0
Guest Editorial

Special Issue: Spotlight on the Future of Cardiovascular Engineering:
Frontiers and Challenges in Cardiovascular Biomechanics
OPEN ACCESS

[+] Author and Article Information
Jeffrey W. Holmes

Departments of Biomedical Engineering
and Medicine and Robert M. Berne
Cardiovascular Research Center,
University of Virginia,
Charlottesville, VA 22908

Jessica E. Wagenseil

Department of Mechanical Engineering
and Materials Science,
Washington University,
St. Louis, MO 63130

J Biomech Eng 138(11), 110301 (Oct 21, 2016) (5 pages) Paper No: BIO-16-1388; doi: 10.1115/1.4034873 History: Received September 26, 2016; Revised September 29, 2016
FIGURES IN THIS ARTICLE

This special issue of JBME highlights frontiers and challenges in cardiovascular biomechanics. Nine young investigators have contributed to this special issue [19]. Experimental work ranges from individual smooth muscle cells to engineered cardiac tissue to whole arteries. Modeling work ranges from predicting cardiomyocyte responses to optogenetic stimuli to patient-specific ventricular mechanics. In this commentary, we both summarize the work showcased in this issue and discuss some of the current frontiers and challenges in the field. We highlight significant advances in growth and remodeling (G&R) and patient-specific computational models, as well as technical and economic considerations for the future utility of such models. We discuss experimental models that move beyond cell culture and the relevance of tissue biomechanics, especially when tissue-specific mechanics can be readily altered with genetic engineering. Finally, we touch on changes in the funding landscape for cardiovascular biomechanics.

Two decades ago, Rodriguez et al. introduced their volumetric finite growth framework, providing a convenient way to account for the effects of the growth or atrophy of solid tissues and organs on residual stress [10]. A decade later, Humphrey and Rajagopal introduced their constrained mixture formulation, which allows for turnover of multiple tissue constituents—each with its own evolving reference configuration—that together determine the mechanical properties of a remodeling tissue [11,12]. Building largely on these landmark papers, modeling growth and remodeling (G&R) has become one of the most exciting and rapidly growing areas in cardiovascular biomechanics. There is good reason for the excitement. Traditional finite-element models are invaluable in designing cardiovascular devices and therapies, allowing designers to predict how deploying a stent or inserting a prosthetic valve will alter blood flow, heart function, and other critical variables. Yet the body adapts to any intervention, so predicting the initial response to a therapy is only the beginning. G&R models represent a fundamentally new capability to predict the single thing that matters most to doctors and patients: long-term outcomes.

The field is now moving beyond a focus on simply formulating quantitative models of growth and beginning to apply these models to answer interesting questions and develop new treatments. For example, G&R models have been used to evaluate the effects of surgical removal [13] or reinforcement [14] of a myocardial infarct, to simulate venous adaptation to elevated pressures as occurs following coronary artery bypass grafting (CABG) [15] and to predict the maturation of tissue-engineered blood vessels in a bioreactor [16]. In this special issue, Hald et al. combined a G&R model with experimental measures of vascular smooth muscle cell contractility to investigate the effects of amyloid beta on cerebrovascular function [4]. They found that amyloid beta reduces smooth muscle cell contractility, and their G&R model predicts that the subsequent amyloid beta-dependent remodeling decreases vasodilatory capacity of the cerebrovascular vessels, which could play a role in neurodegeneration in Alzheimer's disease.

One challenge that will arise as growth models come into widespread use is to develop validation methods that can adequately test these models. At a superficial level, predictions of changes in mass or composition appear fairly easy to verify in animal studies where tissue can be harvested. Yet the mass of most tissues and constituents reflects a continuing balance between deposition and degradation; thus, many different combinations of deposition and degradation rates could yield the same change in mass. This simple idea can have important consequences when designing therapies. For example, trying to block a change in collagen content by inhibiting matrix metalloproteinases (MMPs) will be ineffective if that change arises primarily through decreased deposition rather than through increased degradation.

Measuring deposition and degradation rates of individual tissue components directly may thus be an important step in validating G&R models. To the best of our knowledge, there is relatively little published work in this area in the cardiovascular system. However, recent studies in tendon illustrate methods that may prove useful as well as the potential complexity of understanding turnover of even a single constituent in a single tissue. Langberg et al. used microdialysis to sample interstitial fluid from the region surrounding the Achilles tendon and found that the concentration of peptides generated during collagen assembly (PICP, carboxy-terminal propeptide of type I collagen) and degradation (ICTP, type I collagen carboxy-terminal telopeptide) increased significantly 72 h after a single 3 h distance run [17]. Several years later, the same group used an alternate method—measuring incorporation of amino acids labeled with stable isotopes—and found that 24 h after a single bout of intense exercise, the fractional collagen synthesis rate in patellar tendons increased from 1%/day to nearly 2%/day [18]. Both of these methods suggested high levels of collagen turnover in tendon, with dynamic responses to exercise. Yet in 2013, the same group assayed the levels of radioactive carbon generated during nuclear tests in the 1950 s and 1960 s in tendon tissue obtained at autopsy and found that there is essentially no turnover of carbon atoms in adult tendon after age 17, even over decades of adult life [19]. These apparently contradictory results may indicate that there are multiple pools of collagen in tendon that turn over at very different rates, and measurements of component turnover in cardiovascular tissues may reveal similar complexity.

Another exciting area for potential development is imaging changes in tissue volume, composition, and constituent turnover in vivo. For example, some groups have used high-speed X-ray of implanted metal markers—a decades-old technique for measuring beat-to-beat tissue mechanics in the heart—to quantify transmural gradients in tissue remodeling following myocardial infarction or imposition of hemodynamic overload in animals [2022]. Because it can provide real-time, coupled data on regional mechanics and G&R at multiple locations and time points, this approach could be very valuable in validating and calibrating growth models. Changes in residual stress and in the reference state of individual components predicted by G&R models are more difficult to validate, especially if the goal is to image these changes in intact tissues. Yet systems that integrate ex vivo loading of intact tissues with X-ray diffraction, second-harmonic generation (SHG), or other imaging methods could provide this information. For example, Liao et al. used small angle X-ray scattering to measure rotation and recruitment of collagen fibers during planar biaxial testing [23]; such studies could also reveal changes in collagen fiber reference state with remodeling. Similarly, Wang et al. used SHG to image collagen fiber uncrimping and rotation during inflation of carotid arteries [24]. SHG has also been used to image extracellular matrix (ECM) changes during G&R, including cardiac fibrosis following pressure overload [25] and collagen fiber remodeling in stented pulmonary arteries [26]. Pursell et al. used SHG in this special issue to relate structural changes in collagen fibers to mechanical changes in the pulmonary artery with induced hypertension [6]. They found that the structural and mechanical changes are different in the left and right pulmonary arteries and also different in the axial and circumferential directions, highlighting the complexity of ECM changes during G&R.

Patient-specific modeling is another exciting and potentially high-impact frontier in cardiovascular biomechanics. To date, much of the attention surrounding personalized medicine has focused on identifying factors such as genetic variations that affect the efficacy of different drugs available to treat a particular condition such as high blood pressure. Patient-specific modeling could offer analogous benefits in customizing surgical, interventional, and device-based therapies. For example, cardiac resynchronization therapy (CRT) uses electrical pulses delivered from pacemaker leads implanted in multiple locations to improve contractile performance in patients with heart failure. Cardiologists already use measures of electrical and mechanical synchrony to decide which patients are likely to benefit from CRT; however, many patients who meet the selection criteria do not respond, and many others achieve suboptimal results. A method to customize lead placement and pacemaker settings to individual patients; patient-specific modeling has the potential to provide exactly this information.

Reflecting the excitement surrounding this area, two of the nine papers in this special issue employ customized models of individual animals or patients. Wang et al. used animal-specific finite-element models to investigate the in vivo distribution of myocardial contractile forces across the left ventricular wall [8]. They found that models with transmural gradients in contractility provide the best fit to in vivo data and suggest that changes in the transmural contractility gradient may be an important consideration in evaluating left ventricular function in heart disease. Xi et al. used patient-specific computational models to investigate the effects of pulmonary hypertension on left ventricular mechanics and curvature [9]. They found that curvature changes, which can easily be measured in clinical images, may be useful in quantifying transseptal pressure gradients.

Constructing models of individual patients or animals has many important research applications. Among other advantages, such models help reveal the sources and implications of individual variability, something that until recently most researchers all but ignored. Yet as we seek to move patient-specific models (PSMs) into the clinic, there are important, systemic questions that must be thoughtfully addressed by the biomechanics community. Some of these are already under active discussion. For example, simulation run times are frequently reported, although concerns about whether long run times are practical for clinical application are frequently dismissed with a cursory assertion that computers will keep getting faster. Other issues, such as the parameter identifiability concerns that arise when models with many parameters are fitted to relatively sparse clinical data, are receiving more substantive attention [27,28]. The cardiovascular fluid biomechanics community has also organized an intriguing series of computational challenges that highlight the challenges of standardizing the construction and application of PSMs [2931].

However, in our view, one of the biggest practical limitations to clinical translation of patient-specific models is receiving far too little attention. Put bluntly, in the American medical system, time is quite literally money. In addition to the costs of obtaining the medical images and other datasets that provide inputs to PSMs, the time required to extract this information and customize and verify the model for each individual patient is an important factor in determining clinical viability. For pilot studies, this work is often done by graduate students funded by research grants; in routine clinical practice, it is unclear who would conduct this analysis, and who would pay for it. One way to avoid this dilemma is to only incorporate information into PSMs that can be obtained through fully automated analyses; this approach seems overly restrictive and neglects the fact that at a minimum some quality control of automated analyses will likely be required. A more interesting approach, that may have broader implications for bioengineering as a profession, might be to introduce a new category of clinical engineer, trained in imaging and modeling, who works closely with clinicians to construct and run PSMs while also providing critical quality control and the ability to troubleshoot and adapt the PSM as needed for individual cases. In the long term, the utility and impact of PSM will depend not only on the technical quality of the models but also on these broader economic questions. Thus, an important step we can take as a community is to provide the information needed to evaluate the economic viability of these models, by rigorously documenting the time and expense required to obtain each input to our PSMs, as well as the relative value of each specific input for model accuracy.

When contemplating the future of PSMs, one cardiovascular biomechanics success story that is currently unfolding provides both inspiration and potential lessons. Over the past decade, Taylor and colleagues developed methods for using PSMs of the coronary vasculature derived from computed tomography (CT) to predict fractional flow reserve (FFR), a measure of the degree to which a stenosis reduces maximal flow in response to infusion of a vasodilator during an invasive coronary catheterization procedure [32,33]. The model-predicted metric FFRCT performed well in clinical trials and is now approved for clinical use in the U.S. [3437]. Replacing an invasive procedure with a PSM in routine clinical practice is a major step forward for the field. In addition to hard work and technical quality, this success reflects smart choices that may inform other work: FFRCT relies primarily on patient-specific differences in coronary anatomy that can be determined accurately from imaging and requires very little patient-specific fitting of other model parameters [33].

Cell culture studies are an important tool in biology and bioengineering. It is widely recognized that one trade-off inherent in primary cell culture is that disrupting the physical and biochemical connections between cells and the surrounding ECM makes them easier to study but also fundamentally changes their biology. However, for those who work in cardiovascular biomechanics, the mechanical aspects of this trade-off present additional concerns. Removing cells that are normally subjected to cyclic stretch, pulsatile flow, and other dynamic mechanical stimuli and plating them on static surfaces raises major questions about how to interpret any subsequent measurement. As one example, isolated neonatal myocytes completely disassemble their sarcomeric structure and stop beating, then gradually reassemble it in a different geometry and begin beating again over the first couple of days. Does the response to stretch or shear of cells that have been cultured statically for days or weeks provide useful information on mechanotransduction, or simply reflect a return toward baseline from a severely artificial state? The demonstrated importance of ECM attachments and cytoskeletal tension to mechanotransduction certainly gives pause when interpreting experiments where both these factors have been altered.

Accordingly, the maturation of the field of tissue engineering has also made an important contribution to cardiovascular cell mechanics, by providing a greater range of model systems for studying cell behavior in 3D environments, incorporating relevant features such as cell alignment and ECM contacts. Recently, Shamir and Ewald reviewed 3D cell culture and discussed considerations for cell types, ECM material, and culture geometry for a variety of tissue types and diseases [38]. In this special issue, Rayner and Zheng reviewed engineered microvessels as a 3D platform to study human disease [7]. The microvessel platform allows precise control of factors such as ECM material, cell types, flow rates, pressures, and biochemical environment to study endothelial–blood interactions, endothelial–perivascular interactions, angiogenesis, and tumor biology. Also in this special issue, Drew et al. used engineered cardiac tissue to investigate self-assembly and force development in cardiomyocytes [3]. They found that self-assembly and force generation are optimized under similar conditions and that the organizational structure of various cytoskeletal elements is tightly correlated.

To replace, treat, or model cardiovascular tissue, the in vivo loading conditions and resulting deformations must be fully characterized. In complex geometries, such as the heart valves, in vitro experimental approaches have evolved to better reproduce the in vivo state. Early testing of heart valve mechanics involved removing the valves and individually evaluating mechanical behavior of the leaflets and chordae tendinae [39]. Later testing approaches involved keeping the valves intact, but rigidly restricting the valve annulus for mounting in the test system [40]. In this special issue, Amini Khoiy et al. measured surface strains on the tricuspid valve septal leaflets in an intact beating heart [2]. Their experimental setup provides a platform for testing the effects of valve lesions, valve repair procedures, and validating combined valve/ventricle computational models.

In vitro testing of cardiovascular tissues requires physiologic flow and pressure waveforms that are often difficult to reproduce in benchtop experiments. Custom systems that can reproduce these waveforms have been used to study a range of problems, from evaluating the effect of transcatheter aortic valve positioning on aortic root hemodynamics [41] to assessing potential wave pumping effects in the human aorta [42]. In this special issue, Mechoor et al. presented a low cost, fully programmable pulsatile flow pump capable of producing cyclic, physiologic waveforms [5]. The pump is controlled by commercial software that can be integrated with computational programs, so that a large number of flow waveforms can be tested in an automated framework.

The combination of more sophisticated experimental approaches and imaging of individual tissue components under load have provided a wealth of knowledge about how individual components contribute to the overall mechanical behavior of the composite cardiovascular tissue. Microstructurally motivated models incorporating this information have been used to predict how structural changes in collagen fiber undulation [43] or lack of specific ECM proteins [44] alter arterial mechanical behavior, as well as how collagen fibril kinematics contribute to the mechanical properties of pericardium [45] and heart valves [46]. Yet important work remains in order to achieve the long-standing goal of quantitatively relating structure and function: there are few if any published examples where microstructurally motivated models predicted the mechanical behavior of a cardiovascular tissue based on measured composition and structure alone.

Genetic modification of animals has opened new avenues of investigation in cardiovascular biomechanics. For the first time, the effects of specific changes in passive or active tissue components can be determined and related to changes that may occur in human genetic disease. With the advent of CRISPR-Cas9 gene editing, new mouse models of human disease are being created at an exponential pace. Such models offer intriguing opportunities for dissecting structure–function relationships in cardiovascular tissues. For example, genetically modified mice have been used to determine the effects of the complete absence of [47] or reduction in amounts of [48] elastin on arterial biomechanics at different stages of development, and biomechanical phenotyping of a collection of mouse models representing diseases from thoracic aortic aneurysms to muscular dystrophy to hypertension has been used to better understand contributions of different wall components to arterial mechanics [49]. In this special issue, Aboelkassem and Campbell [1] developed a computational model of a cardiomyocyte that includes light-activated cation channels. They found that optomechanical stimulation can be used to probe dynamic cardiomyocyte behavior without altering its intrinsic properties, predictions that can be tested by generating cardiomyocytes with genetic modification of the channel components.

Mice are the most tractable animal model for genetic manipulations, however, caution must be used in applying data from mouse studies to human disease. The small size of mouse cardiovascular tissues often requires developing new measurement and testing techniques and devices. Additionally, the heart rate of mice is 5–10 times faster than that of humans. This limits temporal resolution of many in vivo imaging modalities and raises questions about differences in time-dependent behaviors, such as pulse wave reflections and viscoelasticity. Mice live about 2.5 yrs, while humans live about 80 yrs. Due to the difference in longevity and despite the differences in heart rate, a human heart will beat about 2.5 × 109 times in a lifetime, compared to only 700 × 106 beats for a mouse [50]. Human cardiovascular disease predominantly affects aging individuals, while most of the research on corresponding mouse models is done at about 3–6 months of age, or the equivalent of a 20–30 yr old human [51]. Often for convenience or economic reasons, sex differences are ignored in most mouse studies, despite clear sex differences in the incidence and disease progression of many cardiovascular pathologies. In light of this trend, the NIH began requiring funded researchers to consider sex as a biologic variable in research design, analyses, and reporting of vertebrate animal and human studies starting in 2015 [52]. Despite these limitations, genetically modified mice represent an important tool in cardiovascular biomechanics.

Reading the papers in this special issue or reviewing recent work in any of the areas discussed here gives a clear sense that cardiovascular biomechanics is enjoying a period of rejuvenation. Exciting new modeling and experimental approaches are producing important insights into cardiovascular health and disease, revealing and enabling new therapeutic opportunities. Furthermore, many young investigators are entering the field and doing innovative, creative science. To some extent, this excitement is reflected in the funding landscape: the American Heart Association (AHA) offers tremendous support to the field of cardiovascular bioengineering and particularly to trainees and young faculty through predoctoral and postdoctoral fellowships, the Scientist Development Grant, and the Beginning Grant-in-Aid; the Biomechanics and Mechanobiology (BMMB) program within the Civil, Mechanical, and Manufacturing Innovation Division at the National Science Foundation (NSF) is funding innovative G&R models in many tissues and organs including the cardiovascular system; and the Modeling and Analysis of Biological Systems (MABS) Study Section at the National Institutes of Health (NIH) is providing high-quality review of investigator-initiated modeling proposals. However, against these positives, trends at the largest funder of cardiovascular research—the National Heart Lung and Blood Institute at the National Institutes of Health—are more concerning. In February 2015, NHLBI ended its participation in the Bioengineering Research Grants (BRG) program [53]; in the prior fiscal year, this program accounted for 26% (28 out of 108) of all the active NHLBI R01 grants to principal investigators in engineering departments.1 Similarly, over the past 3 yrs NHLBI has ended its participation in Bioengineering Research Partnerships (BRPs) and Exploratory Bioengineering Research Grants (EBRGs) and scaled back its commitment to the Multiscale Modeling U01 program. While these actions were taken as part of an overall strategy to focus resources on investigator-initiated R01s, they have the potential to disproportionally affect funding for cardiovascular bioengineering. Consistent with these trends, we note that three of the young faculty listed as senior authors in this special issue acknowledged grants from the AHA on which they serve as principal investigator, two acknowledged grants as PI from the NSF, and only one acknowledged a grant as PI from NHLBI—an R21 awarded under the discontinued EBRG program. It is essential that members of the cardiovascular biomechanics community take every opportunity to articulate to the public and to the relevant funding agencies the value and impact of exciting science such as that featured in this special issue.

Jeffrey W. Holmes
Departments of Biomedical Engineering
and Medicine and Robert M. Berne
Cardiovascular Research Center,
University of Virginia,
Charlottesville, VA 22908Jessica E. Wagenseil
Department of Mechanical Engineering
and Materials Science,
Washington University,
St. Louis, MO 63130

Aboelkassem, Y. , and Campbell, S. G. , 2016, “ Acute Optogenetic Modulation of Cardiac Twitch Dynamics Explored Through Modeling,” ASME J. Biomech. Eng. 138(11), p. 111005. [CrossRef]
Amini Khoiy, K. , Biswas, D. , Decker, T. N. , Asgarian, K. T. , Loth, F. , and Amini, R. , 2016, “ Surface Strains of Porcine Tricuspid Valve Septal Leaflets Measured in Ex-Vivo Beating Hearts,” ASME J. Biomech. Eng. 138(11), p. 111006. [CrossRef]
Drew, N. K. , Johnsen, N. E. , Core, J. Q. , and Grosberg, A. , 2016, “ Multi-Scale Characterization of Engineered Cardiac Tissue Architecture,” ASME J. Biomech. Eng. 138(11), p. 111003. [CrossRef]
Hald, E. S. , Timm, C. D. , and Alford, P. W. , 2016, “ Amyloid Beta Influences Vascular Smooth Muscle Contractility and Mechano-Adaptation,” ASME J. Biomech. Eng. 138(11), p. 111007. [CrossRef]
Mechoor, R. R. , Schmidt, T. , and Kung, E. , 2016, “ A Real-Time Programmable Pulsatile Flow Pump for In-Vitro Cardiovascular Experimentation,” ASME J. Biomech. Eng. 138(11), p. 111002. [CrossRef]
Pursell, E. R. , Vélez-Rendón, D. , and Valdez-Jasso, D. , 2016, “ Biaxial Properties of the Left and Right Pulmonary Arteries in a Monocrotaline Rat Animal Model of Pulmonary Arterial Hypertension,” ASME J. Biomech. Eng. 138(11), p. 111004. [CrossRef]
Rayner, S. , and Zheng, Y. , 2016, “ Engineered Microvessels for the Study of Human Disease,” ASME J. Biomech. Eng. 138(11), p. 110801. [CrossRef]
Wang, H. , Zhang, X. , Dorsey, S. M. , McGarvey, J. R. , Campbell, K. S. , Burdick, J. A. , Gorman, J. H. , 3rd, Pilla, J. J. , Gorman, R. C. , and Wenk, J. F. , 2016, “ Computational Investigation of Transmural Differences in Left Ventricular Contractility,” ASME J. Biomech. Eng. 138(11), p. 114501. [CrossRef]
Xi, C. , Latnie, C. , Zhao, X. , Tan, J. L. , Wall, S. T. , Genet, M. , Zhong, L. , and Lee, L. C. , 2016, “ Patient-Specific Computational Analysis of Ventricular Mechanics in Pulmonary Arterial Hypertension,” ASME J. Biomech. Eng. 138(11), p. 111001. [CrossRef]
Rodriguez, E. K. , Hoger, A. , and McCulloch, A. D. , 1994, “ Stress-Dependent Finite Growth in Soft Elastic Tissues,” J. Biomech., 27(4), pp. 455–467. [CrossRef] [PubMed]
Humphrey, J. D. , and Rajagopal, K. R. , 2002, “ A Constrained Mixture Model for Growth and Remodeling of Soft Tissues,” Math. Models Methods Appl. Sci., 12(3), pp. 407–430. [CrossRef]
Humphrey, J. D. , and Rajagopal, K. R. , 2003, “ A Constrained Mixture Model for Arterial Adaptations to a Sustained Step Change in Blood Flow,” Biomech. Model. Mechanobiol., 2(2), pp. 109–126. [CrossRef] [PubMed]
Klepach, D. , Lee, L. C. , Wenk, J. F. , Ratcliffe, M. B. , Zohdi, T. I. , Navia, J. A. , Kassab, G. S. , Kuhl, E. , and Guccione, J. M. , 2012, “ Growth and Remodeling of the Left Ventricle: A Case Study of Myocardial Infarction and Surgical Ventricular Restoration,” Mech. Res. Commun., 42, pp. 134–141. [CrossRef] [PubMed]
Lee, L. C. , Sundnes, J. , Genet, M. , Wenk, J. F. , and Wall, S. T. , 2016, “ An Integrated Electromechanical-Growth Heart Model for Simulating Cardiac Therapies,” Biomech. Model. Mechanobiol., 15(4), pp. 791–803. [CrossRef] [PubMed]
Ramachandra, A. B. , Sankaran, S. , Humphrey, J. D. , and Marsden, A. L. , 2015, “ Computational Simulation of the Adaptive Capacity of Vein Grafts in Response to Increased Pressure,” ASME J. Biomech. Eng., 137(3), p. 031009. [CrossRef]
Raykin, J. , Rachev, A. I. , and Gleason, R. L. , 2009, “ A Phenomenological Model for Mechanically Mediated Growth, Remodeling, Damage, and Plasticity of Gel-Derived Tissue Engineered Blood Vessels,” ASME J. Biomech. Eng., 131(10), p. 101016. [CrossRef]
Langberg, H. , Skovgaard, D. , Petersen, L. J. , Bulow, J. , and Kjaer, M. , 1999, “ Type I Collagen Synthesis and Degradation in Peritendinous Tissue After Exercise Determined by Microdialysis in Humans,” J. Physiol., 521(Pt. 1), pp. 299–306. [CrossRef] [PubMed]
Miller, B. F. , Olesen, J. L. , Hansen, M. , Døssing, S. , Crameri, R. M. , Welling, R. J. , Langberg, H. , Flyvbjerg, A. , Kjaer, M. , Babraj, J. A. , Smith, K. , and Rennie, M. J. , 2005, “ Coordinated Collagen and Muscle Protein Synthesis in Human Patella Tendon and Quadriceps Muscle After Exercise,” J. Physiol., 567(3), pp. 1021–1033. [CrossRef] [PubMed]
Heinemeier, K. M. , Schjerling, P. , Heinemeier, J. , Magnusson, S. P. , and Kjaer, M. , 2013, “ Lack of Tissue Renewal in Human Adult Achilles Tendon is Revealed by Nuclear Bomb (14)C,” FASEB J., 27(5), pp. 2074–2079. [CrossRef] [PubMed]
Omens, J. H. , and Covell, J. W. , 1991, “ Transmural Distribution of Myocardial Tissue Growth Induced by Volume-Overload Hypertrophy in the Dog,” Circulation, 84(3), pp. 1235–1245. [CrossRef] [PubMed]
Holmes, J. W. , Yamashita, H. , Waldman, L. K. , and Covell, J. W. , 1994, “ Scar Remodeling and Transmural Deformation After Infarction in the Pig,” Circulation, 90(1), pp. 411–420. [CrossRef] [PubMed]
Tsamis, A. , Cheng, A. , Nguyen, T. C. , Langer, F. , Miller, D. C. , and Kuhl, E. , 2012, “ Kinematics of Cardiac Growth: In Vivo Characterization of Growth Tensors and Strains,” J. Mech. Behav. Biomed. Mater., 8, pp. 8165–8177. [CrossRef]
Liao, J. , Yang, L. , Grashow, J. , and Sacks, M. S. , 2007, “ The Relation Between Collagen Fibril Kinematics and Mechanical Properties in the Mitral Valve Anterior Leaflet,” ASME J. Biomech. Eng., 129(1), pp. 78–87. [CrossRef]
Wang, R. , Brewster, L. P. , and Gleason, R. L. , 2013, “ In-Situ Characterization of the Uncrimping Process of Arterial Collagen Fibers Using Two-Photon Confocal Microscopy and Digital Image Correlation,” J. Biomech., 46(15), pp. 2726–2729. [CrossRef] [PubMed]
Martin, T. P. , Norris, G. , McConnell, G. , and Currie, S. , 2013, “ A Novel Approach for Assessing Cardiac Fibrosis Using Label-Free Second Harmonic Generation,” Int. J. Cardiovasc. Imaging, 29(8), pp. 1733–1740. [CrossRef] [PubMed]
Ghazanfari, S. , Driessen-Mol, A. , Hoerstrup, S. P. , Baaijens, F. P. T. , and Bouten, C. V. C. , 2016, “ Collagen Matrix Remodeling in Stented Pulmonary Arteries After Transapical Heart Valve Replacement,” Cells Tissues Organs, 201(3), pp. 159–169. [CrossRef] [PubMed]
Schiavazzi, D. E. , Baretta, A. , Pennati, G. , Hsia, T.-Y. , and Marsden, A. L. , 2016, “ Patient-Specific Parameter Estimation in Single-Ventricle Lumped Circulation Models Under Uncertainty,” Int. J. Numer. Method Biomed. Eng., 26(1), pp. 807–827.
Hadjicharalambous, M. , Chabiniok, R. , Asner, L. , Sammut, E. , Wong, J. , Carr-White, G. , Lee, J. , Razavi, R. , Smith, N. , and Nordsletten, D. , 2014, “ Analysis of Passive Cardiac Constitutive Laws for Parameter Estimation Using 3D Tagged MRI,” Biomech. Model. Mechanobiol., 14(4), pp. 807–828. [CrossRef] [PubMed]
Steinman, D. A. , Hoi, Y. , Fahy, P. , Morris, L. , Walsh, M. T. , Aristokleous, N. , Anayiotos, A. S. , Papaharilaou, Y. , Arzani, A. , Shadden, S. C. , Berg, P. , Janiga, G. , Bols, J. , Segers, P. , Bressloff, N. W. , Cibis, M. , Gijsen, F. H. , Cito, S. , Pallarés, J. , Browne, L. D. , Costelloe, J. A. , Lynch, A. G. , Degroote, J. , Vierendeels, J. , Fu, W. , Qiao, A. , Hodis, S. , Kallmes, D. F. , Kalsi, H. , Long, Q. , Kheyfets, V. O. , Finol, E. A. , Kono, K. , Malek, A. M. , Lauric, A. , Menon, P. G. , Pekkan, K. , Esmaily Moghadam, M. , Marsden, A. L. , Oshima, M. , Katagiri, K. , Peiffer, V. , Mohamied, Y. , Sherwin, S. J. , Schaller, J. , Goubergrits, L. , Usera, G. , Mendina, M. , Valen-Sendstad, K. , Habets, D. F. , Xiang, J. , Meng, H. , Yu, Y. , Karniadakis, G. E. , Shaffer, N. , and Loth, F. , 2013, “ Variability of Computational Fluid Dynamics Solutions for Pressure and Flow in a Giant Aneurysm: The ASME 2012 Summer Bioengineering Conference CFD Challenge,” ASME J. Biomech. Eng., 135(2), p. 021016. [CrossRef]
Berg, P. , Roloff, C. , Beuing, O. , Voss, S. , Sugiyama, S. , Aristokleous, N. , Anayiotos, A. S. , Ashton, N. , Revell, A. , Bressloff, N. W. , Brown, A. G. , Chung, B. J. , Cebral, J. R. , Copelli, G. , Fu, W. , Qiao, A. , Geers, A. J. , Hodis, S. , Dragomir-Daescu, D. , Nordahl, E. , Bora Suzen, Y. , Owais Khan, M. , Valen-Sendstad, K. , Kono, K. , Menon, P. G. , Albal, P. G. , Mierka, O. , Münster, R. , Morales, H. G. , Bonnefous, O. , Osman, J. , Goubergrits, L. , Pallares, J. , Cito, S. , Passalacqua, A. , Piskin, S. , Pekkan, K. , Ramalho, S. , Marques, N. , Sanchi, S. , Schumacher, K. R. , Sturgeon, J. , Švihlová, H. , Hron, J. , Usera, G. , Mendina, M. , Xiang, J. , Meng, H. , Steinman, D. A. , and Janiga, G. , 2015, “ The Computational Fluid Dynamics Rupture Challenge 2013—Phase II: Variability of Hemodynamic Simulations in Two Intracranial Aneurysms,” ASME J. Biomech. Eng., 137(12), p. 121008. [CrossRef]
FDA, “ Computational Fluid Dynamics Computational Round Robin #1 (Nozzle),” U.S. Food and Drug Administration, Washington, DC, accessed Sept. 27, 2016, https://fdacfd.nci.nih.gov/interlab_study_1_nozzle
Kim, H. J. , Vignon-Clementel, I. E. , Coogan, J. S. , Figueroa, C. A. , Jansen, K. E. , and Taylor, C. A. , 2010, “ Patient-Specific Modeling of Blood Flow and Pressure in Human Coronary Arteries,” Ann. Biomed. Eng., 38(10), pp. 3195–3209. [CrossRef] [PubMed]
Taylor, C. A. , Fonte, T. A. , and Min, J. K. , 2013, “ Computational Fluid Dynamics Applied to Cardiac Computed Tomography for Noninvasive Quantification of Fractional Flow Reserve: Scientific Basis,” J. Am. Coll. Cardiol., 61(22), pp. 2233–2241. [CrossRef] [PubMed]
Koo, B. K. , Erglis, A. , Doh, J. H. , Daniels, D. V. , Jegere, S. , Kim, H. S. , Dunning, A. , DeFrance, T. , Lansky, A. , Leipsic, J. , and Min, J. K. , 2011, “ Diagnosis of Ischemia-Causing Coronary Stenoses by Noninvasive Fractional Flow Reserve Computed From Coronary Computed Tomographic Angiograms: Results From the Prospective Multicenter DISCOVER-FLOW (Diagnosis of Ischemia-Causing Stenoses Obtained Via Noninvasive Flow Reserve) Study,” J. Am. Coll. Cardiol., 58(19), pp. 1989–1997. [CrossRef] [PubMed]
Min, J. K. , Leipsic, J. , Pencina, M. J. , Berman, D. S. , Koo, B. K. , van Mieghem, C. , Erglis, A. , Lin, F. Y. , Dunning, A. M. , Apruzzese, P. , Budoff, M. J. , Cole, J. H. , Jaffer, F. A. , Leon, M. B. , Malpeso, J. , Mancini, G. B. , Park, S. J. , Schwartz, R. S. , Shaw, L. J. , and Mauri, L. , 2012, “ Diagnostic Accuracy of Fractional Flow Reserve From Anatomic CT Angiography,” JAMA, 308(12), pp. 1237–1245. [CrossRef] [PubMed]
Hlatky, M. A. , De Bruyne, B. , Pontone, G. , Patel, M. R. , Norgaard, B. L. , Byrne, R. A. , Curzen, N. , Purcell, I. , Gutberlet, M. , Rioufol, G. , Hink, U. , Schuchlenz, H. W. , Feuchtner, G. , Gilard, M. , Andreini, D. , Jensen, J. M. , Hadamitzky, M. , Wilk, A. , Wang, F. , Rogers, C. , and Douglas, P. S. , and PLATFORM Investigators, 2015, “ Quality-of-Life and Economic Outcomes of Assessing Fractional Flow Reserve With Computed Tomography Angiography: PLATFORM,” J. Am. Coll. Cardiol., 66(21), pp. 2315–2323. [CrossRef] [PubMed]
Min, J. K. , 2016, “ Look Backwards But Live Forwards,” JACC Cardiovasc. Imaging (in press).
Shamir, E. R. , and Ewald, A. J. , 2014, “ Three-Dimensional Organotypic Culture: Experimental Models of Mammalian Biology and Disease,” Nat. Rev. Mol. Cell Biol., 15(10), pp. 647–664. [CrossRef] [PubMed]
Clark, R. E. , 1973, “ Stress-Strain Characteristics of Fresh and Frozen Human Aortic and Mitral Leaflets and Chordae Tendineae. Implications for Clinical Use,” J. Thorac. Cardiovasc. Surg., 66(2), pp. 202–208. [PubMed]
Sacks, M. S. , He, Z. , Baijens, L. , Wanant, S. , Shah, P. , Sugimoto, H. , and Yoganathan, A. P. , 2002, “ Surface Strains in the Anterior Leaflet of the Functioning Mitral Valve,” Ann. Biomed. Eng., 30(10), pp. 1281–1290. [CrossRef] [PubMed]
Groves, E. M. , Falahatpisheh, A. , Su, J. L. , and Kheradvar, A. , 2014, “ The Effects of Positioning of Transcatheter Aortic Valves on Fluid Dynamics of the Aortic Root,” ASAIO J., 60(5), pp. 545–552. [CrossRef] [PubMed]
Pahlevan, N. M. , and Gharib, M. , 2014, “ A Wave Dynamics Criterion for Optimization of Mammalian Cardiovascular System,” J. Biomech., 47(7), pp. 1727–1732. [CrossRef] [PubMed]
Zeinali-Davarani, S. , Wang, Y. , Chow, M.-J. , Turcotte, R. , and Zhang, Y. , 2015, “ Contribution of Collagen Fiber Undulation to Regional Biomechanical Properties Along Porcine Thoracic Aorta,” ASME J. Biomech. Eng., 137(5), p. 051001. [CrossRef]
Wan, W. , and Gleason, R. L. , 2013, “ Dysfunction in Elastic Fiber Formation in Fibulin-5 Null Mice Abrogates the Evolution in Mechanical Response of Carotid Arteries During Maturation,” Am. J. Physiol. Heart Circ. Physiol., 304(5), pp. H674–H686. [CrossRef] [PubMed]
Sacks, M. S. , 2003, “ Incorporation of Experimentally-Derived Fiber Orientation Into a Structural Constitutive Model for Planar Collagenous Tissues,” ASME J. Biomech. Eng., 125(2), pp. 280–287. [CrossRef]
Zhang, W. , Ayoub, S. , Liao, J. , and Sacks, M. S. , 2016, “ A Meso-Scale Layer-Specific Structural Constitutive Model of the Mitral Heart Valve Leaflets,” Acta Biomater., 32, pp. 238–255. [CrossRef] [PubMed]
Wagenseil, J. E. , Ciliberto, C. H. , Knutsen, R. H. , Levy, M. A. , Kovacs, A. , and Mecham, R. P. , 2009, “ Reduced Vessel Elasticity Alters Cardiovascular Structure and Function in Newborn Mice,” Circ. Res., 104(10), pp. 1217–1224. [CrossRef] [PubMed]
Le, V. P. , Knutsen, R. H. , Mecham, R. P. , and Wagenseil, J. E. , 2011, “ Decreased Aortic Diameter and Compliance Precedes Blood Pressure Increases in Postnatal Development of Elastin-Insufficient Mice,” Am. J. Physiol. Heart Circ. Physiol., 301(1), pp. H221–H229. [CrossRef] [PubMed]
Bersi, M. R. , Ferruzzi, J. , Eberth, J. F. , Gleason, R. L. , and Humphrey, J. D. , 2014, “ Consistent Biomechanical Phenotyping of Common Carotid Arteries From Seven Genetic, Pharmacological, and Surgical Mouse Models,” Ann. Biomed. Eng., 42(6), pp. 1207–1223. [CrossRef] [PubMed]
Hamlin, R. L. , and Altschuld, R. A. , 2011, “ Extrapolation From Mouse to Man,” Circ. Cardiovasc. Imaging, 4(1), pp. 2–4. [CrossRef] [PubMed]
Flurkey, K. , Currer, J. M. , and Harrison, D. E. , 2007, The Mouse in Biomedical Research, J. G. Fox , M. T. Davisson , F. W. Quimby , S. W. Barthold , C. E. Newcomer , and A. L. Smith , eds., 2nd ed., Academic Press, Burlington, VT, pp. 637–672.
NIH, 2015, “ NOT-OD-15-102: Consideration of Sex as a Biological Variable in NIH-Funded Research,” National Institutes of Health, Washington, DC, accessed Sept. 22, 2016, http://grants.nih.gov/grants/guide/notice-files/NOT-OD-15-102.html
NIH, 2015, “ NOT-HL-15-259: NHLBI No Longer Participates in PAR-13-137 Bioengineering Research Grants (BRG) (R01),” National Institutes of Health, Washington, DC, accessed Sept. 22, 2016, http://grants.nih.gov/grants/guide/notice-files/NOT-HL-15-259.html
Copyright © 2016 by ASME
View article in PDF format.

References

Aboelkassem, Y. , and Campbell, S. G. , 2016, “ Acute Optogenetic Modulation of Cardiac Twitch Dynamics Explored Through Modeling,” ASME J. Biomech. Eng. 138(11), p. 111005. [CrossRef]
Amini Khoiy, K. , Biswas, D. , Decker, T. N. , Asgarian, K. T. , Loth, F. , and Amini, R. , 2016, “ Surface Strains of Porcine Tricuspid Valve Septal Leaflets Measured in Ex-Vivo Beating Hearts,” ASME J. Biomech. Eng. 138(11), p. 111006. [CrossRef]
Drew, N. K. , Johnsen, N. E. , Core, J. Q. , and Grosberg, A. , 2016, “ Multi-Scale Characterization of Engineered Cardiac Tissue Architecture,” ASME J. Biomech. Eng. 138(11), p. 111003. [CrossRef]
Hald, E. S. , Timm, C. D. , and Alford, P. W. , 2016, “ Amyloid Beta Influences Vascular Smooth Muscle Contractility and Mechano-Adaptation,” ASME J. Biomech. Eng. 138(11), p. 111007. [CrossRef]
Mechoor, R. R. , Schmidt, T. , and Kung, E. , 2016, “ A Real-Time Programmable Pulsatile Flow Pump for In-Vitro Cardiovascular Experimentation,” ASME J. Biomech. Eng. 138(11), p. 111002. [CrossRef]
Pursell, E. R. , Vélez-Rendón, D. , and Valdez-Jasso, D. , 2016, “ Biaxial Properties of the Left and Right Pulmonary Arteries in a Monocrotaline Rat Animal Model of Pulmonary Arterial Hypertension,” ASME J. Biomech. Eng. 138(11), p. 111004. [CrossRef]
Rayner, S. , and Zheng, Y. , 2016, “ Engineered Microvessels for the Study of Human Disease,” ASME J. Biomech. Eng. 138(11), p. 110801. [CrossRef]
Wang, H. , Zhang, X. , Dorsey, S. M. , McGarvey, J. R. , Campbell, K. S. , Burdick, J. A. , Gorman, J. H. , 3rd, Pilla, J. J. , Gorman, R. C. , and Wenk, J. F. , 2016, “ Computational Investigation of Transmural Differences in Left Ventricular Contractility,” ASME J. Biomech. Eng. 138(11), p. 114501. [CrossRef]
Xi, C. , Latnie, C. , Zhao, X. , Tan, J. L. , Wall, S. T. , Genet, M. , Zhong, L. , and Lee, L. C. , 2016, “ Patient-Specific Computational Analysis of Ventricular Mechanics in Pulmonary Arterial Hypertension,” ASME J. Biomech. Eng. 138(11), p. 111001. [CrossRef]
Rodriguez, E. K. , Hoger, A. , and McCulloch, A. D. , 1994, “ Stress-Dependent Finite Growth in Soft Elastic Tissues,” J. Biomech., 27(4), pp. 455–467. [CrossRef] [PubMed]
Humphrey, J. D. , and Rajagopal, K. R. , 2002, “ A Constrained Mixture Model for Growth and Remodeling of Soft Tissues,” Math. Models Methods Appl. Sci., 12(3), pp. 407–430. [CrossRef]
Humphrey, J. D. , and Rajagopal, K. R. , 2003, “ A Constrained Mixture Model for Arterial Adaptations to a Sustained Step Change in Blood Flow,” Biomech. Model. Mechanobiol., 2(2), pp. 109–126. [CrossRef] [PubMed]
Klepach, D. , Lee, L. C. , Wenk, J. F. , Ratcliffe, M. B. , Zohdi, T. I. , Navia, J. A. , Kassab, G. S. , Kuhl, E. , and Guccione, J. M. , 2012, “ Growth and Remodeling of the Left Ventricle: A Case Study of Myocardial Infarction and Surgical Ventricular Restoration,” Mech. Res. Commun., 42, pp. 134–141. [CrossRef] [PubMed]
Lee, L. C. , Sundnes, J. , Genet, M. , Wenk, J. F. , and Wall, S. T. , 2016, “ An Integrated Electromechanical-Growth Heart Model for Simulating Cardiac Therapies,” Biomech. Model. Mechanobiol., 15(4), pp. 791–803. [CrossRef] [PubMed]
Ramachandra, A. B. , Sankaran, S. , Humphrey, J. D. , and Marsden, A. L. , 2015, “ Computational Simulation of the Adaptive Capacity of Vein Grafts in Response to Increased Pressure,” ASME J. Biomech. Eng., 137(3), p. 031009. [CrossRef]
Raykin, J. , Rachev, A. I. , and Gleason, R. L. , 2009, “ A Phenomenological Model for Mechanically Mediated Growth, Remodeling, Damage, and Plasticity of Gel-Derived Tissue Engineered Blood Vessels,” ASME J. Biomech. Eng., 131(10), p. 101016. [CrossRef]
Langberg, H. , Skovgaard, D. , Petersen, L. J. , Bulow, J. , and Kjaer, M. , 1999, “ Type I Collagen Synthesis and Degradation in Peritendinous Tissue After Exercise Determined by Microdialysis in Humans,” J. Physiol., 521(Pt. 1), pp. 299–306. [CrossRef] [PubMed]
Miller, B. F. , Olesen, J. L. , Hansen, M. , Døssing, S. , Crameri, R. M. , Welling, R. J. , Langberg, H. , Flyvbjerg, A. , Kjaer, M. , Babraj, J. A. , Smith, K. , and Rennie, M. J. , 2005, “ Coordinated Collagen and Muscle Protein Synthesis in Human Patella Tendon and Quadriceps Muscle After Exercise,” J. Physiol., 567(3), pp. 1021–1033. [CrossRef] [PubMed]
Heinemeier, K. M. , Schjerling, P. , Heinemeier, J. , Magnusson, S. P. , and Kjaer, M. , 2013, “ Lack of Tissue Renewal in Human Adult Achilles Tendon is Revealed by Nuclear Bomb (14)C,” FASEB J., 27(5), pp. 2074–2079. [CrossRef] [PubMed]
Omens, J. H. , and Covell, J. W. , 1991, “ Transmural Distribution of Myocardial Tissue Growth Induced by Volume-Overload Hypertrophy in the Dog,” Circulation, 84(3), pp. 1235–1245. [CrossRef] [PubMed]
Holmes, J. W. , Yamashita, H. , Waldman, L. K. , and Covell, J. W. , 1994, “ Scar Remodeling and Transmural Deformation After Infarction in the Pig,” Circulation, 90(1), pp. 411–420. [CrossRef] [PubMed]
Tsamis, A. , Cheng, A. , Nguyen, T. C. , Langer, F. , Miller, D. C. , and Kuhl, E. , 2012, “ Kinematics of Cardiac Growth: In Vivo Characterization of Growth Tensors and Strains,” J. Mech. Behav. Biomed. Mater., 8, pp. 8165–8177. [CrossRef]
Liao, J. , Yang, L. , Grashow, J. , and Sacks, M. S. , 2007, “ The Relation Between Collagen Fibril Kinematics and Mechanical Properties in the Mitral Valve Anterior Leaflet,” ASME J. Biomech. Eng., 129(1), pp. 78–87. [CrossRef]
Wang, R. , Brewster, L. P. , and Gleason, R. L. , 2013, “ In-Situ Characterization of the Uncrimping Process of Arterial Collagen Fibers Using Two-Photon Confocal Microscopy and Digital Image Correlation,” J. Biomech., 46(15), pp. 2726–2729. [CrossRef] [PubMed]
Martin, T. P. , Norris, G. , McConnell, G. , and Currie, S. , 2013, “ A Novel Approach for Assessing Cardiac Fibrosis Using Label-Free Second Harmonic Generation,” Int. J. Cardiovasc. Imaging, 29(8), pp. 1733–1740. [CrossRef] [PubMed]
Ghazanfari, S. , Driessen-Mol, A. , Hoerstrup, S. P. , Baaijens, F. P. T. , and Bouten, C. V. C. , 2016, “ Collagen Matrix Remodeling in Stented Pulmonary Arteries After Transapical Heart Valve Replacement,” Cells Tissues Organs, 201(3), pp. 159–169. [CrossRef] [PubMed]
Schiavazzi, D. E. , Baretta, A. , Pennati, G. , Hsia, T.-Y. , and Marsden, A. L. , 2016, “ Patient-Specific Parameter Estimation in Single-Ventricle Lumped Circulation Models Under Uncertainty,” Int. J. Numer. Method Biomed. Eng., 26(1), pp. 807–827.
Hadjicharalambous, M. , Chabiniok, R. , Asner, L. , Sammut, E. , Wong, J. , Carr-White, G. , Lee, J. , Razavi, R. , Smith, N. , and Nordsletten, D. , 2014, “ Analysis of Passive Cardiac Constitutive Laws for Parameter Estimation Using 3D Tagged MRI,” Biomech. Model. Mechanobiol., 14(4), pp. 807–828. [CrossRef] [PubMed]
Steinman, D. A. , Hoi, Y. , Fahy, P. , Morris, L. , Walsh, M. T. , Aristokleous, N. , Anayiotos, A. S. , Papaharilaou, Y. , Arzani, A. , Shadden, S. C. , Berg, P. , Janiga, G. , Bols, J. , Segers, P. , Bressloff, N. W. , Cibis, M. , Gijsen, F. H. , Cito, S. , Pallarés, J. , Browne, L. D. , Costelloe, J. A. , Lynch, A. G. , Degroote, J. , Vierendeels, J. , Fu, W. , Qiao, A. , Hodis, S. , Kallmes, D. F. , Kalsi, H. , Long, Q. , Kheyfets, V. O. , Finol, E. A. , Kono, K. , Malek, A. M. , Lauric, A. , Menon, P. G. , Pekkan, K. , Esmaily Moghadam, M. , Marsden, A. L. , Oshima, M. , Katagiri, K. , Peiffer, V. , Mohamied, Y. , Sherwin, S. J. , Schaller, J. , Goubergrits, L. , Usera, G. , Mendina, M. , Valen-Sendstad, K. , Habets, D. F. , Xiang, J. , Meng, H. , Yu, Y. , Karniadakis, G. E. , Shaffer, N. , and Loth, F. , 2013, “ Variability of Computational Fluid Dynamics Solutions for Pressure and Flow in a Giant Aneurysm: The ASME 2012 Summer Bioengineering Conference CFD Challenge,” ASME J. Biomech. Eng., 135(2), p. 021016. [CrossRef]
Berg, P. , Roloff, C. , Beuing, O. , Voss, S. , Sugiyama, S. , Aristokleous, N. , Anayiotos, A. S. , Ashton, N. , Revell, A. , Bressloff, N. W. , Brown, A. G. , Chung, B. J. , Cebral, J. R. , Copelli, G. , Fu, W. , Qiao, A. , Geers, A. J. , Hodis, S. , Dragomir-Daescu, D. , Nordahl, E. , Bora Suzen, Y. , Owais Khan, M. , Valen-Sendstad, K. , Kono, K. , Menon, P. G. , Albal, P. G. , Mierka, O. , Münster, R. , Morales, H. G. , Bonnefous, O. , Osman, J. , Goubergrits, L. , Pallares, J. , Cito, S. , Passalacqua, A. , Piskin, S. , Pekkan, K. , Ramalho, S. , Marques, N. , Sanchi, S. , Schumacher, K. R. , Sturgeon, J. , Švihlová, H. , Hron, J. , Usera, G. , Mendina, M. , Xiang, J. , Meng, H. , Steinman, D. A. , and Janiga, G. , 2015, “ The Computational Fluid Dynamics Rupture Challenge 2013—Phase II: Variability of Hemodynamic Simulations in Two Intracranial Aneurysms,” ASME J. Biomech. Eng., 137(12), p. 121008. [CrossRef]
FDA, “ Computational Fluid Dynamics Computational Round Robin #1 (Nozzle),” U.S. Food and Drug Administration, Washington, DC, accessed Sept. 27, 2016, https://fdacfd.nci.nih.gov/interlab_study_1_nozzle
Kim, H. J. , Vignon-Clementel, I. E. , Coogan, J. S. , Figueroa, C. A. , Jansen, K. E. , and Taylor, C. A. , 2010, “ Patient-Specific Modeling of Blood Flow and Pressure in Human Coronary Arteries,” Ann. Biomed. Eng., 38(10), pp. 3195–3209. [CrossRef] [PubMed]
Taylor, C. A. , Fonte, T. A. , and Min, J. K. , 2013, “ Computational Fluid Dynamics Applied to Cardiac Computed Tomography for Noninvasive Quantification of Fractional Flow Reserve: Scientific Basis,” J. Am. Coll. Cardiol., 61(22), pp. 2233–2241. [CrossRef] [PubMed]
Koo, B. K. , Erglis, A. , Doh, J. H. , Daniels, D. V. , Jegere, S. , Kim, H. S. , Dunning, A. , DeFrance, T. , Lansky, A. , Leipsic, J. , and Min, J. K. , 2011, “ Diagnosis of Ischemia-Causing Coronary Stenoses by Noninvasive Fractional Flow Reserve Computed From Coronary Computed Tomographic Angiograms: Results From the Prospective Multicenter DISCOVER-FLOW (Diagnosis of Ischemia-Causing Stenoses Obtained Via Noninvasive Flow Reserve) Study,” J. Am. Coll. Cardiol., 58(19), pp. 1989–1997. [CrossRef] [PubMed]
Min, J. K. , Leipsic, J. , Pencina, M. J. , Berman, D. S. , Koo, B. K. , van Mieghem, C. , Erglis, A. , Lin, F. Y. , Dunning, A. M. , Apruzzese, P. , Budoff, M. J. , Cole, J. H. , Jaffer, F. A. , Leon, M. B. , Malpeso, J. , Mancini, G. B. , Park, S. J. , Schwartz, R. S. , Shaw, L. J. , and Mauri, L. , 2012, “ Diagnostic Accuracy of Fractional Flow Reserve From Anatomic CT Angiography,” JAMA, 308(12), pp. 1237–1245. [CrossRef] [PubMed]
Hlatky, M. A. , De Bruyne, B. , Pontone, G. , Patel, M. R. , Norgaard, B. L. , Byrne, R. A. , Curzen, N. , Purcell, I. , Gutberlet, M. , Rioufol, G. , Hink, U. , Schuchlenz, H. W. , Feuchtner, G. , Gilard, M. , Andreini, D. , Jensen, J. M. , Hadamitzky, M. , Wilk, A. , Wang, F. , Rogers, C. , and Douglas, P. S. , and PLATFORM Investigators, 2015, “ Quality-of-Life and Economic Outcomes of Assessing Fractional Flow Reserve With Computed Tomography Angiography: PLATFORM,” J. Am. Coll. Cardiol., 66(21), pp. 2315–2323. [CrossRef] [PubMed]
Min, J. K. , 2016, “ Look Backwards But Live Forwards,” JACC Cardiovasc. Imaging (in press).
Shamir, E. R. , and Ewald, A. J. , 2014, “ Three-Dimensional Organotypic Culture: Experimental Models of Mammalian Biology and Disease,” Nat. Rev. Mol. Cell Biol., 15(10), pp. 647–664. [CrossRef] [PubMed]
Clark, R. E. , 1973, “ Stress-Strain Characteristics of Fresh and Frozen Human Aortic and Mitral Leaflets and Chordae Tendineae. Implications for Clinical Use,” J. Thorac. Cardiovasc. Surg., 66(2), pp. 202–208. [PubMed]
Sacks, M. S. , He, Z. , Baijens, L. , Wanant, S. , Shah, P. , Sugimoto, H. , and Yoganathan, A. P. , 2002, “ Surface Strains in the Anterior Leaflet of the Functioning Mitral Valve,” Ann. Biomed. Eng., 30(10), pp. 1281–1290. [CrossRef] [PubMed]
Groves, E. M. , Falahatpisheh, A. , Su, J. L. , and Kheradvar, A. , 2014, “ The Effects of Positioning of Transcatheter Aortic Valves on Fluid Dynamics of the Aortic Root,” ASAIO J., 60(5), pp. 545–552. [CrossRef] [PubMed]
Pahlevan, N. M. , and Gharib, M. , 2014, “ A Wave Dynamics Criterion for Optimization of Mammalian Cardiovascular System,” J. Biomech., 47(7), pp. 1727–1732. [CrossRef] [PubMed]
Zeinali-Davarani, S. , Wang, Y. , Chow, M.-J. , Turcotte, R. , and Zhang, Y. , 2015, “ Contribution of Collagen Fiber Undulation to Regional Biomechanical Properties Along Porcine Thoracic Aorta,” ASME J. Biomech. Eng., 137(5), p. 051001. [CrossRef]
Wan, W. , and Gleason, R. L. , 2013, “ Dysfunction in Elastic Fiber Formation in Fibulin-5 Null Mice Abrogates the Evolution in Mechanical Response of Carotid Arteries During Maturation,” Am. J. Physiol. Heart Circ. Physiol., 304(5), pp. H674–H686. [CrossRef] [PubMed]
Sacks, M. S. , 2003, “ Incorporation of Experimentally-Derived Fiber Orientation Into a Structural Constitutive Model for Planar Collagenous Tissues,” ASME J. Biomech. Eng., 125(2), pp. 280–287. [CrossRef]
Zhang, W. , Ayoub, S. , Liao, J. , and Sacks, M. S. , 2016, “ A Meso-Scale Layer-Specific Structural Constitutive Model of the Mitral Heart Valve Leaflets,” Acta Biomater., 32, pp. 238–255. [CrossRef] [PubMed]
Wagenseil, J. E. , Ciliberto, C. H. , Knutsen, R. H. , Levy, M. A. , Kovacs, A. , and Mecham, R. P. , 2009, “ Reduced Vessel Elasticity Alters Cardiovascular Structure and Function in Newborn Mice,” Circ. Res., 104(10), pp. 1217–1224. [CrossRef] [PubMed]
Le, V. P. , Knutsen, R. H. , Mecham, R. P. , and Wagenseil, J. E. , 2011, “ Decreased Aortic Diameter and Compliance Precedes Blood Pressure Increases in Postnatal Development of Elastin-Insufficient Mice,” Am. J. Physiol. Heart Circ. Physiol., 301(1), pp. H221–H229. [CrossRef] [PubMed]
Bersi, M. R. , Ferruzzi, J. , Eberth, J. F. , Gleason, R. L. , and Humphrey, J. D. , 2014, “ Consistent Biomechanical Phenotyping of Common Carotid Arteries From Seven Genetic, Pharmacological, and Surgical Mouse Models,” Ann. Biomed. Eng., 42(6), pp. 1207–1223. [CrossRef] [PubMed]
Hamlin, R. L. , and Altschuld, R. A. , 2011, “ Extrapolation From Mouse to Man,” Circ. Cardiovasc. Imaging, 4(1), pp. 2–4. [CrossRef] [PubMed]
Flurkey, K. , Currer, J. M. , and Harrison, D. E. , 2007, The Mouse in Biomedical Research, J. G. Fox , M. T. Davisson , F. W. Quimby , S. W. Barthold , C. E. Newcomer , and A. L. Smith , eds., 2nd ed., Academic Press, Burlington, VT, pp. 637–672.
NIH, 2015, “ NOT-OD-15-102: Consideration of Sex as a Biological Variable in NIH-Funded Research,” National Institutes of Health, Washington, DC, accessed Sept. 22, 2016, http://grants.nih.gov/grants/guide/notice-files/NOT-OD-15-102.html
NIH, 2015, “ NOT-HL-15-259: NHLBI No Longer Participates in PAR-13-137 Bioengineering Research Grants (BRG) (R01),” National Institutes of Health, Washington, DC, accessed Sept. 22, 2016, http://grants.nih.gov/grants/guide/notice-files/NOT-HL-15-259.html

Figures

Tables

Errata

Discussions

Some tools below are only available to our subscribers or users with an online account.

Related Content

Customize your page view by dragging and repositioning the boxes below.

Related Journal Articles
Related eBook Content
Topic Collections

Sorry! You do not have access to this content. For assistance or to subscribe, please contact us:

  • TELEPHONE: 1-800-843-2763 (Toll-free in the USA)
  • EMAIL: asmedigitalcollection@asme.org
Sign In