Research Papers

A Novel Method for Quantifying Smooth Regional Variations in Myocardial Contractility Within an Infarcted Human Left Ventricle Based on Delay-Enhanced Magnetic Resonance Imaging

[+] Author and Article Information
Martin Genet

Marie-Curie International Outgoing Fellow
Surgery Department,
University of California at San Francisco,
San Francisco, CA 94122
Institute for Biomedical Engineering,
Zurich CH-8092, Switzerland
e-mail: genet@biomed.ee.ethz.ch

Lik Chuan Lee

Surgery Department,
University of California at San Francisco,
San Francisco, CA 94122
Mechanical Engineering Department,
Michigan State University,
East Lansing, MI 48824
e-mail: lclee@egr.msu.edu

Liang Ge

Surgery Department,
University of California at San Francisco,
San Francisco, CA 94122
e-mail: liang.ge@va.gov

Gabriel Acevedo-Bolton

Surgery Department,
University of California at San Francisco,
San Francisco, CA 94122
e-mail: gabriel.acevedo-bolton@ucsf.edu

Nick Jeung

Radiology Department,
University of California at San Francisco,
San Francisco, CA 94122
e-mail: nickjeung@gmail.com

Alastair Martin

Radiology Department,
University of California at San Francisco,
San Francisco, CA 94122
e-mail: alastair.martin@ucsf.edu

Neil Cambronero

Surgery Department,
University of California at San Francisco,
San Francisco, CA 94122
e-mail: neil.cambronero@ucsfmedctr.org

Andrew Boyle

Medicine Department,
University of California at San Francisco,
San Francisco, CA 94122
e-mail: aboyle@medicine.ucsf.edu

Yerem Yeghiazarians

Department of Medicine, Division of Cardiology,
Cardiovascular Research Institute,
Eli and Edythe Broad Center of Regeneration Medicing and Stem Cell Research,
University of California at San Francisco,
San Francisco, CA 94122
e-mail: Yerem.yeghiazarians@ucsf.edu

Sebastian Kozerke

Institute for Biomedical Engineering,
University and ETH Zurich,
Zurich CH-8092, Switzerland
e-mail: kozerke@biomed.ee.ethz.ch

Julius M. Guccione

Surgery Department, University of California at San Francisco,
San Francisco, CA 94143
e-mail: julius.guccione@ucsfmedctr.org

1Corresponding author.

Manuscript received November 21, 2014; final manuscript received May 11, 2015; published online June 16, 2015. Assoc. Editor: Thao (Vicky) Nguyen.

J Biomech Eng 137(8), 081009 (Aug 01, 2015) (8 pages) Paper No: BIO-14-1573; doi: 10.1115/1.4030667 History: Received November 21, 2014; Revised May 11, 2015; Online June 16, 2015

Heart failure is increasing at an alarming rate, making it a worldwide epidemic. As the population ages and life expectancy increases, this trend is not likely to change. Myocardial infarction (MI)-induced adverse left ventricular (LV) remodeling is responsible for nearly 70% of heart failure cases. The adverse remodeling process involves an extension of the border zone (BZ) adjacent to an MI, which is normally perfused but shows myofiber contractile dysfunction. To improve patient-specific modeling of cardiac mechanics, we sought to create a finite element model of the human LV with BZ and MI morphologies integrated directly from delayed-enhancement magnetic resonance (DE-MR) images. Instead of separating the LV into discrete regions (e.g., the MI, BZ, and remote regions) with each having a homogeneous myocardial material property, we assumed a functional relation between the DE-MR image pixel intensity and myocardial stiffness and contractility—we considered a linear variation of material properties as a function of DE-MR image pixel intensity, which is known to improve the accuracy of the model's response. The finite element model was then calibrated using measurements obtained from the same patient—namely, 3D strain measurements—using complementary spatial modulation of magnetization magnetic resonance (CSPAMM-MR) images. This led to an average circumferential strain error of 8.9% across all American Heart Association (AHA) segments. We demonstrate the utility of our method for quantifying smooth regional variations in myocardial contractility using cardiac DE-MR and CSPAMM-MR images acquired from a 78-yr-old woman who experienced an MI approximately 1 yr prior. We found a remote myocardial diastolic stiffness of C0¯=0.102kPa, and a remote myocardial contractility of Tmax¯=146.9kPa, which are both in the range of previously published normal human values. Moreover, we found a normalized pixel intensity range of 30% for the BZ, which is consistent with the literature. Based on these regional myocardial material properties, we used our finite element model to compute patient-specific diastolic and systolic LV myofiber stress distributions, which cannot be measured directly. One of the main driving forces for adverse LV remodeling is assumed to be an abnormally high level of ventricular wall stress, and many existing and new treatments for heart failure fundamentally attempt to normalize LV wall stress. Thus, our noninvasive method for estimating smooth regional variations in myocardial contractility should be valuable for optimizing new surgical or medical strategies to limit the chronic evolution from infarction to heart failure.

Copyright © 2015 by ASME
Your Session has timed out. Please sign back in to continue.


Guccione, J. M., Moonly, S. M., Moustakidis, P., Costa, K. D., Moulton, M. J., Ratcliffe, M. B., and Pasque, M. K., 2001, “Mechanism Underlying Mechanical Dysfunction in the Border Zone of Left Ventricular Aneurysm: A Finite Element Model Study,” Ann. Thorac. Surg., 71(2), pp. 654–662. [CrossRef] [PubMed]
Jackson, B. M., Gorman, J. H., Moainie, S. L., Guy, T. S., Narula, N., Narula, J., St. John-Sutton, M. G., Edmunds, L. H., and Gorman, R. C., 2002, “Extension of Borderzone Myocardium in Postinfarction Dilated Cardiomyopathy,” J. Am. Coll. Cardiol., 40(6), pp. 1160–1167. [CrossRef] [PubMed]
Wenk, J. F., Klepach, D., Lee, L. C., Zhang, Z., Ge, L., Tseng, E. E., Martin, A., Kozerke, S., Gorman, J. H., Gorman, R. C., and Guccione, J. M., 2012, “First Evidence of Depressed Contractility in the Border Zone of a Human Myocardial Infarction,” Ann. Thorac. Surg., 93(4), pp. 1188–1193. [CrossRef] [PubMed]
Lee, L. C., Wenk, J. F., Klepach, D., Zhang, Z., Saloner, D., Wallace, A. W., Ge, L., Ratcliffe, M. B., and Guccione, J. M., 2011, “A Novel Method for Quantifying In-Vivo Regional Left Ventricular Myocardial Contractility in the Border Zone of a Myocardial Infarction,” ASME J. Biomech. Eng., 133(9), p. 094506. [CrossRef]
Shimkunas, R., Makwana, O., Spaulding, K., Bazargan, M., Khazalpour, M., Takaba, K., Soleimani, M., Myagmar, B.-E., Lovett, D. H., Simpson, P. C., Ratcliffe, M. B., and Baker, A. J., 2014, “Myofilament Dysfunction Contributes to Impaired Myocardial Contraction in the Infarct Border Zone,” Am. J. Physiol.: Heart Circ. Physiol., 307(8), pp. H1150–H1158. [CrossRef] [PubMed]
Shimkunas, R., Zhang, Z., Wenk, J. F., Soleimani, M., Khazalpour, M., Acevedo-Bolton, G., Wang, G., Saloner, D., Mishra, R., Wallace, A. W., Ge, L., Baker, A. J., Guccione, J. M., and Ratcliffe, M. B., 2013, “Left Ventricular Myocardial Contractility is Depressed in the Borderzone After Posterolateral Myocardial Infarction,” Ann. Thorac. Surg., 95(5), pp. 1619–1625. [CrossRef] [PubMed]
Lee, L. C., Wall, S. T., Genet, M., Hinson, A., and Guccione, J. M., 2014, “Bioinjection Treatment: Effects of Post-Injection Residual Stress on Left Ventricular Wall Stress,” J. Biomech., 47(12), pp. 3115–3119. [CrossRef] [PubMed]
Lee, L. C., Ge, L., Zhang, Z., Pease, M., Nikolic, S. D., Mishra, R., Ratcliffe, M. B., and Guccione, J. M., 2014, “Patient-Specific Finite Element Modeling of the Cardiokinetix Parachute Device: Effects on Left Ventricular Wall Stress and Function,” Med. Biol. Eng. Comput.52(6), pp. 557–566. [CrossRef] [PubMed]
Kim, R. J., Fieno, D. S., Parrish, T. B., Harris, K., Chen, E.-L., Simonetti, O., Bundy, J., Finn, J. P., Klocke, F. J., and Judd, R. M., 1999, “Relationship of MRI Delayed Contrast Enhancement to Irreversible Injury, Infarct Age, and Contractile Function,” Circulation, 100(19), pp. 1992–2002. [CrossRef] [PubMed]
Choi, K. M., Kim, R. J., Gubernikoff, G., Vargas, J. D., Parker, M., and Judd, R. M., 2001, “Transmural Extent of Acute Myocardial Infarction Predicts Long-Term Improvement in Contractile Function,” Circulation, 104(10), pp. 1101–1107. [CrossRef] [PubMed]
Wu, E., Judd, R. M., Vargas, J. D., Klocke, F. J., Bonow, R. O., and Kim, R. J., 2001, “Visualisation of Presence, Location, and Transmural Extent of Healed Q-Wave and Non-Q-Wave Myocardial Infarction,” Lancet, 357(9249), pp. 21–28. [CrossRef] [PubMed]
Sun, K., Stander, N., Jhun, C.-S., Zhang, Z., Suzuki, T., Wang, G.-Y., Saeed, M., Wallace, A. W., Tseng, E. E., Baker, A. J., Saloner, D. A., Einstein, D. R., Ratcliffe, M. B., and Guccione, J. M., 2009, “A Computationally Efficient Formal Optimization of Regional Myocardial Contractility in a Sheep With Left Ventricular Aneurysm,” ASME J. Biomech. Eng., 131(11), p. 111001. [CrossRef]
Wenk, J. F., Sun, K., Zhang, Z., Soleimani, M., Ge, L., Saloner, D. A., Wallace, A. W., Ratcliffe, M. B., and Guccione, J. M., 2011, “Regional Left Ventricular Myocardial Contractility and Stress in a Finite Element Model of Posterobasal Myocardial Infarction,” ASME J. Biomech. Eng., 133(4), p. 044501. [CrossRef]
Genet, M., Lee, L. C., Nguyen, R., Haraldsson, H., Acevedo-Bolton, G., Zhang, Z., Ge, L., Ordovas, K., Kozerke, S., and Guccione, J. M., 2014, “Distribution of Normal Human Left Ventricular Myofiber Stress at End-Diastole and End-Systole—A Target for in Silico Studies of Cardiac Procedures,” J. Appl. Physiol., 117, pp. 142–152. [CrossRef] [PubMed]
Sagawa, K., 1978, “The Ventricular Pressure-Volume Diagram Revisited,” Circ. Res., 43(5), pp. 677–687. [CrossRef] [PubMed]
Holzapfel, G. A., 2000, Nonlinear Solid Mechanics: A Continuum Approach for Engineering, Wiley, Chichester, UK, p. 470.
Fung, Y. C., 1993, Biomechanics: Mechanical Properties of Living Tissues, Springer-Verlag, New York. [CrossRef]
Guccione, J. M., McCulloch, A. D., and Waldman, L. K., 1991, “Passive Material Properties of Intact Ventricular Myocardium Determined From a Cylindrical Model,” ASME J. Biomech. Eng., 113(1), pp. 42–55. [CrossRef]
Ateshian, G. A., and Costa, K. D., 2009, “A Frame-Invariant Formulation of Fung Elasticity,” J. Biomech., 42(6), pp. 781–785. [CrossRef] [PubMed]
Guccione, J. M., and McCulloch, A. D., 1993, “Mechanics of Active Contraction in Cardiac Muscle: Part I—Constitutive Relations for Fiber Stress That Describe Deactivation,” ASME J. Biomech. Eng., 115(1), pp. 72–81. [CrossRef]
Guccione, J. M., Waldman, L. K., and McCulloch, A. D., 1993, “Mechanics of Active Contraction in Cardiac Muscle: Part II—Cylindrical Models of the Systolic Left Ventricle,” ASME J. Biomech. Eng., 115(1), pp. 82–90. [CrossRef]
Osman, N. F., Kerwin, W. S., McVeigh, E. R., and Prince, J. L., 1999, “Cardiac Motion Tracking Using CINE Harmonic Phase (HARP) Magnetic Resonance Imaging,” Magn. Reson. Med., 42(6), pp. 1048–1060. [CrossRef] [PubMed]
Ryf, S., Tsao, J., Schwitter, J., Stuessi, A., and Boesiger, P., 2004, “Peak-Combination HARP: A Method to Correct for Phase Errors in HARP,” J. Magn. Reson. Imaging, 20, pp. 874–880. [CrossRef] [PubMed]
Christensen, G. E., Rabbitt, R. D., and Miller, M. I., 1996, “Deformable Templates Using Large Deformation Kinematics,” IEEE Trans. Image Process., 5(10), pp. 1435–1447. [CrossRef] [PubMed]
Tanaka, Y., Genet, M., Lee, L. C., Martin, A. J., Sievers, R., and Gerstenfeld, E. P., 2015, “Utility of High-Resolution Electroanatomic Mapping of the Left Ventricle Using a Multispline Basket Catheter in a Swine Model of Chronic Myocardial Infarction,” Heart Rhythm, 12(1), pp. 144–154. [CrossRef] [PubMed]
Sun, K., Zhang, Z., Suzuki, T., Wenk, J. F., Stander, N., Einstein, D. R., Saloner, D. A., Wallace, A. W., Guccione, J. M., and Ratcliffe, M. B., 2010, “Dor Procedure for Dyskinetic Anteroapical Myocardial Infarction Fails to Improve Contractility in the Border Zone,” J. Thorac. Cardiovasc. Surg., 140(1), pp. 233–239. [CrossRef] [PubMed]
Jones, R. H., Velazquez, E. J., Michler, R. E., Sopko, G., Oh, J. K., O'Connor, C. M., Hill, J. A., Menicanti, L., Sadowski, Z., Desvigne-Nickens, P., Rouleau, J.-L., and Lee, K. L., 2009, “Coronary Bypass Surgery With or Without Surgical Ventricular Reconstruction,” N. Engl. J. Med., 360(17), pp. 1705–1717. [CrossRef] [PubMed]
Walker, J. C., Ratcliffe, M. B., Zhang, P., Wallace, A. W., Fata, B., Hsu, E. W., Saloner, D. A., and Guccione, J. M., 2005, “MRI-Based Finite-Element Analysis of Left Ventricular Aneurysm,” Am. J. Physiol.: Heart Circ. Physiol., 289(2), pp. H692–H700. [CrossRef] [PubMed]
Karim, R., Housden, R. J., Balasubramaniam, M., Chen, Z., Perry, D., Uddin, A., Al-Beyatti, Y., Palkhi, E., Acheampong, P., Obom, S., Hennemuth, A., Lu, Y., Bai, W., Shi, W., Gao, Y., Peitgen, H.-O., Radau, P., Razavi, R., Tannenbaum, A., Rueckert, D., Cates, J., Schaeffter, T., Peters, D., MacLeod, R., and Rhode, K. S., 2013, “Evaluation of Current Algorithms for Segmentation of Scar Tissue From Late Gadolinium Enhancement Cardiovascular Magnetic Resonance of the Left Atrium: An Open-Access Grand Challenge,” J. Cardiovasc. Magn. Reson., 15(105), pp. 1–7. [CrossRef] [PubMed]
Lee, L. C., Genet, M., Tanaka, Y., Guccione, J. M., Martin, A. J., Ordovas, K., Sievers, R., and Gerstenfeld, E. P., 2014, “Comparison of Methodologies for Scar Delineation Using Delayed-Enhancement MRI in a Swine Model of Chronic Infarction,” Heart Rhythm Society Meeting, pp. PO05–PO131. http://ondemand.hrsonline.org/common/presentation-detail.aspx/15/23/1392/10554
Kramer, C. M., Rogers, W. J., Mankad, S., Theobald, T. M., Pakstis, D. L., and Hu, Y.-L., 2000, “Contractile Reserve and Contrast Uptake Pattern by Magnetic Resonance Imaging and Functional Recovery After Reperfused Myocardial Infarction,” J. Am. Coll. Cardiol., 36(6), pp. 1835–1840. [CrossRef] [PubMed]
Toussaint, N., Stoeck, C. T., Sermesant, M., Schaeffter, T., Kozerke, S., and Batchelor, P. G., 2013, “In Vivo Human Cardiac Fibre Architecture Estimation Using Shape-Based Diffusion Tensor Processing,” Med. Image Anal., 17(8), pp. 1243–1255. [CrossRef] [PubMed]
Harmer, J., Pushparajah, K., Toussaint, N., Stoeck, C. T., Chan, R. W., Atkinson, D., Razavi, R., and Kozerke, S., 2013, “In Vivo Myofibre Architecture in the Systemic Right Ventricle,” Eur. Heart J., 34(47), p. 3640. [CrossRef] [PubMed]


Grahic Jump Location
Fig. 1

Relationship between local tissue viability (i.e., pixel intensity measured by DE-MR imaging, normalized by the maximal pixel intensity) and local passive stiffness, as well as local active contractility. For low pixel intensities, i.e., healthy myocardium, the local stiffness, and contractility are equal to their normal values. Conversely, for high pixel intensities, i.e., damaged myocardium, the local stiffness is much higher than normal, and the local contractility is null. We assumed linearly varying material properties across the BZ. The parameters α1 and α2 must be personalized for each patient.

Grahic Jump Location
Fig. 2

Magnetic resonance images used for model personalization. (a) 3D cine images are used for ventricular geometry, (b) 3D CSPAMM-MR images for tissue strain, and (c) 2D DE-MR images for tissue viability.

Grahic Jump Location
Fig. 3

(a) Finite-element mesh of the left ventricular geometry in early-diastole. The contours were created by manual segmentation of cine MR images in mevislab. The fully hexahedral mesh was generated with truegrid. (b) Generic fiber field prescribed to the mesh using custom vtkpython scripts. Helix angle varies transmurally from +60 deg at the endocardium to −60 deg at the epicardium. Transverse and sheet angles are null. (c) Endocardial surface at end-diastole (blue) and end-systole (red), extracted by manual segmentation of cine MR images in mevislab.

Grahic Jump Location
Fig. 4

Nonrigid registration of the viability data with the anatomical data, based on method in Ref. [24]. One short-axis slice of the anatomical mask is shown in blue (outside the ventricular wall) and red (inside), and the gray-scale viability map is superimposed. (a)–(c) Different iterations of the registration process showing initial mismatch and final match between the viability map and the anatomy.

Grahic Jump Location
Fig. 5

(a) Viability map in early diastole. Healthy regions (low pixel intensity) appear in blue, while the infarcted region (high pixel intensity) appears in red. (b) Personalized contractility map determined through numerical optimization. The colors are inverted compared to those in the viability map, so that the regions are consistent: healthy regions (high contractility) appear in blue, while the infarcted region (low contractility) appears in red. Note that because the core infarct area is rather small, the region with zero contractility (red) is small as well. (c) Contour plot, in a long-axis plane, of the 95% normalized pixel intensity, which corresponds, according to the material optimization, to the area with less than 10% contractility compared to the remote region. (d) Same contour plot, as in (c), in a midventricular short-axis plane.

Grahic Jump Location
Fig. 6

Comparison of the ventricular deformation at ED (left) and end-systole (right), predicted by the personalized finite-element model (red line) and measured by cine MRI, in short-axis (top) and long-axis (bottom) views. The overall deformation pattern is well reproduced by the model.

Grahic Jump Location
Fig. 7

(a) Myofiber stress at ED, in kPa. Because of increased stiffness, the infarcted region seems slightly less stressed than the remote region. (b) Myofiber stress at end-systole, in kPa. Because of reduced contractility, the total stress, which combines both passive and active stresses, is significantly lower in the BZ than in the region remote to the infarct.



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