Advertisement

Cardiac MRI Assessment of Mouse Myocardial Infarction and Regeneration

Protocol
First Online:
  • 1.3k Downloads
Part of the Methods in Molecular Biology book series (MIMB, volume 2158)

Abstract

Small animal models are indispensable for cardiac regeneration research. Studies in mouse and rat models have provided important insights into the etiology and mechanisms of cardiovascular diseases and accelerated the development of therapeutic strategies. It is vitally important to be able to evaluate the therapeutic efficacy and have reliable surrogate markers for therapeutic development for cardiac regeneration research. Magnetic resonance imaging (MRI), a versatile and noninvasive imaging modality with excellent penetration depth, tissue coverage, and soft-tissue contrast, is becoming a more important tool in both clinical settings and research arenas. Cardiac MRI (CMR) is versatile, noninvasive, and capable of measuring many different aspects of cardiac functions, and, thus, is ideally suited to evaluate therapeutic efficacy for cardiac regeneration. CMR applications include assessment of cardiac anatomy, regional wall motion, myocardial perfusion, myocardial viability, cardiac function assessment, assessment of myocardial infarction, and myocardial injury. Myocardial infarction models in mice are commonly used model systems for cardiac regeneration research. In this chapter, we discuss various CMR applications to evaluate cardiac functions and inflammation after myocardial infarction.

Key words

Cardiac MRI Myocardial infarction Mouse Tagging Strain Fibrosis Late-gadolinium enhancement Dynamic contrast enhancement Myocardial perfusion Extracellular volume 
This is a preview of subscription content, log in to check access.

References

  1. 1.
    De Leon-Rodriguez LM et al (2015) Basic MR relaxation mechanisms and contrast agent design. J Magn Reson Imaging 42(3):545–565PubMedPubMedCentralGoogle Scholar
  2. 2.
    Vanhoutte L et al (2016) High field magnetic resonance imaging of rodents in cardiovascular research. Basic Res Cardiol 111(4):46PubMedGoogle Scholar
  3. 3.
    Lorusso V et al (1999) Pharmacokinetics and tissue distribution in animals of gadobenate ion, the magnetic resonance imaging contrast enhancing component of gadobenate dimeglumine 0.5 M solution for injection (MultiHance). J Comput Assist Tomogr 23(Suppl 1):S181–S194PubMedGoogle Scholar
  4. 4.
    Morisetti A et al (1999) Toxicological safety evaluation of gadobenate dimeglumine 0.5 M solution for injection (MultiHance), a new magnetic resonance imaging contrast medium. J Comput Assist Tomogr 23(Suppl 1):S207–S217PubMedGoogle Scholar
  5. 5.
    Modo M, Hoehn M, Bulte JW (2005) Cellular MR imaging. Mol Imaging 4(3):143–164PubMedGoogle Scholar
  6. 6.
    Bulte JW (2009) In vivo MRI cell tracking: clinical studies. AJR Am J Roentgenol 193(2):314–325PubMedPubMedCentralGoogle Scholar
  7. 7.
    Bulte JW, Kraitchman DL (2004) Monitoring cell therapy using iron oxide MR contrast agents. Curr Pharm Biotechnol 5(6):567–584PubMedGoogle Scholar
  8. 8.
    Cromer Berman SM, Walczak P, Bulte JW (2011) Tracking stem cells using magnetic nanoparticles. Wiley Interdiscip Rev Nanomed Nanobiotechnol 3(4):343–355PubMedPubMedCentralGoogle Scholar
  9. 9.
    Srinivas M et al (2010) Imaging of cellular therapies. Adv Drug Deliv Rev 62(11):1080–1093PubMedGoogle Scholar
  10. 10.
    Korosoglou G et al (2008) Positive contrast MR-lymphography using inversion recovery with ON-resonant water suppression (IRON). J Magn Reson Imaging 27(5):1175–1180PubMedPubMedCentralGoogle Scholar
  11. 11.
    Rumenapp C, Gleich B, Haase A (2012) Magnetic nanoparticles in magnetic resonance imaging and diagnostics. Pharm Res 29(5):1165–1179PubMedGoogle Scholar
  12. 12.
    Senpan A et al (2009) Conquering the dark side: colloidal iron oxide nanoparticles. ACS Nano 3(12):3917–3926PubMedPubMedCentralGoogle Scholar
  13. 13.
    Shapiro EM et al (2006) Magnetic resonance imaging of the migration of neuronal precursors generated in the adult rodent brain. NeuroImage 32(3):1150–1157PubMedGoogle Scholar
  14. 14.
    Shapiro EM et al (2006) In vivo detection of single cells by MRI. Magn Reson Med 55(2):242–249PubMedGoogle Scholar
  15. 15.
    Wu Y-JL et al (2004) MRI investigation of graft rejection following organ transplantation using rodent models. Method Enzymol 386:73–105Google Scholar
  16. 16.
    Wu YL, Ye Q, Ho C (2011) Cellular and functional imaging of cardiac transplant rejection. Curr Cardiovasc Imaging Rep 4(1):50–62PubMedPubMedCentralGoogle Scholar
  17. 17.
    Wu YL et al (2006) In situ labeling of immune cells with iron oxide particles: an approach to detect organ rejection by cellular MRI. Proc Natl Acad Sci U S A 103(6):1852–1857PubMedPubMedCentralGoogle Scholar
  18. 18.
    Yeh F-C et al (2011) R2∗-ρ imaging on rat allograft cardiac transplantation with acute rejection: a preliminary study. Annual Meeting of the International Society for Magnetic Resonance in MedicineGoogle Scholar
  19. 19.
    Wu YL et al (2009) Noninvasive evaluation of cardiac allograft rejection by cellular and functional cardiac magnetic resonance. JACC Cardiovasc Imaging 2(6):731–741PubMedPubMedCentralGoogle Scholar
  20. 20.
    Ye Q et al (2008) Longitudinal tracking of recipient macrophages in a rat chronic cardiac allograft rejection model with noninvasive magnetic resonance imaging using micrometer-sized paramagnetic iron oxide particles. Circulation 118(2):149–156PubMedPubMedCentralGoogle Scholar
  21. 21.
    Chen CL et al (2011) A new nano-sized iron oxide particle with high sensitivity for cellular magnetic resonance imaging. Mol Imaging Biol 13(5):825–839PubMedPubMedCentralGoogle Scholar
  22. 22.
    Yeh TC et al (1993) Intracellular labeling of T-cells with superparamagnetic contrast agents. Magn Reson Med 30:617–625PubMedGoogle Scholar
  23. 23.
    Yeh TC et al (1995) In vivo dynamic MRI tracking of rat T-cells labeled with superparamagnetic iron-oxide particles. Magn Reson Med 33:200–208PubMedGoogle Scholar
  24. 24.
    Kanno S et al (2001) Macrophage accumulation associated with rat cardiac allograft rejection detected by magnetic resonance imaging with ultrasmall superparamagnetic iron oxide particles. Circulation 104(8):934–938PubMedGoogle Scholar
  25. 25.
    Kanno S et al (2000) A novel approach with magnetic resonance imaging used for the detection of lung allograft rejection. J Thorac Cardiovasc Surg 120(5):923–934PubMedGoogle Scholar
  26. 26.
    Dodd SJ et al (1999) Detection of single mammalian cells by high-resolution magnetic resonance imaging. Biophys J 76(1 Pt 1):103–109PubMedPubMedCentralGoogle Scholar
  27. 27.
    Zhang Y et al (2000) Magnetic resonance imaging detection of rat renal transplant rejection by monitoring macrophage infiltration. Kidney Int 58(3):1300–1310PubMedGoogle Scholar
  28. 28.
    Shapiro EM, Skrtic S, Koretsky AP (2005) Sizing it up: cellular MRI using micron-sized iron oxide particles. Magn Reson Med 53(2):329–338PubMedGoogle Scholar
  29. 29.
    Shapiro EM et al (2004) MRI detection of single particles for cellular imaging. Proc Natl Acad Sci U S A 101(30):10901–10906PubMedPubMedCentralGoogle Scholar
  30. 30.
    DiFrancesco MW et al (2008) Comparison of SNR and CNR for in vivo mouse brain imaging at 3 and 7 T using well matched scanner configurations. Med Phys 35(9):3972–3978PubMedPubMedCentralGoogle Scholar
  31. 31.
    Magnotta VA, Friedman L (2006) Measurement of signal-to-noise and contrast-to-noise in the fBIRN multicenter imaging study. J Digit Imaging 19(2):140–147PubMedPubMedCentralGoogle Scholar
  32. 32.
    Maubon AJ et al (1999) Effect of field strength on MR images: comparison of the same subject at 0.5, 1.0, and 1.5 T. Radiographics 19(4):1057–1067PubMedGoogle Scholar
  33. 33.
    Epstein FH (2007) MR in mouse models of cardiac disease. NMR Biomed 20(3):238–255PubMedGoogle Scholar
  34. 34.
    Vandsburger MH et al (2007) Multi-parameter in vivo cardiac magnetic resonance imaging demonstrates normal perfusion reserve despite severely attenuated beta-adrenergic functional response in neuronal nitric oxide synthase knockout mice. Eur Heart J 28(22):2792–2798PubMedGoogle Scholar
  35. 35.
    Hiller KH et al (2008) Magnetic resonance of mouse models of cardiac disease. Handb Exp Pharmacol 185(Pt 2):245–257Google Scholar
  36. 36.
    Foley LM et al (2005) Murine orthostatic response during prolonged vertical studies: effect on cerebral blood flow measured by arterial spin-labeled MRI. Magn Reson Med 54(4):798–806PubMedGoogle Scholar
  37. 37.
    Xie H et al (2019) Differential effects of anesthetics on resting state functional connectivity in the mouse. J Cereb Blood Flow Metab.  https://doi.org/10.1177/0271678X19847123
  38. 38.
    Munting LP et al (2019) Influence of different isoflurane anesthesia protocols on murine cerebral hemodynamics measured with pseudo-continuous arterial spin labeling. NMR Biomed 32(8):e4105PubMedPubMedCentralGoogle Scholar
  39. 39.
    Uhrig L, Dehaene S, Jarraya B (2014) Cerebral mechanisms of general anesthesia. Ann Fr Anesth Reanim 33(2):72–82PubMedGoogle Scholar
  40. 40.
    Murtaza G et al (2016) Avertin®, but not volatile anesthetics addressing the two-pore domain K+ channel, TASK-1, slows down cilia-driven particle transport in the mouse trachea. PLoS One 11(12):e0167919PubMedPubMedCentralGoogle Scholar
  41. 41.
    Hua X et al (2010) Noninvasive real-time measurement of nasal mucociliary clearance in mice by pinhole gamma scintigraphy. J Appl Physiol (1985) 108(1):189–196Google Scholar
  42. 42.
    Yang S et al (2016) Sevoflurane and isoflurane inhibit KCl-induced Class II phosphoinositide 3-kinase alpha subunit mediated vasoconstriction in rat aorta. BMC Anesthesiol 16(1):63PubMedPubMedCentralGoogle Scholar
  43. 43.
    Qi F et al (2009) Volatile anesthetics inhibit angiotensin II-induced vascular contraction by modulating myosin light chain phosphatase inhibiting protein, CPI-17 and regulatory subunit, MYPT1 phosphorylation. Anesth Analg 109(2):412–417PubMedGoogle Scholar
  44. 44.
    Ishikawa A et al (2007) The mechanism behind the inhibitory effect of isoflurane on angiotensin II-induced vascular contraction is different from that of sevoflurane. Anesth Analg 105(1):97–102PubMedGoogle Scholar
  45. 45.
    Bishop J et al (2006) Retrospective gating for mouse cardiac MRI. Magn Reson Med 55(3):472–477PubMedGoogle Scholar
  46. 46.
    Kim B et al (2018) Retrospective motion gating in cardiac MRI using a simultaneously acquired navigator. NMR Biomed 31(3).  https://doi.org/10.1002/nbm.3874
  47. 47.
    Larson AC et al (2004) Self-gated cardiac cine MRI. Magn Reson Med 51(1):93–102PubMedPubMedCentralGoogle Scholar
  48. 48.
    Liu J et al (2017) Highly-accelerated self-gated free-breathing 3D cardiac cine MRI: validation in assessment of left ventricular function. MAGMA 30(4):337–346PubMedPubMedCentralGoogle Scholar
  49. 49.
    Ingle RR et al (2015) Self-gated fat-suppressed cardiac cine MRI. Magn Reson Med 73(5):1764–1774PubMedGoogle Scholar
  50. 50.
    Hiba B et al (2007) Cardiac and respiratory self-gated cine MRI in the mouse: comparison between radial and rectilinear techniques at 7T. Magn Reson Med 58(4):745–753PubMedGoogle Scholar
  51. 51.
    Hiba B et al (2006) Cardiac and respiratory double self-gated cine MRI in the mouse at 7 T. Magn Reson Med 55(3):506–513PubMedGoogle Scholar
  52. 52.
    Saeed M et al (2015) Cardiac MR imaging: current status and future direction. Cardiovasc Diagn Ther 5(4):290–310PubMedPubMedCentralGoogle Scholar
  53. 53.
    Saeed M et al (2017) Magnetic resonance imaging for characterizing myocardial diseases. Int J Cardiovasc Imaging 33(9):1395–1414PubMedGoogle Scholar
  54. 54.
    Alexander KP et al (2007) Acute coronary care in the elderly, part I: non-ST-segment-elevation acute coronary syndromes: a scientific statement for healthcare professionals from the American Heart Association Council on Clinical Cardiology: in collaboration with the Society of Geriatric Cardiology. Circulation 115(19):2549–2569PubMedGoogle Scholar
  55. 55.
    Kusumoto FM et al (2018) Systematic review for the 2017 AHA/ACC/HRS guideline for management of patients with ventricular arrhythmias and the prevention of sudden cardiac death: a report of the American College of Cardiology/American Heart Association Task Force on Clinical Practice Guidelines and the Heart Rhythm Society. Heart Rhythm 15(10):e253–e274PubMedGoogle Scholar
  56. 56.
    Al-Khatib SM et al (2018) 2017 AHA/ACC/HRS guideline for management of patients with ventricular arrhythmias and the prevention of sudden cardiac death: executive summary: a report of the American College of Cardiology/American Heart Association Task Force on Clinical Practice Guidelines and the Heart Rhythm Society. Heart Rhythm 15(10):e190–e252PubMedGoogle Scholar
  57. 57.
    Baumgartner HC et al (2017) Recommendations on the echocardiographic assessment of aortic valve stenosis: a focused update from the European Association of Cardiovascular Imaging and the American Society of Echocardiography. Eur Heart J Cardiovasc Imaging 18(3):254–275Google Scholar
  58. 58.
    Vinnakota KC, Bassingthwaighte JB (2004) Myocardial density and composition: a basis for calculating intracellular metabolite concentrations. Am J Physiol Heart Circ Physiol 286(5):H1742–H1749PubMedGoogle Scholar
  59. 59.
    Slawson SE et al (1998) Cardiac MRI of the normal and hypertrophied mouse heart. Magn Reson Med 39(6):980–987PubMedGoogle Scholar
  60. 60.
    Berr SS et al (2005) Black blood gradient echo cine magnetic resonance imaging of the mouse heart. Magn Reson Med 53(5):1074–1079PubMedGoogle Scholar
  61. 61.
    Lanier GM et al (2012) An update on diastolic dysfunction. Cardiol Rev 20(5):230–236PubMedGoogle Scholar
  62. 62.
    Alagiakrishnan K et al (2013) Update on diastolic heart failure or heart failure with preserved ejection fraction in the older adults. Ann Med 45(1):37–50PubMedGoogle Scholar
  63. 63.
    Zerhouni EA et al (1988) Human heart: tagging with MR imaging--a method for noninvasive assessment of myocardial motion. Radiology 169(1):59–63PubMedGoogle Scholar
  64. 64.
    McVeigh ER, Zerhouni EA (1991) Noninvasive measurement of transmural gradients in myocardial strain with MR imaging. Radiology 180(3):677–683PubMedGoogle Scholar
  65. 65.
    Axel L, Goncalves RC, Bloomgarden D (1992) Regional heart wall motion: two-dimensional analysis and functional imaging with MR imaging. Radiology 183(3):745–750PubMedGoogle Scholar
  66. 66.
    Young AA et al (1994) Two-dimensional left ventricular deformation during systole using magnetic resonance imaging with spatial modulation of magnetization. Circulation 89(2):740–752PubMedGoogle Scholar
  67. 67.
    Osman NF, Prince JL (2000) Visualizing myocardial function using HARP MRI. Phys Med Biol 45(6):1665–1682PubMedGoogle Scholar
  68. 68.
    Castillo E et al (2005) Quantitative assessment of regional myocardial function with MR-tagging in a multi-center study: interobserver and intraobserver agreement of fast strain analysis with Harmonic Phase (HARP) MRI. J Cardiovasc Magn Reson 7(5):783–791PubMedGoogle Scholar
  69. 69.
    Epstein FH et al (2002) MR tagging early after myocardial infarction in mice demonstrates contractile dysfunction in adjacent and remote regions. Magn Reson Med 48(2):399–403PubMedGoogle Scholar
  70. 70.
    DeVore AD et al (2017) Impaired left ventricular global longitudinal strain in patients with heart failure with preserved ejection fraction: insights from the RELAX trial. Eur J Heart Fail 19(7):893–900PubMedGoogle Scholar
  71. 71.
    Buggey J et al (2017) Left ventricular global longitudinal strain in patients with heart failure with preserved ejection fraction: outcomes following an acute heart failure hospitalization. ESC Heart Fail 4(4):432–439PubMedPubMedCentralGoogle Scholar
  72. 72.
    Hasselberg NE et al (2015) Left ventricular global longitudinal strain is associated with exercise capacity in failing hearts with preserved and reduced ejection fraction. Eur Heart J Cardiovasc Imaging 16(2):217–224PubMedGoogle Scholar
  73. 73.
    Biering-Sorensen T et al (2017) Prognostic importance of left ventricular mechanical dyssynchrony in heart failure with preserved ejection fraction. Eur J Heart Fail 19(8):1043–1052PubMedGoogle Scholar
  74. 74.
    Schnelle M et al (2018) Echocardiographic evaluation of diastolic function in mouse models of heart disease. J Mol Cell Cardiol 114:20–28PubMedPubMedCentralGoogle Scholar
  75. 75.
    Osman NF et al (1999) Cardiac motion tracking using CINE harmonic phase (HARP) magnetic resonance imaging. Magn Reson Med 42(6):1048–1060PubMedPubMedCentralGoogle Scholar
  76. 76.
    Osman NF, Prince JL (2004) Regenerating MR tagged images using harmonic phase (HARP) methods. IEEE Trans Biomed Eng 51(8):1428–1433PubMedGoogle Scholar
  77. 77.
    Sosnovik DE et al (2007) Cardiac MRI in mice at 9.4 Tesla with a transmit-receive surface coil and a cardiac-tailored intensity-correction algorithm. J Magn Reson Imaging 26(2):279–287PubMedGoogle Scholar
  78. 78.
    Shehata ML et al (2009) Myocardial tissue tagging with cardiovascular magnetic resonance. J Cardiovasc Magn Reson 11:55PubMedPubMedCentralGoogle Scholar
  79. 79.
    Gilson WD et al (2004) Complementary displacement-encoded MRI for contrast-enhanced infarct detection and quantification of myocardial function in mice. Magn Reson Med 51(4):744–752PubMedGoogle Scholar
  80. 80.
    Gilson WD et al (2005) Measurement of myocardial mechanics in mice before and after infarction using multislice displacement-encoded MRI with 3D motion encoding. Am J Physiol Heart Circ Physiol 288(3):H1491–H1497PubMedGoogle Scholar
  81. 81.
    Axel L, Dougherty L (1989) Heart wall motion: improved method of spatial modulation of magnetization for MR imaging. Radiology 172(2):349–350PubMedGoogle Scholar
  82. 82.
    Axel L, Dougherty L (1989) MR imaging of motion with spatial modulation of magnetization. Radiology 171(3):841–845PubMedGoogle Scholar
  83. 83.
    de Crespigny AJ, Carpenter TA, Hall LD (1991) Cardiac tagging in the rat using a DANTE sequence. Magn Reson Med 21(1):151–156PubMedGoogle Scholar
  84. 84.
    Tsekos NV et al (1995) Myocardial tagging with B1 insensitive adiabatic DANTE inversion sequences. Magn Reson Med 34(3):395–401PubMedGoogle Scholar
  85. 85.
    Butler J et al (2014) Developing therapies for heart failure with preserved ejection fraction: current state and future directions. JACC Heart Fail 2(2):97–112PubMedPubMedCentralGoogle Scholar
  86. 86.
    Kraigher-Krainer E et al (2014) Impaired systolic function by strain imaging in heart failure with preserved ejection fraction. J Am Coll Cardiol 63(5):447–456PubMedGoogle Scholar
  87. 87.
    Oktay AA, Rich JD, Shah SJ (2013) The emerging epidemic of heart failure with preserved ejection fraction. Curr Heart Fail Rep 10(4):401–410PubMedGoogle Scholar
  88. 88.
    Protti A et al (2015) Assessment of myocardial remodeling using an elastin/tropoelastin specific agent with high field magnetic resonance imaging (MRI). J Am Heart Assoc 4(8):e001851PubMedPubMedCentralGoogle Scholar
  89. 89.
    Jeung MY et al (2012) Myocardial tagging with MR imaging: overview of normal and pathologic findings. Radiographics 32(5):1381–1398PubMedGoogle Scholar
  90. 90.
    Grotenhuis HB et al (2017) Abnormal myocardial contractility after pediatric heart transplantation by cardiac MRI. Pediatr Cardiol 38(6):1198–1205PubMedPubMedCentralGoogle Scholar
  91. 91.
    Musa TA et al (2017) Cardiovascular magnetic resonance evaluation of symptomatic severe aortic stenosis: association of circumferential myocardial strain and mortality. J Cardiovasc Magn Reson 19(1):13PubMedPubMedCentralGoogle Scholar
  92. 92.
    Schneeweis C et al (2014) Comparison of myocardial tagging and feature tracking in patients with severe aortic stenosis. J Heart Valve Dis 23(4):432–440PubMedGoogle Scholar
  93. 93.
    Ahmed MI et al (2012) Relation of torsion and myocardial strains to LV ejection fraction in hypertension. JACC Cardiovasc Imaging 5(3):273–281PubMedPubMedCentralGoogle Scholar
  94. 94.
    Fuchs E et al (2004) Cardiac rotation and relaxation in patients with chronic heart failure. Eur J Heart Fail 6(6):715–722PubMedGoogle Scholar
  95. 95.
    Delhaas T et al (2008) Left ventricular apical torsion and architecture are not inverted in situs inversus totalis. Prog Biophys Mol Biol 97(2–3):513–519PubMedGoogle Scholar
  96. 96.
    Cutri E et al (2015) The cardiac torsion as a sensitive index of heart pathology: a model study. J Mech Behav Biomed Mater 55:104–119PubMedGoogle Scholar
  97. 97.
    Hong Z et al (2015) The value of myocardial torsion and aneurysm volume for evaluating cardiac function in rabbit with left ventricular aneurysm. PLoS One 10(4):e0121876PubMedPubMedCentralGoogle Scholar
  98. 98.
    Piya MK et al (2011) Abnormal left ventricular torsion and cardiac autonomic dysfunction in subjects with type 1 diabetes mellitus. Metabolism 60(8):1115–1121PubMedPubMedCentralGoogle Scholar
  99. 99.
    Burns KV et al (2011) Torsion and dyssynchrony differences between chronically paced and non-paced heart failure patients. J Card Fail 17(6):495–502PubMedGoogle Scholar
  100. 100.
    Campbell SG et al (2013) Altered ventricular torsion and transmural patterns of myocyte relaxation precede heart failure in aging F344 rats. Am J Physiol Heart Circ Physiol 305(5):H676–H686PubMedPubMedCentralGoogle Scholar
  101. 101.
    Schelbert EB et al (2011) Myocardial extravascular extracellular volume fraction measurement by gadolinium cardiovascular magnetic resonance in humans: slow infusion versus bolus. J Cardiovasc Magn Reson 13:16PubMedPubMedCentralGoogle Scholar
  102. 102.
    Schelbert EB et al (2010) Late gadolinium-enhancement cardiac magnetic resonance identifies postinfarction myocardial fibrosis and the border zone at the near cellular level in ex vivo rat heart. Circ Cardiovasc Imaging 3(6):743–752PubMedPubMedCentralGoogle Scholar
  103. 103.
    Flett AS et al (2010) Equilibrium contrast cardiovascular magnetic resonance for the measurement of diffuse myocardial fibrosis: preliminary validation in humans. Circulation 122(2):138–144PubMedGoogle Scholar
  104. 104.
    Broberg CS et al (2010) Quantification of diffuse myocardial fibrosis and its association with myocardial dysfunction in congenital heart disease. Circ Cardiovasc Imaging 3(6):727–734PubMedPubMedCentralGoogle Scholar
  105. 105.
    Lipshultz SE et al (2014) Anthracycline-related cardiotoxicity in childhood cancer survivors. Curr Opin Cardiol 29(1):103–112PubMedGoogle Scholar
  106. 106.
    Kellman P, Hansen MS (2014) T1-mapping in the heart: accuracy and precision. J Cardiovasc Magn Reson 16:2PubMedPubMedCentralGoogle Scholar
  107. 107.
    Kellman P, Arai AE, Xue H (2013) T1 and extracellular volume mapping in the heart: estimation of error maps and the influence of noise on precision. J Cardiovasc Magn Reson 15:56PubMedPubMedCentralGoogle Scholar
  108. 108.
    Treibel TA et al (2016) Automatic measurement of the myocardial interstitium: synthetic extracellular volume quantification without hematocrit sampling. JACC Cardiovasc Imaging 9(1):54–63PubMedGoogle Scholar
  109. 109.
    Miller CA et al (2013) Comprehensive validation of cardiovascular magnetic resonance techniques for the assessment of myocardial extracellular volume. Circ Cardiovasc Imaging 6(3):373–383PubMedGoogle Scholar
  110. 110.
    Liu X et al (2014) Interrogating congenital heart defects with noninvasive fetal echocardiography in a mouse forward genetic screen. Circ Cardiovasc Imaging 7(1):31–42PubMedGoogle Scholar
  111. 111.
    Schelbert EB et al (2015) Myocardial fibrosis quantified by extracellular volume is associated with subsequent hospitalization for heart failure, death, or both across the spectrum of ejection fraction and heart failure stage. J Am Heart Assoc 4(12):e002613PubMedPubMedCentralGoogle Scholar
  112. 112.
    Ertel A et al (2015) Increased myocardial extracellular volume in active idiopathic systemic capillary leak syndrome. J Cardiovasc Magn Reson 17:76PubMedPubMedCentralGoogle Scholar
  113. 113.
    Wong TC et al (2012) Association between extracellular matrix expansion quantified by cardiovascular magnetic resonance and short-term mortality. Circulation 126(10):1206–1216PubMedPubMedCentralGoogle Scholar
  114. 114.
    Cooper MA et al (2014) How accurate is MOLLI T1 mapping in vivo? Validation by spin echo methods. PLoS One 9(9):e107327PubMedPubMedCentralGoogle Scholar
  115. 115.
    Iliff JJ et al (2013) Cerebral arterial pulsation drives paravascular CSF-interstitial fluid exchange in the murine brain. J Neurosci 33(46):18190–18199PubMedPubMedCentralGoogle Scholar
  116. 116.
    Roujol S et al (2014) Accuracy, precision, and reproducibility of four T1 mapping sequences: a head-to-head comparison of MOLLI, ShMOLLI, SASHA, and SAPPHIRE. Radiology 272(3):683–689PubMedGoogle Scholar
  117. 117.
    Cortez-Retamozo V et al (2012) Origins of tumor-associated macrophages and neutrophils. Proc Natl Acad Sci U S A 109(7):2491–2496PubMedPubMedCentralGoogle Scholar
  118. 118.
    Keliher EJ et al (2011) 89Zr-labeled dextran nanoparticles allow in vivo macrophage imaging. Bioconjug Chem 22(12):2383–2389PubMedPubMedCentralGoogle Scholar
  119. 119.
    Frangogiannis NG (2008) The immune system and cardiac repair. Pharmacol Res 58(2):88–111PubMedPubMedCentralGoogle Scholar
  120. 120.
    Frangogiannis NG (2012) Regulation of the inflammatory response in cardiac repair. Circ Res 110(1):159–173PubMedPubMedCentralGoogle Scholar
  121. 121.
    Frangogiannis NG, Smith CW, Entman ML (2002) The inflammatory response in myocardial infarction. Cardiovasc Res 53(1):31–47PubMedGoogle Scholar
  122. 122.
    Almenar L et al (2003) Utility of cardiac magnetic resonance imaging for the diagnosis of heart transplant rejection. Transplant Proc 35(5):1962–1964PubMedGoogle Scholar
  123. 123.
    Eltzschig HK, Eckle T (2011) Ischemia and reperfusion--from mechanism to translation. Nat Med 17(11):1391–1401PubMedGoogle Scholar
  124. 124.
    Abrous DN, Koehl M, Le Moal M (2005) Adult neurogenesis: from precursors to network and physiology. Physiol Rev 85(2):523–569PubMedGoogle Scholar
  125. 125.
    Mannon RB (2012) Macrophages: contributors to allograft dysfunction, repair, or innocent bystanders? Curr Opin Organ Transplant 17(1):20–25PubMedPubMedCentralGoogle Scholar
  126. 126.
    Monassier JP (2008) Reperfusion injury in acute myocardial infarction: from bench to cath lab. Part II: clinical issues and therapeutic options. Arch Cardiovasc Dis 101(9):565–575PubMedGoogle Scholar
  127. 127.
    Frangogiannis NG (2006) Targeting the inflammatory response in healing myocardial infarcts. Curr Med Chem 13(16):1877–1893PubMedGoogle Scholar
  128. 128.
    Libby P et al (1973) Reduction of experimental myocardial infarct size by corticosteroid administration. J Clin Invest 52(3):599–607PubMedPubMedCentralGoogle Scholar
  129. 129.
    Roberts R, DeMello V, Sobel BE (1976) Deleterious effects of methylprednisolone in patients with myocardial infarction. Circulation 53(3 Suppl):I204–I206PubMedGoogle Scholar
  130. 130.
    Cardilo-Reis L, Witztum JL, Binder CJ (2010) When monocytes come (too) close to our hearts. J Am Coll Cardiol 55(15):1639–1641PubMedGoogle Scholar
  131. 131.
    van Amerongen MJ et al (2007) Macrophage depletion impairs wound healing and increases left ventricular remodeling after myocardial injury in mice. Am J Pathol 170(3):818–829PubMedPubMedCentralGoogle Scholar
  132. 132.
    Leor J et al (2006) Ex vivo activated human macrophages improve healing, remodeling, and function of the infarcted heart. Circulation 114(1 Suppl):I94–I100PubMedGoogle Scholar
  133. 133.
    Leuschner F et al (2010) Angiotensin-converting enzyme inhibition prevents the release of monocytes from their splenic reservoir in mice with myocardial infarction. Circ Res 107(11):1364–1373PubMedPubMedCentralGoogle Scholar
  134. 134.
    Lee WW et al (2012) PET/MRI of inflammation in myocardial infarction. J Am Coll Cardiol 59(2):153–163PubMedPubMedCentralGoogle Scholar
  135. 135.
    Christen T, Shimizu K, Libby P (2010) Advances in imaging of cardiac allograft rejection. Curr Cardiovasc Imaging Rep 3:99–105Google Scholar
  136. 136.
    Majmudar MD, Nahrendorf M (2012) Cardiovascular molecular imaging: the road ahead. J Nucl Med 53(5):673–676PubMedGoogle Scholar
  137. 137.
    Buxton DB et al (2011) Report of the National Heart, Lung, and Blood Institute working group on the translation of cardiovascular molecular imaging. Circulation 123(19):2157–2163PubMedPubMedCentralGoogle Scholar
  138. 138.
    Nahrendorf M et al (2009) Multimodality cardiovascular molecular imaging, Part II. Circ Cardiovasc Imaging 2(1):56–70PubMedPubMedCentralGoogle Scholar
  139. 139.
    Sinusas AJ et al (2008) Multimodality cardiovascular molecular imaging, part I. Circ Cardiovasc Imaging 1(3):244–256PubMedGoogle Scholar
  140. 140.
    Sosnovik DE et al (2005) Magnetic resonance imaging of cardiomyocyte apoptosis with a novel magneto-optical nanoparticle. Magn Reson Med 54(3):718–724PubMedGoogle Scholar
  141. 141.
    Wu YL et al (2016) MRI investigation of new approach to improve the recovery of myocardial ischemia reperfusion injury by treatment with intralipid. World J Cardiovasc Dis 6(10):352–371Google Scholar
  142. 142.
    Wu YL et al (2013) Magnetic resonance imaging investigation of macrophages in acute cardiac allograft rejection after heart transplantation. Circ Cardiovasc Imaging 6(6):965–973PubMedGoogle Scholar
  143. 143.
    Cerqueira MD et al (2002) Standardized myocardial segmentation and nomenclature for tomographic imaging of the heart. A statement for healthcare professionals from the Cardiac Imaging Committee of the Council on Clinical Cardiology of the American Heart Association. Int J Cardiovasc Imaging 18(1):539–542PubMedGoogle Scholar
  144. 144.
    Christodoulou A (2015) A subspace approach to accelerated cardiovascular magnetic resonance imaging, in Electrical and Computer Engineering, University of Illinois at Urbana–Champaign: Urbana–Champaign, IL. p 112Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2021

Authors and Affiliations

  1. 1.Department of Developmental Biology, Rangos Research Center Animal Imaging Core, School of MedicineUniversity of PittsburghPittsburghUSA

Personalised recommendations

Cite protocol

Buy options