechanics of Transcatheter Aortic Valve Replacement Complications and Computational Predictive Modeling

Transcatheter aortic valve replacement (TAVR) is a rapidly growing field enabling replacement of diseased aortic valves without the need for open heart surgery. However, due to the nature of the procedure and nonremoval of the diseased tissue, there are rates of complications ranging from tissue rupture and coronary obstruction to paravalvular leak, valve thrombosis, and permanent pacemaker implantation. In recent years, computational modeling has shown a great deal of promise in its capabilities to understand the biomechanical implications of TAVR as well as help preoperatively predict risks inherent to device-patient-specific anatomy biomechanical interaction. This includes intricate replication of stent and leaflet designs and tested and validated simulated deployments with structural and fluid mechanical simulations. This review outlines current biomechanical understanding of device-related complications from TAVR and related predictive strategies using computational modeling. An outlook on future modeling strategies highlighting reduced order modeling which could significantly reduce the high time and cost that are required for computational prediction of TAVR outcomes is presented in this review paper. A summary of current commercial/in-development software is presented in the final section.

Validation of a robust method for quantification of three-dimensional growth of the thoracic aorta using deformable image registration

PURPOSE

Accurate assessment of thoracic aortic aneurysm (TAA) growth is important for appropriate clinical management. Maximal aortic diameter is the primary metric that is used to assess growth, but it suffers from substantial measurement variability. A recently proposed technique, termed vascular deformation mapping (VDM), is able to quantify three-dimensional aortic growth using clinical computed tomography angiography (CTA) data using an approach based on deformable image registration (DIR). However, the accuracy and robustness of VDM remains undefined given the lack of ground truth from clinical CTA data, and, furthermore, the performance of VDM relative to standard manual diameter measurements is unknown.

METHODS

To evaluate the performance of the VDM pipeline for quantifying aortic growth, we developed a novel and systematic evaluation process to generate 76 unique synthetic CTA growth phantoms (based on 10 unique cases) with variable degrees and locations of aortic wall deformation. Aortic deformation was quantified using two metrics: area ratio (AR), defined as the ratio of surface area in triangular mesh elements and the magnitude of deformation in the normal direction (DiN) relative to the aortic surface. Using these phantoms, we further investigated the effects on VDM's measurement accuracy resulting from factors that influence the quality of clinical CTA data such as respiratory translations, slice thickness, and image noise. Lastly, we compare the measurement error of VDM TAA growth assessments against two expert raters performing standard diameter measurements of synthetic phantom images.

RESULTS

Across our population of 76 synthetic growth phantoms, the median absolute error was 0.063 (IQR: 0.073-0.054) for AR and 0.181 mm (interquartile range [IQR]: 0.214-0.143 mm) for DiN. Median relative error was 1.4% for AR and3.3 % $3.3\%$ for DiN at the highest tested noise level (contrast-to-noise ratio [CNR] = 2.66). Error in VDM output increased with slice thickness, with the highest median relative error of 1.5% for AR and 4.1% for DiN at a slice thickness of 2.0 mm. Respiratory motion of the aorta resulted in maximal absolute error of 3% AR and 0.6 mm in DiN, but bulk translations in aortic position had a very small effect on measured AR and DiN values (relative errors< 1 % $< 1\%$ ). VDM-derived measurements of magnitude and location of maximal diameter change demonstrated significantly high accuracy and lower variability compared to two expert manual raters (p < 0.03 $p<0.03$ across all comparisons).

CONCLUSIONS

VDM yields an accurate, three-dimensional assessment of aortic growth in TAA patients and is robust to factors such as image noise, respiration-induced translations, and differences in patient position. Further, VDM significantly outperformed two expert manual raters in assessing the magnitude and location of aortic growth despite optimized experimental measurement conditions. These results support validation of the VDM technique for accurate quantification of aortic growth in patients and highlight several important advantages over diameter measurements.

[Role of imaging modalities in the choice of treatment strategy for mega aorta syndrome]

The authors report a 76-year-old female with mega-aorta syndrome that was asymptomatic for a long time. The first symptoms appeared after ascending aorta enlargement up to 81 mm and compression of superior vena cava. The patient underwent frozen elephant trunk procedure. The authors demonstrate the possibilities of assessing the aortic strain by ECG-synchronized CT angiography and 2D transesophageal ultrasound with speckle tracking. Potential role of these methods in determining the type of aortic reconstruction is discussed.

Patient-Specific Analysis of Ascending Thoracic Aortic Aneurysm with the Living Heart Human Model

In ascending thoracic aortic aneurysms (ATAAs), aneurysm kinematics are driven by ventricular traction occurring every heartbeat, increasing the stress level of dilated aortic wall. Aortic elongation due to heart motion and aortic length are emerging as potential indicators of adverse events in ATAAs; however, simulation of ATAA that takes into account the cardiac mechanics is technically challenging. The objective of this study was to adapt the realistic Living Heart Human Model (LHHM) to the anatomy and physiology of a patient with ATAA to assess the role of cardiac motion on aortic wall stress distribution. Patient-specific segmentation and material parameter estimation were done using preoperative computed tomography angiography (CTA) and ex vivo biaxial testing of the harvested tissue collected during surgery. The lumped-parameter model of systemic circulation implemented in the LHHM was refined using clinical and echocardiographic data. The results showed that the longitudinal stress was highest in the major curvature of the aneurysm, with specific aortic quadrants having stress levels change from tensile to compressive in a transmural direction. This study revealed the key role of heart motion that stretches the aortic root and increases ATAA wall tension. The ATAA LHHM is a realistic cardiovascular platform where patient-specific information can be easily integrated to assess the aneurysm biomechanics and potentially support the clinical management of patients with ATAAs.

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.

Creation of Anatomically Correct and Optimized for 3D Printing Human Bones Models

Educational institutions in several countries state that the education sector should be modernized to ensure a contemporary, individualized, and more open learning process by introducing and developing advance digital solutions and learning tools. Visualization along with 3D printing have already found their implementation in different medical fields in Pauls Stradiņš Clinical University Hospital, and Rīga Stradiņš University, where models are being used for prosthetic manufacturing, surgery planning, simulation of procedures, and student education. The study aimed to develop a detailed methodology for the creation of anatomically correct and optimized models for 3D printing from radiological data using only free and widely available software. In this study, only free and cross-platform software from widely available internet sources has been used—“Meshmixer”, “3D Slicer”, and “Meshlab”. For 3D printing, the Ultimaker 5S 3D printer along with PLA material was used. In its turn, radiological data have been obtained from the “New Mexico Decedent Image Database”. In total, 28 models have been optimized and printed. The developed methodology can be used to create new models from scratch, which can be used will find implementation in different medical and scientific fields—simulation processes, anthropology, 3D printing, bioprinting, and education.

Transcatheter Heart Valve Implantation in Bicuspid Patients with Self-Expanding Device

Bicuspid aortic valve (BAV) patients are conventionally not treated by transcathether aortic valve implantation (TAVI) because of anatomic constraint with unfavorable outcome. Patient-specific numerical simulation of TAVI in BAV may predict important clinical insights to assess the conformability of the transcathether heart valves (THV) implanted on the aortic root of members of this challenging patient population. We aimed to develop a computational approach and virtually simulate TAVI in a group of n.6 stenotic BAV patients using the self-expanding Evolut Pro THV. Specifically, the structural mechanics were evaluated by a finite-element model to estimate the deformed THV configuration in the oval bicuspid anatomy. Then, a fluid–solid interaction analysis based on the smoothed-particle hydrodynamics (SPH) technique was adopted to quantify the blood-flow patterns as well as the regions at high risk of paravalvular leakage (PVL). Simulations demonstrated a slight asymmetric and elliptical expansion of the THV stent frame in the BAV anatomy. The contact pressure between the luminal aortic root surface and the THV stent frame was determined to quantify the device anchoring force at the level of the aortic annulus and mid-ascending aorta. At late diastole, PVL was found in the gap between the aortic wall and THV stent frame. Though the modeling framework was not validated by clinical data, this study could be considered a further step towards the use of numerical simulations for the assessment of TAVI in BAV, aiming at understanding patients not suitable for device implantation on an anatomic basis.

Differential Diagnosis of Intraplaque Hemorrhage and Dissection on High-Resolution MR Imaging in Patients with Focal High Signal of the Vertebrobasilar Artery on TOF Imaging

PURPOSE

Intraplaque hemorrhage (IPH) and dissection in the vertebrobasilar artery (VBA) on time of flight (TOF) source imaging are seen as focal eccentric high-signal intensity. The purpose of this study is to identify IPH and dissection in the VBA using high-resolution magnetic resonance imaging (HR-MRI).

METHODS

A total of 78 patients (VBA IPH: 55; dissection: 23) with focal high-signal intensity in the VBA on simultaneous non-contrast angiography and intraplaque hemorrhage (SNAP) of HR-MRI were included in this study. The focal high-signal intensity in the VBA on SNAP was defined as >200% than that of the adjacent muscle. We analyzed the signal intensity ratio (area of focal high signal intensity area/lumen) on TOF imaging and black blood (BB) T2-weighted imaging.

RESULTS

The VBA IPH group was older than the dissection group and had more hypertension. Signal intensity of a false lumen in patients with dissection on TOF imaging was significantly higher than that of VBA IPH (p < 0.001). The signal intensity ratio between lumen and lesion on TOF imaging was significantly higher in the dissection group (p < 0.001). The signal intensity of a false lumen in patients with dissection on BB T2-weighted imaging was significantly lower than that of VBA IPH (p < 0.001). The signal intensity ratio between lumen and lesion on BB T2-weighted imaging was significantly higher in the VBA IPH group (p < 0.001).

CONCLUSIONS

TOF imaging and BB T2-weighted imaging on HR-MRI in patients with focal eccentric high-signal intensity on TOF imaging can distinguish between VBA IPH and dissection.

On the Left Ventricular Remodeling of Patients with Stenotic Aortic Valve: A Statistical Shape Analysis

The left ventricle (LV) constantly changes its shape and function as a response to pathological conditions, and this process is known as remodeling. In the presence of aortic stenosis (AS), the degenerative process is not limited to the aortic valve but also involves the remodeling of LV. Statistical shape analysis (SSA) offers a powerful tool for the visualization and quantification of the geometrical and functional patterns of any anatomic changes. In this paper, a SSA method was developed to determine shape descriptors of the LV under different degrees of AS and thus to shed light on the mechanistic link between shape and function. A total of n=86 patients underwent computed tomography (CT) for the evaluation of valvulopathy were segmented to obtain the LV surface and then were automatically aligned to a reference template by rigid registrations and transformations. Shape modes of the anatomical LV variation induced by the degree of AS were assessed by principal component analysis (PCA). The first shape mode represented nearly 50% of the total variance of LV shape in our patient population and was mainly associated to a spherical LV geometry. At Pearson's analysis, the first shape mode was positively correlated to both the end-diastolic volume (p<0.01, R=0.814) and end-systolic volume (p<0.01, and R=0.922), suggesting LV impairment in patients with severe AS. A predictive model built with PCA-related shape modes achieved better performance in stratifying the occurrence of adverse events with respect to a baseline model using clinical demographic data as risk predictors. This study demonstrated the potential of SSA approaches to detect the association of complex 3D shape features with functional LV parameters.

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.

Linking Aortic Mechanical Properties, Gene Expression and Microstructure: A New Perspective on Regional Weakening in Abdominal Aortic Aneurysms

Background: Current clinical practice for the assessment of abdominal aortic aneurysms (AAA) is based on vessel diameter and does not account for the multifactorial, heterogeneous remodeling that results in the regional weakening of the aortic wall leading to aortic growth and rupture. The present study was conducted to determine correlations between a novel non-invasive surrogate measure of regional aortic weakening and the results from invasive analyses performed on corresponding ex vivo aortic samples. Tissue samples were evaluated to classify local wall weakening and the likelihood of further degeneration based on non-invasive indices.

Methods: A combined, image-based fluid dynamic and in-vivo strain analysis approach was used to estimate the Regional Aortic Weakness (RAW) index and assess individual aortas of AAA patients prior to elective surgery. Nine patients were treated with complete aortic resection allowing the systematic collection of tissue samples that were used to determine regional aortic mechanics, microstructure and gene expression by means of mechanical testing, microscopy and transcriptomic analyses.

Results: The RAW index was significantly higher for samples exhibiting lower mechanical strength (p = 0.035) and samples classified as low elastin content (p = 0.020). Samples with higher RAW index had the greatest number of genes differentially expressed compared to any constitutive metric. High RAW samples showed a decrease in gene expression for elastin and a down-regulation of pathways responsible for cell movement, reorganization of cytoskeleton, and angiogenesis.

Conclusions: This work describes the first AAA index free of assumptions for material properties and accounting for patient-specific mechanical behavior in relation to aneurysm strength. Use of the RAW index captured biomechanical changes linked to the weakening of the aorta and revealed changes in microstructure and gene expression. This approach has the potential to provide an improved tool to aid clinical decision-making in the management of aortic pathology.

Deep learning approach for the segmentation of aneurysmal ascending aorta

Diagnosis of ascending thoracic aortic aneurysm (ATAA) is based on the measurement of the maximum aortic diameter, but size is not a good predictor of the risk of adverse events. There is growing interest in the development of novel image-derived risk strategies to improve patient risk management towards a highly individualized level. In this study, the feasibility and efficacy of deep learning for the automatic segmentation of ATAAs was investigated using UNet, ENet, and ERFNet techniques. Specifically, CT angiography done on 72 patients with ATAAs and different valve morphology (i.e., tricuspid aortic valve, TAV, and bicuspid aortic valve, BAV) were semi-automatically segmented with Mimics software (Materialize NV, Leuven, Belgium), and then used for training of the tested deep learning models. The segmentation performance in terms of accuracy and time inference were compared using several parameters. All deep learning models reported a dice score higher than 88%, suggesting a good agreement between predicted and manual ATAA segmentation. We found that the ENet and UNet are more accurate than ERFNet, with the ENet much faster than UNet. This study demonstrated that deep learning models can rapidly segment and quantify the 3D geometry of ATAAs with high accuracy, thereby facilitating the expansion into clinical workflow of personalized approach to the management of patients with ATAAs.

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.

Influence of Material Model and Aortic Root Motion in Finite Element Analysis of Two Exemplary Cases of Proximal Aortic Dissection

The risk of type-A dissection is increased in subjects with connective tissue disorders and dilatation of the proximal aorta. The location and extents of vessel wall tears in these patients could be potentially missed during prospective imaging studies. The objective of this study is to estimate the distribution of systolic wall stress in two exemplary cases of proximal dissection using finite element analysis (FEA) and evaluate the sensitivity of the distribution to the choice of anisotropic material model and root motion. FEA was performed for predissection aortas, without prior knowledge of the origin and extents of vessel wall tear. The stress distribution was evaluated along the wall tear in the postdissection aortas. The stress distribution was compared for the Fung and Holzapfel models with and without root motion. For the subject with spiral dissection, peak stress coincided with the origin of the tear in the sinotubular junction. For the case with root dissection, maximum stress was obtained at the distal end of the tear. The FEA predicted tear pressure was 20% higher for the subject with root dissection as compared to the case with spiral dissection. The predicted tear pressure was higher (9-11%) for root motions up to 10 mm. The Holzapfel model predicted a tear pressure that was lower (8-15%) than the Fung model. The FEA results showed that both material response and root motion could potentially influence the predicted dissection pressure of the proximal aorta at least for conditions tested in this study.

Ascending Aorta Resection and End-to-End Anastomosis: Redistribution of Wall Shear Stress Induced by a Bioprosthetic Heart Valve

Although aortic resection and end-to-end anastomosis are applied to repair ascending aortic aneurysm, there is a lack of information on the late risk of post-operative complications, such as aortic dissection and aneurysmal re-dilatation. It is recognized that altered hemodynamic forces exerted on an aortic wall play an important role on dissection and aneurysm formation. We present a case in which the hemodynamic forces were investigated prior and after repair of an ascending aorta treated by resection with end-to-end anastomosis and a bioprosthetic heart valve. Post-operative wall shear stress was redistributed uniformly along the vessel circumference, and this may suggest a reduced risk of complications near aortic root, but not exclude the re-dilatation of the ascending aorta.

Developing an Analogue Residual Limb for Comparative DVC Analysis of Transtibial Prosthetic Socket Designs

Personalised prosthetic sockets are fabricated by expert clinicians in a skill- and experience-based process, with research providing tools to support evidence-based practice. We propose that digital volume correlation (DVC) may offer a deeper understanding of load transfer from prosthetic sockets into the residual limb, and tissue injury risk. This study’s aim was to develop a transtibial amputated limb analogue for volumetric strain estimation using DVC, evaluating its ability to distinguish between socket designs. A soft tissue analogue material was developed, comprising silicone elastomer and sand particles as fiducial markers for image correlation. The material was cast to form an analogue residual limb informed by an MRI scan of a person with transtibial amputation, for whom two polymer check sockets were produced by an expert prosthetist. The model was micro-CT scanned according to (i) an unloaded noise study protocol and (ii) a case study comparison between the two socket designs, loaded to represent two-legged stance. The scans were reconstructed to give 108 µm voxels. The DVC noise study indicated a 64 vx subvolume and 50% overlap, giving better than 0.32% strain sensitivity, and ~3.5 mm spatial resolution of strain. Strain fields induced by the loaded sockets indicated tensile, compressive and shear strain magnitudes in the order of 10%, with a high signal:noise ratio enabling distinction between the two socket designs. DVC may not be applicable for socket design in the clinical setting, but does offer critical 3D strain information from which existing in vitro and in silico tools can be compared and validated to support the design and manufacture of prosthetic sockets, and enhance the biomechanical understanding of the load transfer between the limb and the prosthesis.

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.

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.

Statistical Shape Analysis of Ascending Thoracic Aortic Aneurysm: Correlation between Shape and Biomechanical Descriptors

An ascending thoracic aortic aneurysm (ATAA) is a heterogeneous disease showing different patterns of aortic dilatation and valve morphologies, each with distinct clinical course. This study aimed to explore the aortic morphology and the associations between shape and function in a population of ATAA, while further assessing novel risk models of aortic surgery not based on aortic size. Shape variability of n = 106 patients with ATAA and different valve morphologies (i.e., bicuspid versus tricuspid aortic valve) was estimated by statistical shape analysis (SSA) to compute a mean aortic shape and its deformation. Once the computational atlas was built, principal component analysis (PCA) allowed to reduce the complex ATAA anatomy to a few shape modes, which were correlated to shear stress and aortic strain, as determined by computational analysis. Findings demonstrated that shape modes are associated to specific morphological features of aneurysmal aorta as the vessel tortuosity and local bulging of the ATAA. A predictive model, built with principal shape modes of the ATAA wall, achieved better performance in stratifying surgically operated ATAAs versus monitored ATAAs, with respect to a baseline model using the maximum aortic diameter. Using current imaging resources, this study demonstrated the potential of SSA to investigate the association between shape and function in ATAAs, with the goal of developing a personalized approach for the treatment of the severity of aneurysmal aorta.

Shear Stress and Aortic Strain Associations With Biomarkers of Ascending Thoracic Aortic Aneurysm

BACKGROUND

This study aims to investigate the association of wall shear stress (WSS) and aortic strain with circulating biomarkers including matrix metalloproteinases (MMP), tissue inhibitors of metalloproteinase (TIMP), and exosomal level of microRNA (miRNA) in ascending aortic aneurysms of patients with bicuspid or tricuspid aortic valve.

METHODS

A total of 76 variables from 125 patients with ascending aortic aneurysms were collected from (1) blood plasma to measure plasma levels of miRNAs and protein activity; (2) computational flow analysis to estimate peak systolic WSS and time-average WSS (TAWSS); and (3) imaging analysis of computed tomography angiography to determine aortic wall strain. Principal component analysis followed by logistic regression allowed the development of a predictive model of aortic surgery by combining biomechanical descriptors and biomarkers.

RESULTS

The protein activity of MMP-1, TIMP-1, and MMP-2 was positively correlated to the systolic WSS and TAWSS observed in the proximal ascending aorta (eg, R = 0.52, P < .001, for MMP-1 with TAWSS) where local maxima of WSS were found. For bicuspid patients, aortic wall strain was associated with miR-26a (R = 0.55, P = .041) and miR-320a (R = 0.69, P < .001), which shows a significant difference between bicuspid and tricuspid patients. Receiver-operating characteristics curves revealed that the combination of WSS, MMP-1, TIMP-1, and MMP-12 is predictive of aortic surgery (area under the curve 0.898).

CONCLUSIONS

Increased flow-based and structural descriptors of ascending aortic aneurysms are associated with high levels of circulating biomarkers, implicating adverse vascular remodeling in the dilated aorta by mechanotransduction. A combination of shear stress and circulating biomarkers has the potential to improve the decision-making process for ascending aortic aneurysms to a highly individualized level.

Pre-Operative Modeling of Transcatheter Mitral Valve Replacement in a Surgical Heart Valve Bioprosthesis

Obstruction of the left ventricular outflow tract (LVOT) is a common complication of transcatheter mitral valve replacement (TMVR). This procedure can determine an elongation of an LVOT (namely, the neo-LVOT), ultimately portending hemodynamic impairment and patient death. This study aimed to understand the biomechanical implications of LVOT obstruction in a patient who underwent TMVR using a transcatheter heart valve (THV) to repair a failed bioprosthetic heart valve. We first reconstructed the heart anatomy and the bioprosthetic heart valve to virtually implant a computer-aided-design (CAD) model of THV and evaluate the neo-LVOT area. A numerical simulation of THV deployment was then developed to assess the anchorage of the THV to the bioprosthetic heart valve as well as the resulting Von Mises stress at the mitral annulus and the contract pressure among implanted bioprostheses. Quantification of neo-LVOT and THV deployment may facilitate more accurate predictions of the LVOT obstruction in TMVR and help clinicians in the optimal choice of the THV size.

Computational modeling of bicuspid aortopathy: Towards personalized risk strategies

This paper describes current advances on the application of in-silico for the understanding of bicuspid aortopathy and future perspectives of this technology on routine clinical care. This includes the impact that artificial intelligence can provide to develop computer-based clinical decision support system and that wearable sensors can offer to remotely monitor high-risk bicuspid aortic valve (BAV) patients. First, we discussed the benefit of computational modeling by providing tangible examples of in-silico software products based on computational fluid-dynamic (CFD) and finite-element method (FEM) that are currently transforming the way we diagnose and treat cardiovascular diseases. Then, we presented recent findings on computational hemodynamic and structural mechanics of BAV to highlight the potentiality of patient-specific metrics (not-based on aortic size) to support the clinical-decision making process of BAV-associated aneurysms. Examples of BAV-related personalized healthcare solutions are illustrated.

Three-dimensional thoracic aorta principal strain analysis from routine ECG-gated computerized tomography: feasibility in patients undergoing transcatheter aortic valve replacement

BACKGROUND

Functional impairment of the aorta is a recognized complication of aortic and aortic valve disease. Aortic strain measurement provides effective quantification of mechanical aortic function, and 3-dimenional (3D) approaches may be desirable for serial evaluation. Computerized tomographic angiography (CTA) is routinely performed for various clinical indications, and offers the unique potential to study 3D aortic deformation. We sought to investigate the feasibility of performing 3D aortic strain analysis in a candidate population of patients undergoing transcatheter aortic valve replacement (TAVR).

METHODS

Twenty-one patients with severe aortic valve stenosis (AS) referred for TAVR underwent ECG-gated CTA and echocardiography. CTA images were analyzed using a 3D feature-tracking based technique to construct a dynamic aortic mesh model to perform peak principal strain amplitude (PPSA) analysis. Segmental strain values were correlated against clinical, hemodynamic and echocardiographic variables. Reproducibility analysis was performed.

RESULTS

The mean patient age was 81±6 years. Mean left ventricular ejection fraction was 52±14%, aortic valve area (AVA) 0.6±0.3 cm² and mean AS pressure gradient (MG) 44±11 mmHg. CTA-based 3D PPSA analysis was feasible in all subjects. Mean PPSA values for the global thoracic aorta, ascending aorta, aortic arch and descending aorta segments were 6.5±3.0, 10.2±6.0, 6.1±2.9 and 3.3±1.7%, respectively. 3D PSSA values demonstrated significantly more impairment with measures of worsening AS severity, including AVA and MG for the global thoracic aorta and ascending segment (p<0.001 for all). 3D PSSA was independently associated with AVA by multivariable modelling. Coefficients of variation for intra- and inter-observer variability were 5.8 and 7.2%, respectively.

CONCLUSIONS

Three-dimensional aortic PPSA analysis is clinically feasible from routine ECG-gated CTA. Appropriate reductions in PSSA were identified with increasing AS hemodynamic severity. Expanded study of 3D aortic PSSA for patients with various forms of aortic disease is warranted.