- Open Access
A model to determine the effect of collagen fiber alignment on heart function post myocardial infarction
© Voorhees and Han; licensee BioMed Central Ltd. 2014
Received: 17 November 2013
Accepted: 8 January 2014
Published: 22 January 2014
Adverse remodeling of the left ventricle (LV) following myocardial infarction (MI) leads to heart failure. Recent studies have shown that scar anisotropy is a determinant of cardiac function post-MI, however it remains unclear how changes in extracellular matrix (ECM) organization and structure contribute to changes in LV function. The objective of this study is to develop a model to identify potential mechanisms by which collagen structure and organization affect LV function post-MI.
A four-region, multi-scale, cylindrical model of the post-MI LV was developed. The mechanical properties of the infarct region are governed by a constitutive equation based on the uncrimping of collagen fibers. The parameters of this constitutive equation include collagen orientation, angular dispersion, fiber stiffness, crimp angle, and density. Parametric variation of these parameters was used to elucidate the relationship between collagen properties and LV function.
The mathematical model of the LV revealed several factors that influenced cardiac function post-MI. LV function was maximized when collagen fibers were aligned longitudinally. Increased collagen density was also found to improve stroke volume for longitudinal alignments while increased fiber stiffness decreased stroke volume for circumferential alignments.
The results suggest that cardiac function post-MI is best preserved through increased circumferential compliance. Further, this study identifies several collagen fiber-level mechanisms that could potentially regulate both infarct level and organ level mechanics. Improved understanding of the multi-scale relationships between the ECM and LV function will be beneficial in the design of new diagnostic and therapeutic technologies.
Coronary heart disease is the leading cause of death, accounting for over 400,000 lives in the United States every year . Blockage of the diseased coronary arteries leads to myocardial infarction (MI). Post-MI, the left ventricle (LV) undergoes a complex remodeling process that results in the formation of a scar or infarct. The increased stiffness and diminished contractility of the scar reduce LV function and can lead to heart failure . The decrease in LV pump function has been correlated to the size of the infarct, but infarct size alone cannot explain patient outcome . One key factor affecting LV function is the mechanical properties of the scar tissue. Increased infarct stiffness reduces inflation under systolic pressures, but also impairs filling of the LV during diastole . Therefore, it is imperative to elucidate the relationship between scar composition and mechanics in order to identify the properties that best preserve LV function.
Cardiac scar tissue is composed of collagen fibers, the primary determinant of the mechanical properties of the scar, as well as cells and other extracellular matrix proteins which are considered to be a ground substance . Thus, cardiac scar tissue demonstrates anisotropic, non-linear material behavior . Naturally, the density and alignment of collagen fibers affect the mechanical properties of scar tissue and thus the wall stress, with scar tissue demonstrating a high stiffness along the collagen fiber direction and a lower stiffness in the cross-fiber directions . Furthermore, it has been shown that collagen deposition during remodeling is highly controlled by the local stresses or strains in the tissue [7–9]. In arteries it has been shown that the angle of mean collagen fiber alignment adapts to optimize a tissue’s load-bearing ability which leads to fibers that are aligned in a direction in between the directions of the two principal stresses . In fact, recent studies by Fomovsky et al. have shown that mechanical cues are the primary determinant of collagen alignment . Therapeutic strategies that aim to limit infarct expansion by reducing local stress are currently under development [12, 13], including efforts to anisotropically reinforce the infarct region . Thus, it is of clinical interest to determine the relationship between collagen alignment, scar tissue mechanics, and LV function, in order to find a possible alignment that would better preserve LV function.
The objective of this study was to illustrate how collagen alignment affects LV function post-MI using a simple, thin-walled, cylindrical LV model with a scar region governed by a collagen fiber based constitutive equation. The cylindrical model is based on a concept, previously presented by Han et al.  and Oshinski et al. , to predict ejection fraction from MRI images, and the collagen fiber-based constitutive equation was based on the work of Grytz and Meschke . This simple multi-scale model allows us to focus on identifying the properties of the collagen matrix that primarily determine LV function post-MI.
where p is a Lagrange multiplier used to enforce incompressibility, [I] is the identity matrix, [F] is the deformation gradient, and [C] is the right Cauchy-Green deformation tensor.
where l s and t are the the sarcomere length and time during a cardiac cycle, respectively. ω is a function of the sarcomere length and time. σ0 is the peak contraction stress that depends on cellular calcium concentration and sarcomere length . The total stress in the healthy regions is calculated as the sum of the active stress which is non-zero solely along the direction of myocyte orientation, and the passive Cauchy stress tensor. The individual layers of the healthy tissue were assumed to have the same planar deformation (i.e. the transmural shear deformation was ignored).
Boundary conditions and equilibrium equations
where q θ and q z are the internal circumferential and longitudinal forces, P is the pressure, r is the deformed radius of the LV, and h is the deformed longitudinal height of the LV.
The spring system model was also used to describe which regions share displacements. The deformations are described by the components of the deformation gradient tensor, [F], as seen in Eq. (8). In the circumferential direction, regions RI and RIII are fixed in parallel so that they will always have the same circumferential deformation λ θ R . The same goes for regions RII and S which share circumferential deformation λ θ S . In the longitudinal direction, regions RI and RII are fixed in parallel so that they will share longitudinal deformation λ z R and shear deformation κ θz R . The same is true for regions RIII and S that share λ z S , and κ θz S . From Eq. (11) a full set of equilibrium equations can be derived which in conjunction with the constitutive equations can be solved numerically to determine the unknown displacements. A more complete derivation appears in the appendix.
Numerical solution methods
where, r is the radius of the LV, and l i , h i , and t i are the deformed length, height and thickness of each of the four regions RI, RII, RIII and S assuming that the deformed LV shape remains cylindrical. The total volume of the ventricle was determined by combining the component volumes. All numerical operations were carried out using Matlab (Mathworks, Natick, MA). An iterative solver based on Newton’s method with derivatives calculated by complex Taylor series expansion was used to simulate diastolic filling from 0 to an end diastolic pressure of 1.3 kPa (9.8 mm Hg) over 100 pressure increments. Convergence testing showed that end-diastolic volumes calculated using 50 pressure increments were only 0.1% less than those calculated using 100 increments. To simulate systole, 10 points on the end-systolic pressure volume relationship (ESPVR) curve were calculated using a Newton’s method solution technique to solve the force balance equations of Eq. (11) with the added time-varying myocyte contraction forces and an added isovolumic constraint so that pressure could be calculated as a function of time. Each systolic contraction was calculated using 100 time points. From this ESPVR the final systolic volume was taken to be the point on the curve at 13 kPa (98 mm Hg). Stroke volume (SV) and ejection fraction (EF) were then calculated from the end diastolic and systolic volumes.
Parameters of the example infarcted LV model
Total undeformed LV length
Total undeformed LV height
Undeformed thickness of healthy regions
Undeformed thickness of scar region
Mechanical properties of healthy regions
Fung constant (c)
Fung constant (bcc)
Fung constant (bxx)
Fung constant (bxc)
Number of layers
Myocyte fiber angle range
−50 to 50
Mechanical properties of scar region
Collagen crimp angle (θo)
Ratio of collagen helix diameter (Dcol) to filament diameter (dcol)
Young’s modulus of collagen (Ecol)
Neo-Hookean constant for ground material (cg)
Fiber alignment factor ( γ )
Volumetric collagen fiber fraction (ωcol)
We developed a cylindrical LV model that links the wall stress and pumping function of the dysfunctional LV to the density and alignment of collagen fibers in the scar tissue. Using this model, we examined the effect of collagen alignment and density on LV function post-MI. Our results demonstrate that the orientation of collagen fibers in the scar region of the myocardium has a large influence on the performance of the LV. It was found that longitudinal fiber alignment limits scar deformation during systole and maximizes LV performance.
The current model clearly demonstrates how the mechanics of the healthy contractile tissue are coupled to the properties of the collagen network in the scar region. The conclusion of longitudinal aligned fibers maximizing the LV function agrees with reports in the literature . This study also ellucidates the mechanism behind this improvement. The results suggest that rather than increased longitudinal stiffness improving function it is actually increased circumferential compliance that has the largest benefit. Increased circumferential compliance allows for the greatest diastolic filling, which in turn allows for greater contractile ability through the Frank-Starling mechanism. It is worth noting that an infarct that is too compliant may eventually lead to reduced systolic function as it inflates under systolic pressure. Examining the ESPVR’s in Figure 5C, it is seen that the longitudinal and circumferential alignment curves cross once the pressure exceeds about 16 kPa indicating that the increased circumferential compliance is leading to increased infarct bulging under high pressures.
While the parametric studies demonstrated that smaller scars preserve LV function they also revealed that for certain scar sizes, thinner scars increase LV function, a far less intuitive conclusion. This is due to the fact that thinner scars allow for more diastolic filling. There is a limit to this though, as scars with an infarct thickness to remote thickness ratio of less than 20% had a reduced SV due to increased systolic bulging. We do not endorse infarct thinning as an advantageous phenomenon as it increases the risk of cardiac rupture and infarct expansion.
The finding that increased collagen density has a positive effect on SV for longitudinally aligned fibers but very little effect for circumferential alignments is explained by the fact that as more collagen fibers replace the ground substance, the tissue becomes more compliant in the cross-fiber direction but stiffer in the direction of the fibers, which leads to increased diastolic filling. The importance of increased compliance is further seen in the findings that increasing the neo-Hookean constant decreased SV. One important takeaway from the parametric studies is that there appears to be an ideal stiffness at which diastolic filling is increased but systolic contraction is not greatly impaired. This is seen in the results for the infract thickness, Young’s modulus, and initial crimp angle. It is also interesting that the fitted values we found for the Young’s modulus and the crimp angle of porcine scar tissue appear to be near these ideal stiffness regions. The finding that increased fiber dispersion is beneficial for circumferentially aligned fibers but detrimental for longitudinally aligned fibers further highlights the importance of scar anisotropy in preserving ventricular function.
This work was strongly motivated by the work from the Holmes group . They studied the effect of scar anisotropy on LV function using a finite element model with mechanical properties of both the healthy and infarct regions described by a Fung-type constitutive equation. By comparing isotropic scars, longitudinally stiff scars, and circumferentially stiff scars, they found that SV would be maximized when the scar tissue was stiffer in the longitudinal direction than in the circumferential direction. Fomovsky’s work identified the direct role that infarct stiffness plays in controlling LV mechanics so as to inform the design of infarct restraint or stiffening therapies. Our current study developed a simple model to relate cardiac function to the features of the scar extracellular matrix rather than simply to stiffness through the use of a microstructure based constitutive model. The agreements that we see between our results and the results of the more complex finite element model presented by Fomovsky, in terms of increased longitudinal stiffness leading to increased SV give us confidence in our findings. A more recent study from the Holmes group has demonstrated that regional mechanics control collagen alignment . In complement, we have demonstrated here that collagen alignment affects the scar mechanical properties and thus LV function. The model we have presented could be a simple, easy to use, analytical tool for studying how initial collagen alignment controls remodeling and infarct expansion through mechanical feedback.
While this model has offered much insight into the importance of collagen alignment in determining heart function, there are several limitations to the study. This model assumes a thin walled geometry which ignores the effects of transmural variation in stresses. While the choice of the thin-walled assumption may create additional error in the calculated stresses, it was deemed acceptable since the focus of the study was on identifying potential mechanisms rather than conducting detailed local stress analysis. Based on the fact that infarct scars are thinner than normal LV wall, we chose to model the infarct as a single layer with a disperse fiber alignment. In reality the infarct is composed of several layers of collagen fibers with a mean fiber orientation that varies through the wall thickness similar to the helical pattern of myocyte alignment in normal myocardium. This simplification in itself should have little effect on the results due to the thin wall assumption of the model. Further, the choice of a single fiber distribution simplified the fitting of the scar properties to experimental data. This model also neglects the effects of ventricular torsion, which plays an important role in systolic ejection .
In addition, the assumption that adjacent regions share deformations along the shared edges allowed for a simple method of enforcing material compatibility. On the downside, this means that the stress field is non-continuous. This assumption was made to capture the phenomenon of infarct coupling whereby increasing the stiffness of the infarct reduces the deformation of the neighboring regions. In reality this effect is much smaller in regions that are far from the infarct region than in the small border zones that surround the infarct with properties that transition from scar to normal, making it possible that the magnitude of the fiber orientation effects are slightly overstated by the model. Given that the properties in the normal regions should be independent of the infarct properties we believe that the mechanisms through which scar architecture influence SV identified by this model are correct. In whole we believe that the model is not oversimplified as it matches behavior reported both by Fomovsky, (improved SV with increased longitudinal stiffness) and by Bogen (reduced stiffness allowing for greater diastolic filling and contraction for small infarcts) [4, 24].
The ground-up modeling approach we have chosen will allow us to refine the model in future work. While the collagen network is the primary determinant of infarct mechanics in the fibrotic scar, there are other structural proteins such as elastin and ECM modifications such as cross-linking that also play a role in LV mechanics . Refinements of the constitutive model to include the effects of these ECM features would be an interesting direction for future work. In addition, the ischemic LV is highly inhomogeneous and further effort to model the border zones as well as the transmural variations in infarct properties would add to the physiological relevance of the model. Another future direction could examine the mechanics of the LV during the ischemic or inflammatory phases occurring immediately following MI. During these phases mechanical properties of the infarct are controlled less by collagen and more by the turnover of existing ECM and intracellular structural proteins such as titin as well as edema. The development of the collagen network and in turn the mechanical properties of the scar following this initial period are highly controlled by the expression of growth factors such as TGF-β and proteases such as MMPs [3, 31]. Coupling the mechanical model to a dynamic model of protein regulation is one potential direction for future research. Another dynamic process that could be modeled in future work is the change in the orientation of the fibers that occur under load, which can dynamically change the mechanical properties of the tissue [32, 33].
The post-MI LV is a complex and dynamic physiological environment and much work is ongoing to create highly accurate models. The goal of our paper was not to improve upon the accuracy of the existing numerical models, but to instead clarify the role that collagen plays in controlling infarct mechanics. Compared to some of the complex finite element models found in the literature, the simplicity of this model is actually an advantage as it focuses attention to the most important variables. This makes it a quick tool for identifying the key structural determinants of LV function post-MI. For example, the current simplicity of the model may make it a good choice for investigating many of the temporal changes that occur post-MI.
In summary, our model results showed that EF and SV are both optimized when fibers are aligned longitudinally, which further supports findings in the literature. The finding of an ideal fiber angle may aid in the development of diagnostic indices and treatment strategies based on determining fiber angle (perhaps through angle-sensitive MRI ) and directing it towards an optimal orientation through controlled remodeling. Since the model is based on the mechanical properties of actual infarct tissue it will also serve as an excellent starting point for creating multi-scale models that explore the interactions of genome, proteome, cell, tissue, and organ level changes in the post-MI remodeling process.
A simple model of the post-MI LV was developed to demonstrate that a longitudinal alignment of collagen fibers in the scar region maximizes cardiac function. This improvement is the result of increased compliance in the circumferential direction which allows for greater diastolic filling and greater systolic contraction through the Frank-Starling mechanism.
1. Derivation of equilibrium equations
Now that relationships between force, stress, and deformation have been established for individual regions it is necessary to examine the four region model with forces and deformations being shared as described in Figure 3. In order to determine the magnitudes of the internal force vectors described in Eq. (11) it is necessary to examine the components of these vectors again.
This set of 6 independent equilibrium equations can be used in conjunction with the constitutive equations to solve for the 6 independent deformation components, .
This work was supported by CAREER award #0644646 from the National Science Foundation and grant HL095852 and HHSN 268201000036C (N01-HV-00244) for the San Antonio Cardiovascular Proteomics Center from the National Institutes of Health.
- Roger VL, Go AS, Lloyd-Jones DM, Adams RJ, Berry JD, Brown TM, Carnethon MR, Dai SF, de Simone G, Ford ES, et al: Executive summary: heart disease and stroke statistics-2011 update a report from the american heart association. Circulation. 2011, 123: 459-463.View ArticleGoogle Scholar
- Holmes JW, Borg TK, Covell JW: Structure and mechanics of healing myocardial infarcts. Annu Rev Biomed Eng. 2005, 7: 223-253. 10.1146/annurev.bioeng.7.060804.100453.View ArticlePubMedGoogle Scholar
- Zamilpa R, Lindsey ML: Extracellular matrix turnover and signaling during cardiac remodeling following MI: Causes and consequences. J Mol Cell Cardiol. 2010, 48: 558-563. 10.1016/j.yjmcc.2009.06.012.PubMed CentralView ArticlePubMedGoogle Scholar
- Bogen DK, Rabinowitz SA, Needleman A, McMahon TA, Abelmann WH: An analysis of the mechanical disadvantage of myocardial infarction in the canine left ventricle. Circ Res. 1980, 47: 728-741. 10.1161/01.RES.47.5.728.View ArticlePubMedGoogle Scholar
- Gupta KB, Ratcliffe MB, Fallert MA, Edmunds LH, Bogen DK: Changes in passive mechanical stiffness of myocardial tissue with aneurysm formation. Circulation. 1994, 89: 2315-2326. 10.1161/01.CIR.89.5.2315.View ArticlePubMedGoogle Scholar
- Holmes JW, Nunez JA, Covell JW: Functional implications of myocardial scar structure. Am J Physiol Heart Circ Physiol. 1997, 272: H2123-H2130.Google Scholar
- Miller AD, Tyagi SC: Mutation in collagen gene induces cardiomyopathy in transgenic mice. J Cell Biochem. 2002, 85: 259-267. 10.1002/jcb.10130.View ArticlePubMedGoogle Scholar
- Gleason RL, Humphrey JD: A mixture model of arterial growth and remodeling in hypertension: altered muscle tone and tissue turnover. J Vasc Res. 2004, 41: 352-363. 10.1159/000080699.View ArticlePubMedGoogle Scholar
- Lin J, Lopez EF, Jin YF, Van Remmen H, Bauch T, Han HC, Lindsey ML: Age-related cardiac muscle sarcopenia: combining experimental and mathematical modeling to identify mechanisms. Exp Gerontol. 2008, 43: 296-306. 10.1016/j.exger.2007.12.005.PubMed CentralView ArticlePubMedGoogle Scholar
- Hariton I, deBotton G, Gasser T, Holzapfel G: Stress-driven collagen fiber remodeling in arterial walls. Biomech Model Mechanobiol. 2007, 6: 163-175. 10.1007/s10237-006-0049-7.View ArticlePubMedGoogle Scholar
- Fomovsky GM, Rouillard AD, Holmes JW: Regional mechanics determine collagen fiber structure in healing myocardial infarcts. J Mol Cell Cardiol. 2012, 52: 1083-1090. 10.1016/j.yjmcc.2012.02.012.PubMed CentralView ArticlePubMedGoogle Scholar
- Gorman R, Jackson B, Burdick J, Gorman J: Infarct restraint to limit adverse ventricular remodeling. J Cardiovasc Transl Res. 2011, 4: 73-81. 10.1007/s12265-010-9244-0.PubMed CentralView ArticlePubMedGoogle Scholar
- Morita M, Eckert CE, Matsuzaki K, Noma M, Ryan LP, Burdick JA, Jackson BM, Gorman Iii JH, Sacks MS, Gorman RC: Modification of infarct material properties limits adverse ventricular remodeling. Ann Thorac Surg. 2011, 92: 617-624. 10.1016/j.athoracsur.2011.04.051.PubMed CentralView ArticlePubMedGoogle Scholar
- Fomovsky GM, Clark SA, Parker KM, Ailawadi G, Holmes JW: Anisotropic reinforcement of acute anteroapical infarcts improves pump function / clinical perspective. Circ Heart Fail. 2012, 5: 515-522. 10.1161/CIRCHEARTFAILURE.111.965731.PubMed CentralView ArticlePubMedGoogle Scholar
- Han HC, Oshinski JN, Ku DN, Pettigrew RI: A left ventricle model to predict post-revascularization ejection fraction based on cine magnetic resonance images. J Biomech Eng. 2002, 124: 52-55. 10.1115/1.1428555.View ArticlePubMedGoogle Scholar
- Oshinski JN, Han HC, Ku DN, Pettigrew RI: Quantitative prediction of improvement in cardiac function after revascularization with MR imaging and modeling: initial results. Radiology. 2001, 221: 515-522. 10.1148/radiol.2212010124.View ArticlePubMedGoogle Scholar
- Grytz R, Meschke G: Constitutive modeling of crimped collagen fibrils in soft tissues. J Mech Behav Biomed. 2009, 2: 522-533. 10.1016/j.jmbbm.2008.12.009.View ArticleGoogle Scholar
- Healy LJ, Jiang Y, Hsu EW: Quantitative comparison of myocardial fiber structure between mice, rabbit, and sheep using diffusion tensor cardiovascular magnetic resonance. J Cardiovasc Magn Reson. 2011, 13: 74-10.1186/1532-429X-13-74.PubMed CentralView ArticlePubMedGoogle Scholar
- Walker JC, Guccione JM, Jiang Y, Zhang P, Wallace AW, Hsu EW, Ratcliffe MB: Helical myofiber orientation after myocardial infarction and left ventricular surgical restoration in sheep. J Thorac Cardiovasc Surg. 2005, 129: 382-390. 10.1016/j.jtcvs.2004.06.006.View ArticlePubMedGoogle Scholar
- Streeter DD, Hanna WT: Engineering mechanics for successive states in canine left ventricular myocardium: II. Fiber angle and sarcomere length. Circ Res. 1973, 33: 656-664. 10.1161/01.RES.33.6.656.View ArticlePubMedGoogle Scholar
- Guccione JM, McCulloch AD, Waldman LK: Passive material properties of intact ventricular myocardium determined from a cylindrical model. J Biomech Eng. 1991, 113: 42-55. 10.1115/1.2894084.View ArticlePubMedGoogle Scholar
- Fung YC: Biomechanics: mechanical properties of living tissues. 1993, New York: SpringerView ArticleGoogle Scholar
- Sun W, Sacks MS: Finite element implementation of a generalized fung-elastic constitutive model for planar soft tissues. Biomech Model Mechanobiol. 2005, 4: 190-199. 10.1007/s10237-005-0075-x.View ArticlePubMedGoogle Scholar
- Fomovsky G, Macadangdang J, Ailawadi G, Holmes J: Model-based design of mechanical therapies for myocardial infarction. J Cardiovasc Transl Res. 2011, 4: 82-91. 10.1007/s12265-010-9241-3.PubMed CentralView ArticlePubMedGoogle Scholar
- Humphrey JD: Cardiovascular solid mechanics: cells, tissues, and organs. 2002, New York: SpringerView ArticleGoogle Scholar
- Guccione JM, McCulloch AD: Mechanics of active contraction in cardiac muscle: part I–constitutive relations for fiber stress that describe deactivation. J Biomech Eng. 1993, 115: 72-81. 10.1115/1.2895473.View ArticlePubMedGoogle Scholar
- Guccione JM, Waldman LK, McCulloch AD: Mechanics of active contraction in cardiac muscle: part II–cylindrical models of the systolic left ventricle. J Biomech Eng. 1993, 115: 82-90. 10.1115/1.2895474.View ArticlePubMedGoogle Scholar
- Grytz R, Meschke G: A computational remodeling approach to predict the physiological architecture of the collagen fibril network in corneo-scleral shells. Biomech Model Mechanobiol. 2010, 9: 225-235. 10.1007/s10237-009-0173-2.View ArticlePubMedGoogle Scholar
- Rankin JS, McHale PA, Arentzen CE, Ling D, Greenfield JC, Anderson RW: The three-dimensional dynamic geometry of the left ventricle in the conscious dog. Circ Res. 1976, 39: 304-313. 10.1161/01.RES.39.3.304.View ArticlePubMedGoogle Scholar
- Notomi Y, Srinath G, Shiota T, Martin-Miklovic MG, Beachler L, Howell K, Oryszak SJ, Deserranno DG, Freed AD, Greenberg NL, et al: Maturational and adaptive modulation of left ventricular torsional biomechanics: doppler tissue imaging observation from infancy to adulthood. Circulation. 2006, 113: 2534-2541. 10.1161/CIRCULATIONAHA.105.537639.View ArticlePubMedGoogle Scholar
- Jin Y, Han HC, Berger J, Dai Q, Lindsey ML: Combining experimental and mathematical modeling to reveal mechanisms of macrophage-dependent left ventricular remodeling. BMC Syst Biol. 2011, 5 (60): 1-14.Google Scholar
- Sellaro TL, Hildebrand D, Lu Q, Vyavahare N, Scott M, Sacks MS: Effects of collagen fiber orientation on the response of biologically derived soft tissue biomaterials to cyclic loading. J Biomed Mater Res A. 2007, 80A: 194-205. 10.1002/jbm.a.30871.View ArticleGoogle Scholar
- Liao J, Yang L, Grashow J, Sacks MS: Molecular orientation of collagen in intact planar connective tissues under biaxial stretch. Acta Biomater. 2005, 1: 45-54. 10.1016/j.actbio.2004.09.007.View ArticlePubMedGoogle Scholar
- Seidel T, Hammer N, Garnov N, Schneider G, Steinke H: An algorithm for the calculation of three-dimensional collagen fiber orientation in ligaments using angle-sensitive MRI. Magn Reson Med. 2013, 69: 1594-1602. 10.1002/mrm.24408.View ArticleGoogle Scholar
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