Structural finite-element analysis (FEA) has been widely used to study the biomechanics of human tissues and organs, as well as tissue–medical device interactions, and treatment strategies. However, patient-specific FEA models usually require complex procedures to set up and long computing times to obtain final simulation results, preventing prompt feedback to clinicians in time-sensitive clinical applications. In this study, by using machine learning techniques, we developed a deep learning (DL) model to directly estimate the stress distributions of the aorta. The DL model was designed and trained to take the input of FEA and directly output the aortic wall stress distributions, bypassing the FEA calculation process. The trained DL model is capable of predicting the stress distributions with average errors of 0.492% and 0.891% in the Von Mises stress distribution and peak Von Mises stress, respectively. This study marks, to our knowledge, the first study that demonstrates the feasibility and great potential of using the DL technique as a fast and accurate surrogate of FEA for stress analysis.

Geometric features of the aorta are linked to patient risk of rupture in the clinical decision to electively repair an ascending aortic aneurysm (AsAA). Previous approaches have focused on relationship between intuitive geometric features (e.g. diameter and curvature) and wall stress. This work investigates the feasibility of a machine learning approach to establish the linkages between shape features and FEA predicted AsAA rupture risk, and it may serve as a faster surrogate for FEA associated with long simulation time and numerical convergence issues. This method consists of four main steps: (1) constructing a statistical shape model (SSM) from clinical 3D CT images of AsAA patients; (2) generating a dataset of representative aneurysm shapes and obtaining FEA predicted risk scores defined as systolic pressure divided by rupture pressure (rupture is determined by a threshold criterion); (3) establishing relationship between shape features and risk by using classifiers and regressors; and (4) evaluating such relationship in cross validation. The results show that SSM parameters can be used as strong shape features to make predictions of risk scores consistent with FEA, which lead to an average risk classification accuracy of 95.58% by using support vector machine and an average regression error of 0.0332 by using support vector regression, while intuitive geometric features have relatively weak performance. Compared to FEA, this machine learning approach is magnitudes faster. In our future studies, material properties and inhomogeneous thickness will be incorporated into the models and learning algorithms, which may lead to a practical system for clinical applications.

Objectives Studies have shown that patients harboring bicuspid aortic valve (BAV) or bovine aortic arch (BAA) are more likely to develop ascending aortic aneurysm (AsAA) than the general population. A thorough quantification of the AsAA tissue properties for these patient groups may offer insight into the underlying mechanisms of AsAA development in these patients. Thus, the objective of this study was to investigate and compare the mechanical and microstructural properties of aortic tissues from AsAA patients with and without concomitant BAV or BAA. Materials and methods AsAA (n = 20), BAV (n = 20) and BAA (n = 15) human tissues were obtained from patients who underwent elective AsAA surgery. Planar biaxial and uniaxial failure tests were used to characterize the mechanical and failure properties of the tissues, respectively. Histological analysis was performed to detect the medial degenerative characteristics of aortic aneurysm. Individual layer thickness and composition were quantified for each patient group. Results The circumferential (CIRC) response of the BAV samples was stiffer than both AsAA (p = 0.473) and BAA (p = 0.152) tissues at low load. The BAV tissues were nearly isotropic while AsAA and BAA tissues were anisotropic. The areal strain of BAV samples were significantly less than AsAA (p = 0.041) and BAA (p = 0.004) tissues at a low load. The BAA samples were similar to the AsAA samples in both mechanical and failure properties. On the microstructural level, all samples displayed moderate medial degeneration characterized by elastin fragmentation, cell loss, mucoid accumulation and fibrosis. The ultimate tensile strength of BAV and BAA tissues were also found to decrease with age. Conclusions The BAV tissues were stiffer than both AsAA and BAA tissues, and the BAA tissues were similar to the AsAA tissues. The BAV samples were thinnest with less elastin than AsAA and BAA samples, which may attribute to the loss of extensibility at low load of these tissues. No apparent difference in failure mechanics among the tissue groups suggests that each of the patient groups may have a similar risk of rupture.

Objective Bovine pericardium (BP) has been identified as a choice biomaterial for the development of surgical bioprosthetic heart valves (BHV) and transcatheter aortic valves (TAV). Porcine pericardium (PP) and younger BP have been suggested as candidates TAV leaflet biomaterials for smaller-profile devices due to their reduced thickness; however, their mechanical and structural properties remain to be fully characterized. This study characterized the material properties of chemically treated thick (PPK) and thin (PPN) PP, as well as fetal (FBP), calf (CBP) and adult (ABP) BP tissues in order to better understand their mechanical behavior. Methods Planar biaxial testing and uniaxial failure testing methods were employed to quantify tissue mechanical responses and failure properties. Fiber characteristics were examined using histological analysis. Results ABP and CBP tissues were significantly stiffer and stronger than the younger FBP tissues. Histological analysis revealed a significantly larger concentration of thin immature collagen fibers in the FBP tissues than in the ABP and CBP tissues. While PP tissues were thinnest, they were stiffer and less extensible than the BP tissues. Conclusions Due to comparable mechanical properties but significantly reduced thickness, CBP tissue may be a more suitable material for TAV manufacturing than ABP tissue. FBP tissue, despite its reduced thickness and higher flexibility, was weaker and should be studied in more detail. Although PP tissues are the thinnest, they were least extensible and failed at earlier strain than BP tissues. The differences between PP and BP tissues should be further investigated and suggest that they should not be used interchangeably in the manufacturing of TAV.

Martin C, Sun W, Elefteriades J. Patient-specific finite element analysis of ascending aorta aneurysms.

Objectives Currently, percutaneous aortic valve (PAV) replacement devices are being investigated to treat aortic stenosis in patients deemed to be of too high a risk for conventional open-chest surgery. Successful PAV deployment and function are heavily reliant on the tissue–stent interaction. Many PAV feasibility trials have been conducted with porcine models under the assumption that these tissues are similar to human; however, this assumption may not be valid. The goal of this study was to characterize and compare the biomechanical properties of aged human and porcine aortic tissues. Methods The biaxial mechanical properties of the left coronary sinus, right coronary sinus, non-coronary sinus, and ascending aorta of eight aged human (90.1 ± 6.8 years) and 10 porcine (6–9 months) hearts were quantified. Tissue structure was analyzed via histological techniques. Results Aged human aortic tissues were significantly stiffer than the corresponding porcine tissues in both the circumferential and longitudinal directions (p < 0.001). In addition, the nearly linear stress–strain behavior of the porcine tissues, compared with the highly nonlinear response of the human tissues at a low strain range, suggested structural differences between the aortic tissues from these two species. Histological analysis revealed that porcine samples were composed of more elastin and less collagen fibers than the respective human samples. Conclusions Significant material and structural differences were observed between the human and porcine tissues, which raise questions on the validity of using porcine models to investigate the biomechanics involved in PAV intervention.

Aortic aneurysm is a leading cause of death in adults, often taking lives without any premonitory signs or symptoms. Adverse clinical outcomes of aortic aneurysm are preventable by elective surgical repair; however, identifying at-risk individuals is difficult. The objective of this study was to perform a predictive biomechanical analysis of ascending aortic aneurysm (AsAA) tissue to assess rupture risk on a patient-specific level. AsAA tissues, obtained intra-operatively from 50 patients, were subjected to biaxial mechanical and uniaxial failure tests to obtain their passive elastic mechanical properties. A novel analytical method was developed to predict the AsAA pressure-diameter response as well as the aortic wall yield and failure responses. Our results indicated that the mean predicted AsAA diameter at rupture was 5.6 ± 0.7 cm, and the associated blood pressure to induce rupture was 579.4 ± 214.8 mmHg. Statistical analysis showed significant positive correlation between aneurysm tissue compliance and predicted risk of rupture, where patients with a pressure-strain modulus ≥100 kPa may be nearly twice as likely to experience rupture than patients with more compliant aortic tissue. The mechanical analysis of pre-dissection patient tissue properties established in this study could predict the “future” onset of yielding and rupture in AsAA patients. The analysis results implicate decreased tissue compliance as a risk factor for AsAA rupture. The presented methods may serve as a basis for the development of a pre-operative planning tool for AsAA evaluation, a tool currently unavailable.

Aortic valve disease develops in an escalating fashion in elderly patients. Current treatments including total valve replacement and valve repair techniques are still suboptimal. A thorough understanding of the animal and human valve tissue properties, particularly their differences, is crucial for the establishment of preclinical animal models and strategies for evaluating new valve treatment techniques, such as transcatheter valve intervention and tissue engineered valves. The goal of this study was to characterize and compare the biomechanical properties and histological structure of healthy ovine, porcine, and human aortic valve leaflets. The biaxial mechanical properties of the aortic valve leaflets of 10 ovine (~1 year), 10 porcine (6–9 months), and 10 aged human (80.6 ± 8.34) hearts were quantified. Tissue microstructure was analyzed via histological techniques. Aged human aortic valve leaflets were significantly less compliant than both ovine and porcine leaflets, with the ovine leaflets being the most compliant. Histological analysis revealed structural differences between the species: the human and porcine leaflets contained more collagen and elastin than the ovine leaflets. Significant mechanical and structural differences in the aortic valve tissues of 6- to 9-month-old porcine models and 1-year-old ovine models with respect to those of aged humans, suggest that these animal models may not be representative of the typical patient undergoing aortic valve replacement.