Complete spatiotemporal quantification of cardiac motion in mice through enhanced acquisition and super-resolution reconstruction.

The quantification of cardiac motion using cardiac magnetic resonance imaging (CMR) has shown promise as an early-stage marker for cardiovascular diseases. Despite the growing popularity of CMR-based myocardial strain calculations, measures of complete spatiotemporal strains (i.e., three-dimensional strains over the cardiac cycle) remain elusive. Complete spatiotemporal strain calculations are primarily hampered by poor spatial resolution, with the rapid motion of the cardiac wall also challenging the reproducibility of such strains. We hypothesize that a super-resolution reconstruction (SRR) framework that leverages combined image acquisitions at multiple orientations will enhance the reproducibility of complete spatiotemporal strain estimation. Two sets of CMR acquisitions were obtained for five wild-type mice, combining short-axis scans with radial and orthogonal long-axis scans. Super-resolution reconstruction, integrated with tissue classification, was performed to generate full four-dimensional (4D) images. The resulting enhanced and full 4D images enabled complete quantification of the motion in terms of 4D myocardial strains. Additionally, the effects of SRR in improving accurate strain measurements were evaluated using an in-silico heart phantom. The SRR framework revealed near isotropic spatial resolution, high structural similarity, and minimal loss of contrast, which led to overall improvements in strain accuracy. In essence, a comprehensive methodology was generated to quantify complete and reproducible myocardial deformation, aiding in the much-needed standardization of complete spatiotemporal strain calculations.

An image registration framework to estimate 3D myocardial strains from cine cardiac MRI in mice.

Accurate and efficient quantification of cardiac motion offers promising biomarkers for non-invasive diagnosis and prognosis of structural heart diseases. Cine cardiac magnetic resonance imaging remains one of the most advanced imaging tools to provide image acquisitions needed to assess and quantify in-vivo heart kinematics. The majority of cardiac motion studies are focused on human data, and there remains a need to develop and implement an image-registration pipeline to quantify full three-dimensional (3D) cardiac motion in mice where ideal image acquisition is challenged by the subject size and heart rate and the possibility of traditional tagged imaging is hampered. In this study, we used diffeomorphic image registration to estimate strains in the left ventricular wall in two wild-type mice and one diabetic mouse. Our pipeline resulted in a continuous and fully 3D strain map over one cardiac cycle. The estimation of 3D regional and transmural variations of strains is a critical step towards identifying mechanistic biomarkers for improved diagnosis and phenotyping of structural left heart diseases including heart failure with reduced or preserved ejection fraction.

In Silico Tissue Engineering: A Coupled Agent-Based Finite Element Approach.

Over the past two decades, the increase in prevalence of cardiovascular diseases and the limited availability of autologous blood vessels and saphenous vein grafts have motivated the development of tissue-engineered vascular grafts (TEVGs). However, compliance mismatch and poor mechanical properties of the TEVGs remain as two major issues that need to be addressed. Researchers have investigated the role of various culture conditions and mechanical conditioning in deposition and orientation of collagen fibers, which are the key structural components in the vascular wall; however, the intrinsic complexity of mechanobiological interactions demands implementing new engineering approaches that allow researchers to investigate various scenarios more efficiently. In this study, we utilized a coupled agent-based finite element analysis (AB-FEA) modeling approach to study the effect of various loading modes (uniaxial, biaxial, and equibiaxial), boundary conditions, stretch magnitudes, and growth factor concentrations on growth and remodeling of smooth muscle cell-populated TEVGs, with specific focus on collagen deposition and orientation. Our simulations (12 weeks of culture) showed that biaxial cyclic loading (and not uniaxial or equibiaxial) leads to alignment of collagen fibers in the physiological directions. Moreover, axial boundary conditions of the TEVG act as determinants of fiber orientations. Decreasing the serum concentration, from 10% to 5% or 1%, significantly decreased the growth and remodeling speed, but only affected the fiber orientation in the 1% serum case. In conclusion, in silico tissue engineering has the potential to evolve the future of tissue engineering, as it will allow researchers to conceptualize various interactions and investigate numerous scenarios with great speed. In this study, we were able to predict the orientation of collagen fibers in TEVGs using a coupled AB-FEA model in less than 8 h. Impact Statement Tissue-engineered vascular grafts (TEVGs) hold potential to replace the current gold standard of vascular grafting, saphenous vein grafts. However, developing TEVGs that mimic the mechanical performance of the native tissue remains a challenging task. We developed a computational model of the grafts' remodeling processes and studied the effects of various loading mechanisms and culture conditions on collagen fiber orientation, which is a key factor in mechanical performance of the grafts. We were able to predict the fiber orientations accurately and show that biaxial loading and axial boundary conditions are important factors in collagen fiber organization.

Mechanobiological model of arterial growth and remodeling.

A coupled agent-based model (ABM) and finite element analysis (FEA) computational framework is developed to study the interplay of bio-chemo-mechanical factors in blood vessels and their role in maintaining homeostasis. The agent-based model implements the power of REPAST Simphony libraries and adapts its environment for biological simulations. Coupling a continuum-level model (FEA) to a cellular-level model (ABM) has enabled this computational framework to capture the response of blood vessels to increased or decreased levels of growth factors, proteases and other signaling molecules (on the micro scale) as well as altered blood pressure. Performance of the model is assessed by simulating porcine left anterior descending artery under normotensive conditions and transient increases in blood pressure and by analyzing sensitivity of the model to variations in the rule parameters of the ABM. These simulations proved that the model is stable under normotensive conditions and can recover from transient increases in blood pressure. Sensitivity studies revealed that the model is most sensitive to variations in the concentration of growth factors that affect cellular proliferation and regulate extracellular matrix composition (mainly collagen).

QCT-based failure analysis of proximal femurs under various loading orientations.

In this paper, the variations of the failure strength and pattern of human proximal femur with loading orientation were analysed using a novel quantitative computed tomography (QCT)-based linear finite element (FE) method. The QCT images of 4 fresh-frozen femurs were directly converted into voxel-based finite element models for the analyses of the failure loads and patterns. A new geometrical reference system was used for the alignment of the mechanical loads on the femoral head. A new method was used for recognition and assortment of the high-risk elements using a strain energy-based measure. The FE results were validated with the experimental results of the same specimens and the results of similar case studies reported in the literature. The validated models were used for the computational investigation of the failure loads and patterns under 15 different loading conditions. A consistent variation of the failure loads and patterns was found for the 60 different analysed cases. Finally, it was shown that the proposed procedure can be used as a reliable tool for the failure analysis of proximal femurs, e.g. identification of the relevant loading directions for specific failure patterns, or determination of the loading conditions under which the proximal femurs are failure-prone.

Analysis of strength and failure pattern of human proximal femur using quantitative computed tomography (QCT)-based finite element method.

This paper presents a novel method for fast and reliable prediction of the failure strength of human proximal femur, using the quantitative computed tomography (QCT)-based linear finite element analysis (FEA). Ten fresh frozen human femora (age: 34±16) were QCT-scanned and the pertinent 3D voxel-based finite element models were constructed. A specially-designed holding frame was used to define and maintain a unique geometrical reference system for both FEA and in-vitro mechanical testing. The analyses and tests were carried out at 8 different loading orientations. A new scheme was developed for assortment of the element risk factor (defined as the ratio of the strain energy density to the yield strain energy for each element) and implemented for the prediction of the failure strength. The predicted and observed failure patterns were in correspondence, and the FEA predictions of the failure loads were in very good agreement with the experimental results (R2=0.86, slope=0.96, p<0.01). The average computational time was 5 min (on a regular desktop personal computer) for an average element number of 197,000. Noting that the run-time for a similar nonlinear model is about 8h, it was concluded that the proposed linear scheme is overwhelmingly efficient in terms of computational costs. Thus, it can efficiently be used to predict the femoral failure strength with the same accuracy of similar nonlinear models.