Hybrid PET/MR imaging in myocardial inflammation post-myocardial infarction

Abstract

Hybrid PET/MR imaging is an emerging imaging modality combining positron emission tomography (PET) and magnetic resonance imaging (MRI) in the same system. Since the introduction of clinical PET/MRI in 2011, it has had some impact (e.g., imaging the components of inflammation in myocardial infarction), but its role could be much greater. Many opportunities remain unexplored and will be highlighted in this review. The inflammatory process post-myocardial infarction has many facets at a cellular level which may affect the outcome of the patient, specifically the effects on adverse left ventricular remodeling, and ultimately prognosis. The goal of inflammation imaging is to track the process non-invasively and quantitatively to determine the best therapeutic options for intervention and to monitor those therapies. While PET and MRI, acquired separately, can image aspects of inflammation, hybrid PET/MRI has the potential to advance imaging of myocardial inflammation. This review contains a description of hybrid PET/MRI, its application to inflammation imaging in myocardial infarction and the challenges, constraints, and opportunities in designing data collection protocols. Finally, this review explores opportunities in PET/MRI: improved registration, partial volume correction, machine learning, new approaches in the development of PET and MRI pulse sequences, and the use of novel injection strategies.

This is a preview of subscription content, access via your institution.

Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6

Abbreviations

AMI:

Acute myocardial infarction

MI:

Myocardial infarction

IOT:

Infarcted obstructed tissue

INOT:

Infarcted not obstructed tissue

RT:

Remote tissue

BOLD:

Blood oxygen level dependent

CEST:

Chemical exchange saturation transfer

SPIO:

Superparamagnetic iron oxide

TSPO:

Translocator protein

References

  1. 1.

    Braunwald E. Shattuck lecture–cardiovascular medicine at the turn of the millennium: Triumphs, concerns, and opportunities. N Engl J Med. 1997;337:1360-9. https://doi.org/10.1056/NEJM199711063371906.

    CAS  Article  PubMed  Google Scholar 

  2. 2.

    Braunwald E. Research advances in heart failure: A compendium. Circ Res. 2013;113:633-45. https://doi.org/10.1161/CIRCRESAHA.113.302254.

    CAS  Article  PubMed  Google Scholar 

  3. 3.

    Eitel I, Kubusch K, Strohm O, Desch S, Mikami Y, De Waha S, et al. Prognostic value and determinants of a hypointense infarct core in T2-weighted cardiac magnetic resonance in acute reperfused ST-elevation-myocardial infarction. Circ Cardiovasc Imaging. 2011;4:354-62. https://doi.org/10.1161/CIRCIMAGING.110.960500.

    Article  PubMed  Google Scholar 

  4. 4.

    Pfeffer MA, Braunwald E. Ventricular remodeling after myocardial infarction: Experimental observations and clinical implications. Circulation. 1990;81:116-72. https://doi.org/10.1161/01.CIR.81.4.1161.

    Article  Google Scholar 

  5. 5.

    Ganame J, Messalli G, Dymarkowski S, Rademakers FE, Desmet W, Van De Werf F, et al. Impact of myocardial haemorrhage on left ventricular function and remodelling in patients with reperfused acute myocardial infarction. Eur Heart J. 2009;30:1440-9. https://doi.org/10.1093/eurheartj/ehp093.

    Article  PubMed  Google Scholar 

  6. 6.

    Kali A, Cokic I, Tang R, Dohnalkova A, Kovarik L, Yang HJ, et al. Persistent microvascular obstruction after myocardial infarction culminates in the confluence of ferric iron oxide crystals, proinflammatory burden, and adverse remodeling. Circ Cardiovasc Imaging. 2016;9:e004996. https://doi.org/10.1161/CIRCIMAGING.115.004996.

    Article  PubMed  PubMed Central  Google Scholar 

  7. 7.

    Kali A, Tang RLQ, Kumar A, Min JK, Dharmakumar R. Detection of acute reperfusion myocardial hemorrhage with cardiac MR imaging: T2 versus T2*. Radiology. 2013;269:387-95. https://doi.org/10.1148/radiol.13122397.

    Article  PubMed  PubMed Central  Google Scholar 

  8. 8.

    Kali A, Kumar A, Cokic I, Tang RLQ, Tsaftaris SA, Friedrich MG, et al. Chronic manifestation of postreperfusion intramyocardial hemorrhage as regional iron deposition: A cardiovascular magnetic resonance study with ex vivo validation. Circ Cardiovasc Imaging. 2013;6:218-28. https://doi.org/10.1161/CIRCIMAGING.112.000133.

    Article  PubMed  Google Scholar 

  9. 9.

    Frangogiannis NG. The inflammatory response in myocardial injury, repair, and remodelling. Nat Rev Cardiol. 2014;11:255-65. https://doi.org/10.1038/nrcardio.2014.28.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  10. 10.

    Weir RAP, Miller AM, Murphy GEJ, Clements S, Steedman T, Connell JMC, et al. Serum soluble ST2: A potential novel mediator in left ventricular and infarct remodeling after acute myocardial infarction. J Am Coll Cardiol. 2010;55:243-50. https://doi.org/10.1016/j.jacc.2009.08.047.

    CAS  Article  PubMed  Google Scholar 

  11. 11.

    Fishbein MC, Maclean D, Maroko PR. The histopathologic evolution of myocardial infarction. Chest. 1978;73:843-9. https://doi.org/10.1378/chest.73.6.843.

    CAS  Article  PubMed  Google Scholar 

  12. 12.

    Frangogiannis NG, Smith CW, Entman ML. The inflammatory response in myocardial infarction. Cardiovasc Res. 2002;53:31-47. https://doi.org/10.1016/S0008-6363(01)00434-5.

    CAS  Article  PubMed  Google Scholar 

  13. 13.

    Hofmann U, Knorr S, Vogel B, Weirather J, Frey A, Ertl G, et al. Interleukin-13 deficiency aggravates healing and remodeling in male mice after experimental myocardial infarction. Circ Hear Fail. 2014;7:822-30. https://doi.org/10.1161/CIRCHEARTFAILURE.113.001020.

    CAS  Article  Google Scholar 

  14. 14.

    Chatelain P, Latour JG, Tran D, de Lorgeril M, Dupras G, Bourassa M. Neutrophil accumulation in experimental myocardial infarcts: Relation with extent of injury and effect of reperfusion. Circulation. 1987;75:1083-90. https://doi.org/10.1161/01.CIR.75.5.1083.

    CAS  Article  PubMed  Google Scholar 

  15. 15.

    Dewald O, Ren G, Duerr GD, Zoerlein M, Klemm C, Gersch C, et al. Of mice and dogs: Species-specific differences in the inflammatory response following myocardial infarction. Am J Pathol. 2004;164:665-77. https://doi.org/10.1016/S0002-9440(10)63154-9.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  16. 16.

    Epelman S, Lavine KJ, Randolph GJ. Origin and functions of tissue macrophages. Immunity. 2014;41:21-35. https://doi.org/10.1016/j.immuni.2014.06.013.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  17. 17.

    Lavine KJ, Epelman S, Uchida K, Weber KJ, Nichols CG, Schilling JD, et al. Distinct macrophage lineages contribute to disparate patterns of cardiac recovery and remodeling in the neonatal and adult heart. Proc Natl Acad Sci. 2014;111:16029-34. https://doi.org/10.1073/pnas.1406508111.

    CAS  Article  PubMed  Google Scholar 

  18. 18.

    Serhan CN, Savill J. Resolution of inflammation: The beginning programs the end. Nat Immunol. 2005;6:1191-7. https://doi.org/10.1038/ni1276.

    CAS  Article  PubMed  Google Scholar 

  19. 19.

    Warnatsch A, Ioannou M, Wang Q, Papayannopoulos V. Neutrophil extracellular traps license macrophages for cytokine production in atherosclerosis. Science. 2015;349:316-20. https://doi.org/10.1126/science.aaa8064.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  20. 20.

    Yan Y, Gause KT, Kamphuis MMJ, Ang CS, O’Brien-Simpson NM, Lenzo JC, et al. Differential roles of the protein corona in the cellular uptake of nanoporous polymer particles by monocyte and macrophage cell lines. ACS Nano. 2013;7:10960-70. https://doi.org/10.1021/nn404481f.

    CAS  Article  PubMed  Google Scholar 

  21. 21.

    Bronte V, Pittet MJ. The spleen in local and systemic regulation of immunity. Immunity. 2013;39:806-18. https://doi.org/10.1016/j.immuni.2013.10.010.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  22. 22.

    Swirski FK, Nahrendorf M, Etzrodt M, Wildgruber M, Cortez-Retamozo V, Panizzi P, et al. Identification of splenic reservoir monocytes and their deployment to inflammatory sites. Science. 2009;325:612-6. https://doi.org/10.1126/science.1175202.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  23. 23.

    Dutta P, Nahrendorf M. Monocytes in myocardial infarction. Arterioscler Thromb Vasc Biol. 2015;35:1066-70. https://doi.org/10.1161/ATVBAHA.114.304652.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  24. 24.

    Hettinger J, Richards DM, Hansson J, Barra MM, Joschko AC, Krijgsveld J, et al. Origin of monocytes and macrophages in a committed progenitor. Nat Immunol. 2013;14:821-30. https://doi.org/10.1038/ni.2638.

    CAS  Article  PubMed  Google Scholar 

  25. 25.

    Koelwyn GJ, Corr EM, Erbay E, Moore KJ. Regulation of macrophage immunometabolism in atherosclerosis. Nat Immunol. 2018;19:526-37. https://doi.org/10.1038/s41590-018-0113-3.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  26. 26.

    Tavakoli S, Zamora D, Ullevig S, Asmis R. Bioenergetic profiles diverge during macrophage polarization: Implications for the interpretation of 18F-FDG PET imaging of atherosclerosis. J Nucl Med. 2013;54:1661-7. https://doi.org/10.2967/jnumed.112.119099.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  27. 27.

    Dobaczewski M, Xia Y, Bujak M, Gonzalez-Quesada C, Frangogiannis NG. CCR5 signaling suppresses inflammation and reduces adverse remodeling of the infarcted heart, mediating recruitment of regulatory T cells. Am J Pathol. 2010;176:2177-87. https://doi.org/10.2353/ajpath.2010.090759.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  28. 28.

    Frangogiannis NG. Regulation of the inflammatory response in cardiac repair. Circ Res. 2012;110:159-73. https://doi.org/10.1161/CIRCRESAHA.111.243162.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  29. 29.

    Seropian IM, Toldo S, Van Tassell BW, Abbate A. Anti-inflammatory strategies for ventricular remodeling following St-segment elevation acute myocardial infarction. J Am Coll Cardiol. 2014;63:1593-603. https://doi.org/10.1016/j.jacc.2014.01.014.

    CAS  Article  PubMed  Google Scholar 

  30. 30.

    Satomi T, Ogawa M, Mori I, Ishino S, Kubo K, Magata Y, et al. Comparison of contrast agents for atherosclerosis imaging using cultured macrophages: FDG versus ultrasmall superparamagnetic iron oxide. J Nucl Med. 2013;54:999-1004. https://doi.org/10.2967/jnumed.112.110551.

    CAS  Article  PubMed  Google Scholar 

  31. 31.

    Zhu L, Zhao Q, Yang T, Ding W, Zhao Y. Cellular metabolism and macrophage functional polarization. Int Rev Immunol. 2015;34:82-100. https://doi.org/10.3109/08830185.2014.969421.

    CAS  Article  PubMed  Google Scholar 

  32. 32.

    Recalcati S, Locati M, Marini A, Santambrogio P, Zaninotto F, De Pizzol M, et al. Differential regulation of iron homeostasis during human macrophage polarized activation. Eur J Immunol. 2010;40:824-35. https://doi.org/10.1002/eji.200939889.

    CAS  Article  PubMed  Google Scholar 

  33. 33.

    Corna G, Campana L, Pignatti E, Castiglioni A, Tagliafico E, Bosurgi L, et al. Polarization dictates iron handling by inflammatory and alternatively activated macrophages. Haematologica. 2010;95:1814-22. https://doi.org/10.3324/haematol.2010.023879.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  34. 34.

    Goldhawk D, Gelman N, Sengupta A, Prato F. The interface between iron metabolism and gene-based iron contrast for MRI. Magn Reson Insights. 2015;8:9-14. https://doi.org/10.4137/mri.s23555.

    Article  PubMed  PubMed Central  Google Scholar 

  35. 35.

    Young AA, Kramer CM, Ferrari VA, Axel L, Reichek N. Three-dimensional left ventricular deformation in hypertrophic cardiomyopathy. Circulation. 1994;90:854-67. https://doi.org/10.1161/01.CIR.90.2.854.

    CAS  Article  PubMed  Google Scholar 

  36. 36.

    Everaars H, Robbers LFHJ, Götte M, Croisille P, Hirsch A, Teunissen PFA, et al. Strain analysis is superior to wall thickening in discriminating between infarcted myocardium with and without microvascular obstruction. Eur Radiol. 2018;28:5171-81. https://doi.org/10.1007/s00330-018-5493-0.

    Article  PubMed  PubMed Central  Google Scholar 

  37. 37.

    Lotz J, Meier C, Leppert A, Galanski M. Cardiovascular flow measurement with imaging: Basic facts and implementation. RadioGraphics. 2002;22:651-71.

    Article  Google Scholar 

  38. 38.

    Vasanawala SS, Hanneman K, Alley MT, Hsiao A. Congenital heart disease assessment with 4D flow MRI. J Magn Reson Imaging. 2015;42:870-86. https://doi.org/10.1002/jmri.24856.

    Article  PubMed  PubMed Central  Google Scholar 

  39. 39.

    Garcia J, Sheitt H, Bristow MS, Lydell C, Howarth AG, Heydari B, et al. Left atrial vortex size and velocity distributions by 4D flow MRI in patients with paroxysmal atrial fibrillation: Associations with age and CHA2 DS2-VASc risk score. J Magn Reson Imaging. 2019. https://doi.org/10.1002/jmri.26876.

    Article  PubMed  Google Scholar 

  40. 40.

    Thornhill RE, Prato FS, Pereira RS, Wisenberg G, Sykes J. Examining a canine model of stunned myocardium with Gd-DTPA-enhanced MRI. Magn Reson Med. 2001;45:864-71. https://doi.org/10.1002/mrm.1115.

    CAS  Article  PubMed  Google Scholar 

  41. 41.

    Thornhill RE, Prato FS, Wisenberg G, White JA, Nowell J, Sauer A. Feasibility of the single-bolus strategy for measuring the partition coefficient of Gd-DTPA in patients with myocardial infarction: Independence of image delay time and maturity of scar. Magn Reson Med. 2006;55:780-9. https://doi.org/10.1002/mrm.20830.

    Article  PubMed  Google Scholar 

  42. 42.

    Pereira RS, Prato FS, Sykes J, Wisenberg G. Assessment of myocardial viability using MRI during a constant infusion of GD-DTPA: Further studies at early and late periods of reperfusion. Magn Reson Med. 1999;42:60-8. https://doi.org/10.1002/(SICI)1522-2594(199907)42:1%3c60:AID-MRM10%3e3.0.CO;2-9.

    CAS  Article  PubMed  Google Scholar 

  43. 43.

    Flacke SJ, Fischer SE. Measurement of the gadopentetate dimeglumine partition coefficient in human myocardium in vivo: Normal distribution and elevation in acute and chronic infarction. Radiology. 2001;218:703-10.

    CAS  Article  Google Scholar 

  44. 44.

    Kim RJ, Fieno DS, Parrish TB, Harris K, Chen EL, Simonetti O, et al. Relationship of MRI delayed contrast enhancement to irreversible injury, infarct age, and contractile function. Circulation. 1999;100:1992-2002. https://doi.org/10.1161/01.CIR.100.19.1992.

    CAS  Article  PubMed  Google Scholar 

  45. 45.

    Lekx KS, Prato FS, Sykes J, Wisenberg G. The partition coefficient of Gd-DTPA reflects maintained tissue viability in a canine model of chronic significant coronary stenosis. J Cardiovasc Magn Reson. 2004;6:33-42. https://doi.org/10.1081/JCMR-120027803.

    Article  PubMed  Google Scholar 

  46. 46.

    Pereira RS, Wisenberg G, Prato FS, Yvorchuk K. Clinical assessment of myocardial viability using MRI during a constant infusion of Gd-DTPA. Magn Reson Mater Physics, Biol Med. 2000;11:104-13. https://doi.org/10.1016/S1352-8661(00)00093-4.

    CAS  Article  Google Scholar 

  47. 47.

    Wu KC, Heldman AW, Brinker JA, Hare JM, Lima JAC. Microvascular obstruction after nonsurgical septal reduction for the treatment of hypertrophic cardiomyopathy. Circulation. 2001;104:1868. https://doi.org/10.1161/hc4001.096355.

    CAS  Article  PubMed  Google Scholar 

  48. 48.

    Kim RJ, Wu E, Rafael A, Chen EL, Parker MA, Simonetti O, et al. The use of contrast-enhanced magnetic resonance imaging to identify reversible myocardial dysfunction. N Engl J Med. 2000;343:1445-53. https://doi.org/10.1016/s1062-1458(01)00166-0.

    CAS  Article  PubMed  Google Scholar 

  49. 49.

    Kali A, Cokic I, Tang RLQ, Yang HJ, Sharif B, Marbán E, et al. Determination of location, size, and transmurality of chronic myocardial infarction without exogenous contrast media by using cardiac magnetic resonance imaging at 3 T. Circ Cardiovasc Imaging. 2014;7:471-81. https://doi.org/10.1161/CIRCIMAGING.113.001541.

    Article  PubMed  PubMed Central  Google Scholar 

  50. 50.

    Kali A, Choi EY, Sharif B, Kim YJ, Bi X, Spottiswoode B, et al. Native T1 mapping by 3-T CMR imaging for characterization of chronic myocardial infarctions. JACC Cardiovasc Imaging. 2015;8:1019-30. https://doi.org/10.1016/j.jcmg.2015.04.018.

    Article  PubMed  Google Scholar 

  51. 51.

    Wang G, Yang H-J, Kali A, Cokic I, Tang R, Xie G, et al. Influence of myocardial hemorrhage on staging of reperfused myocardial infarctions with T2 cardiac magnetic resonance imaging. JACC Cardiovasc Imaging. 2019;12:693-703. https://doi.org/10.1016/j.jcmg.2018.01.018.

    CAS  Article  PubMed  Google Scholar 

  52. 52.

    Jerosch-Herold M, Seethamraju RT, Swingen CM, Wilke NM, Stillman AE. Analysis of myocardial perfusion MRI. J Magn Reson Imaging. 2004;19:758-70. https://doi.org/10.1002/jmri.20065.

    Article  PubMed  Google Scholar 

  53. 53.

    Bellamy DD, Pereira RS, McKenzie CA, Prato FS, Drost DJ, Sykes J, et al. Gd-DTPA bolus tracking in the myocardium using T1 fast acquisition relaxation mapping (T1 FARM). Magn Reson Med. 2001;46:555-64. https://doi.org/10.1002/mrm.1227.

    CAS  Article  PubMed  Google Scholar 

  54. 54.

    Ishida M, Schuster A, Morton G, Chiribiri A, Hussain S, Paul M, et al. Development of a universal dual-bolus injection scheme for the quantitative assessment of myocardial perfusion cardiovascular magnetic resonance. J Cardiovasc Magn Reson. 2011;13:28. https://doi.org/10.1186/1532-429X-13-28x.

    Article  PubMed  PubMed Central  Google Scholar 

  55. 55.

    Papanastasiou G, Williams MC, Kershaw LE, Dweck MR, Alam S, Mirsadraee S, et al. Measurement of myocardial blood flow by cardiovascular magnetic resonance perfusion: Comparison of distributed parameter and Fermi models with single and dual bolus. J Cardiovasc Magn Reson. 2015. https://doi.org/10.1186/s12968-015-0125-1.

    Article  PubMed  PubMed Central  Google Scholar 

  56. 56.

    Tong CY, Prato FS, Wisenberg G, Lee TY, Carroll E, Sandler D, et al. Measurement of the extraction efficiency and distribution volume for Gd-DTPA in normal and diseased canine myocardium. Magn Reson Med. 1993;30:337-46. https://doi.org/10.1002/mrm.1910300310.

    CAS  Article  PubMed  Google Scholar 

  57. 57.

    Maddahi J, Packard RRS. Cardiac PET perfusion tracers: Current status and future directions. Semin Nucl Med. 2014;44:333-43. https://doi.org/10.1053/j.semnuclmed.2014.06.011.

    Article  PubMed  PubMed Central  Google Scholar 

  58. 58.

    Kunze KP, Rischpler C, Hayes C, Ibrahim T, Laugwitz KL, Haase A, et al. Measurement of extracellular volume and transit time heterogeneity using contrast enhanced myocardial perfusion MRI in patients after acute myocardial infarction. Magn Reson Med. 2017;77:2320-30. https://doi.org/10.1002/mrm.26320.

    CAS  Article  PubMed  Google Scholar 

  59. 59.

    Schelbert HR, Phelps ME, Huang SC, MacDonald NS, Hansen H, Selin C, et al. N-13 ammonia as an indicator of myocardial blood flow. Circulation. 1981;63:1259-72. https://doi.org/10.1161/01.CIR.63.6.1259.

    CAS  Article  PubMed  Google Scholar 

  60. 60.

    Li D, Dhawale P, Rubin PJ, Haacke EM, Gropler RJ. Myocardial signal response to dipyridamole and dobutamine: Demonstration of the BOLD effect using a doubleecho gradient-echo sequence. Magn Reson Med. 1996;36:16-20. https://doi.org/10.1002/mrm.1910360105.

    CAS  Article  PubMed  Google Scholar 

  61. 61.

    Yang H-J, Ilkary O, Dey D, Sykes J, Klein M, Butler J, et al. Accurate needle-free assessment of myocardial oxygenation for ischemic heart disease. Sci Transl Med. 2019;11:eaat4407.

    CAS  Article  Google Scholar 

  62. 62.

    Van Zijl PCM, Yadav NN. Chemical exchange saturation transfer (CEST): What is in a name and what isn’t ? Magn Reson Med. 2011;65:927-48. https://doi.org/10.1002/mrm.22761.

    CAS  Article  Google Scholar 

  63. 63.

    Liu G, Song X, Chan KWY, McMahon MT. Nuts and bolts of chemical exchange saturation transfer MRI. NMR Biomed. 2013;26:810-28. https://doi.org/10.1002/nbm.2899.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  64. 64.

    Vinogradov E, Sherry AD, Lenkinski RE. CEST: From basic principles to applications, challenges and opportunities. J Magn Reson. 2013;229:155-72. https://doi.org/10.1016/j.jmr.2012.11.024.

    CAS  Article  PubMed  Google Scholar 

  65. 65.

    Jones CK, Schlosser MJ, Van Zijl PCM, Pomper MG, Golay X, Zhou J. Amide proton transfer imaging of human brain tumors at 3T. Magn Reson Med. 2006;56:585-92. https://doi.org/10.1002/mrm.20989.

    Article  PubMed  PubMed Central  Google Scholar 

  66. 66.

    Cai K, Haris M, Singh A, Kogan F, Greenberg JH, Hariharan H, et al. Magnetic resonance imaging of glutamate. Nat Med. 2012;18:302-6. https://doi.org/10.1038/nm.2615.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  67. 67.

    Haris M, Nanga RRPR, Singh A, Cai K, Kogan F, Hariharan H, et al. Exchange rates of creatine kinase metabolites: Feasibility of imaging creatine by chemical exchange saturation transfer MRI. NMR Biomed. 2013;25:1305-9. https://doi.org/10.1002/nbm.2792.Exchange.

    Article  Google Scholar 

  68. 68.

    Lindeman LR, Randtke EA, High RA, Jones KM, Howison CM, Pagel MD. A comparison of exogenous and endogenous CEST MRI methods for evaluating in vivo pH. Magn Reson Med. 2018;79:2766-72. https://doi.org/10.1002/mrm.26924.

    Article  PubMed  Google Scholar 

  69. 69.

    Jones KM, Randtke EA, Yoshimaru ES, Howison CM, Chalasani P, Klein RR, et al. Clinical translation of tumor acidosis measurements with AcidoCEST MRI. Mol Imaging Biol. 2017;19:617-25. https://doi.org/10.1007/s11307-016-1029-7.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  70. 70.

    Chan KWY, McMahon MT, Kato Y, Liu G, Bulte JWM, Bhujwalla ZM, et al. Natural D-glucose as a biodegradable MRI contrast agent for detecting cancer. Magn Reson Med. 2012;68:1764-73. https://doi.org/10.1002/mrm.24520.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  71. 71.

    Walker-Samuel S, Ramasawmy R, Torrealdea F, Rega M, Rajkumar V, Johnson SP, et al. In vivo imaging of glucose uptake and metabolism in tumors. Nat Med. 2013;19:1067-72. https://doi.org/10.1038/nm.3252.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  72. 72.

    Rivlin M, Horev J, Tsarfaty I, Navon G. Molecular imaging of tumors and metastases using chemical exchange saturation transfer (CEST) MRI. Sci Rep. 2013;3:3045. https://doi.org/10.1038/srep03045.

    Article  PubMed  PubMed Central  Google Scholar 

  73. 73.

    Jones KM, Pollard AC, Pagel MD. Clinical applications of chemical exchange saturation transfer (CEST) MRI. J Magn Reson Imaging. 2018;47:11-27. https://doi.org/10.1002/jmri.25838.

    Article  Google Scholar 

  74. 74.

    Haris M, Singh A, Cai K, Kogan F, McGarvey J, Debrosse C, et al. A technique for in vivo mapping of myocardial creatine kinase metabolism. Nat Med. 2014;20:209-14. https://doi.org/10.1038/nm.3436.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  75. 75.

    Zhou Z, Nguyen C, Chen Y, Shaw JL, Deng Z, Xie Y, et al. Optimized CEST cardiovascular magnetic resonance for assessment of metabolic activity in the heart. J Cardiovasc Magn Reson. 2017;19:95. https://doi.org/10.1186/s12968-017-0411-1.

    Article  PubMed  PubMed Central  Google Scholar 

  76. 76.

    Pumphrey A, Yang Z, Ye S, Powell DK, Thalman S, Watt DS, et al. Advanced cardiac chemical exchange saturation transfer (cardioCEST) MRI for in vivo cell tracking and metabolic imaging. NMR Biomed. 2016;29:74-83. https://doi.org/10.1002/nbm.3451.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  77. 77.

    Petz A, Grandoch M, Gorski DJ, Abrams M, Piroth MA, Schneckmann R, et al. Cardiac hyaluronan synthesis is critically involved in the cardiac macrophage response and promotes healing after ischemia reperfusion injury. Circ Res. 2019;124:1433-47. https://doi.org/10.1161/CIRCRESAHA.118.313285.

    CAS  Article  PubMed  Google Scholar 

  78. 78.

    Bulte JWM, Daldrup-Link HE. Clinical tracking of cell transfer and cell transplantation: Trials and tribulations. Radiology. 2018. https://doi.org/10.1148/radiol.2018180449.

    Article  PubMed  PubMed Central  Google Scholar 

  79. 79.

    Suzuki Y, Cunningham CH, Noguchi KI, Chen IY, Weissman IL, Yeung AC, et al. In vivo serial evaluation of superparamagnetic iron-oxide labeled stem cells by off resonance positive contrast. Magn Reson Med. 2008;60:1269-75. https://doi.org/10.1002/mrm.21816.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  80. 80.

    Makela AV, Foster PJ. Imaging macrophage distribution and density in mammary tumors and lung metastases using fluorine-19 MRI cell tracking. Magn Reson Med. 2018;80:1138-47. https://doi.org/10.1002/mrm.27081.

    CAS  Article  PubMed  Google Scholar 

  81. 81.

    Rothe M, Jahn A, Weiss K, Hwang JH, Szendroedi J, Kelm M, et al. In vivo 19 F MR inflammation imaging after myocardial infarction in a large animal model at 3 T. Magn Reson Mater Physics, Biol Med. 2019;32:5-13. https://doi.org/10.1007/s10334-018-0714-8.

    CAS  Article  Google Scholar 

  82. 82.

    Bönner F, Merx MW, Klingel K, Begovatz P, Flögel U, Sager M, et al. Monocyte imaging after myocardial infarction with 19FMRI at 3 T: A pilot study in explanted porcine hearts. Eur Heart J Cardiovasc Imaging. 2015;16:612-20. https://doi.org/10.1093/ehjci/jev008.

    Article  PubMed  Google Scholar 

  83. 83.

    Dassanayake PSB, Goldhawk DE. Monocyte MRI relaxation rates are regulated by extracellular iron and hepcidin. MSc Thesis, West University; 2019

  84. 84.

    Liu L, Alizadeh K, Donnelly SC, Dassanayake P, Hou TT, McGirr R, et al. MagA expression attenuates iron export activity in undifferentiated multipotent P19 cells. PLoS ONE. 2019;14:1-19. https://doi.org/10.1371/journal.pone.0217842.

    CAS  Article  Google Scholar 

  85. 85.

    Yang HJ, Sharif B, Pang J, Kali A, Bi X, Cokic I, et al. Free-breathing, motioncorrected, highly efficient whole heart T2 mapping at 3T with hybrid radial-cartesian trajectory. Magn Reson Med. 2016;75:126-36. https://doi.org/10.1002/mrm.25576.

    Article  PubMed  Google Scholar 

  86. 86.

    Goldhawk DE, Rohani R, Sengupta A, Gelman N, Prato FS. Using the magnetosome to model effective gene-based contrast for magnetic resonance imaging. Wiley Interdiscip Rev Nanomed Nanobiotechnol. 2012;4:378-88. https://doi.org/10.1002/wnan.1165.

    CAS  Article  PubMed  Google Scholar 

  87. 87.

    Goldhawk DE, Gelman N, Thompson RT, Prato FS. Forming magnetosome-like nanoparticles in mammalian cells for molecular MRI. In: Design and applications of nanoparticles in biomedical imaging; 2017. p. 187-203

  88. 88.

    Ylä-Herttuala S, Baker AH. Cardiovascular gene therapy: Past, present, and future. Mol Ther. 2017;25:1095-106. https://doi.org/10.1016/j.ymthe.2017.03.027.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  89. 89.

    Wollenweber T, Bengel FM. Cardiac molecular imaging. Semin Nucl Med. 2014;44:386-97. https://doi.org/10.1053/j.semnuclmed.2014.05.002.

    Article  PubMed  Google Scholar 

  90. 90.

    Larson SR, Pieper JA, Hulten EA, Ficaro EP, Corbett JR, Murthy VL, et al. Characterization of a highly effective preparation for suppression of myocardial glucose utilization. J Nucl Cardiol. 2019. https://doi.org/10.1007/s12350-019-01786-w.

    Article  PubMed  Google Scholar 

  91. 91.

    Borchert T, Beitar L, Langer LBN, Polyak A, Wester H-J, Ross TL, et al. Dissecting the target leukocyte subpopulations of clinically relevant inflammation radiopharmaceuticals. J Nucl Cardiol. 2019. https://doi.org/10.1007/s12350-019-01929-z.

    Article  PubMed  Google Scholar 

  92. 92.

    Wisenberg G, Thiessen JD, Pavlovsky W, Butler J, Wilk B, Prato FS. Same day comparison of PET/CT and PET/MR in patients with cardiac sarcoidosis. J Nucl Cardiol. 2019. https://doi.org/10.1007/s12350-018-01578-8.

    Article  PubMed  Google Scholar 

  93. 93.

    Murthy VL, Bateman TM, Beanlands RS, Berman DS, Borges-Neto S, Chareonthaitawee P, et al. Clinical quantification of myocardial blood flow using PET: Joint position paper of the SNMMI cardiovascular council and the ASNC. J Nucl Med. 2018;59:273-93. https://doi.org/10.2967/jnumed.117.201368.

    CAS  Article  PubMed  Google Scholar 

  94. 94.

    Rust TC, DiBella EVR, McGann CJ, Christian PE, Hoffman JM, Kadrmas DJ. Rapid dual-injection single-scan 13N-ammonia PET for quantification of rest and stress myocardial blood flows. Phys Med Biol. 2006;51:5347-62. https://doi.org/10.1088/0031-9155/51/20/018.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  95. 95.

    Ory D, Celen S, Verbruggen A, Bormans G. PET radioligands for in vivo visualization of neuroinflammation. Curr Pharm Des. 2014;20:5897-913. https://doi.org/10.2174/1381612820666140613120212.

    CAS  Article  PubMed  Google Scholar 

  96. 96.

    Vivash L, OBrien TJ. Imaging microglial activation with TSPO PET: Lighting up neurologic diseases? J Nucl Med. 2015;57:165-8. https://doi.org/10.2967/jnumed.114.141713.

    CAS  Article  PubMed  Google Scholar 

  97. 97.

    Thackeray JT, Hupe HC, Wang Y, Bankstahl JP, Berding G, Ross TL, et al. Myocardial inflammation predicts remodeling and neuroinflammation after myocardial infarction. J Am Coll Cardiol. 2018;71:263-75. https://doi.org/10.1016/j.jacc.2017.11.024.

    CAS  Article  PubMed  Google Scholar 

  98. 98.

    Van De Wiele C, Sathekge M, Maes A. Targeting monocytes and macrophages by means of SPECT and PET. Q J Nucl Med Mol Imaging. 2014;58:269-75.

    Google Scholar 

  99. 99.

    Bansal A, Pandey MK, Demirhan YE, Nesbitt JJ, Crespo-Diaz RJ, Terzic A, et al. Novel 89Zr cell labeling approach for PET-based cell trafficking studies. EJNMMI Res. 2015. https://doi.org/10.1186/s13550-015-0098-y.

    Article  PubMed  PubMed Central  Google Scholar 

  100. 100.

    Prato FS, Butler J, Sykes J, Keenliside L, Blackwood KJ, Thompson RT, et al. Can the inflammatory response be evaluated using 18F-FDG within zones of microvascular obstruction after myocardial infarction? J Nucl Med. 2015;56:299-304. https://doi.org/10.2967/jnumed.114.147835.

    CAS  Article  PubMed  Google Scholar 

  101. 101.

    Blackwood KJ, Lewden B, Wells RG, Sykes J, Stodilka RZ, Wisenberg G, et al. In vivo SPECT quantification of transplanted cell survival after engraftment using 111In-tropolone in infarcted canine myocardium. J Nucl Med. 2009;50:927-35. https://doi.org/10.2967/jnumed.108.058966.

    Article  PubMed  Google Scholar 

  102. 102.

    Amsalem Y, Mardor Y, Feinberg MS, Landa N, Miller L, Daniels D, et al. Iron-oxide labeling and outcome of transplanted mesenchymal stem cells in the infarcted myocardium. Circulation. 2007;116:38-45. https://doi.org/10.1161/CIRCULATIONAHA.106.680231.

    CAS  Article  Google Scholar 

  103. 103.

    Yao Y, Li Y, Ma G, Liu N, Ju S, Jin J, et al. In vivo magnetic resonance imaging of injected endothelial progenitor cells after myocardial infarction in rats. Mol Imaging Biol. 2011;13:303-13. https://doi.org/10.1007/s11307-010-0359-0.

    Article  PubMed  Google Scholar 

  104. 104.

    Parashurama N, Ahn BC, Ziv K, Ito K, Paulmurugan R, Willmann JK, et al. Multimodality molecular imaging of cardiac cell transplantation: Part I Reporter gene design, characterization, and optical in vivo imaging of bone marrow stromal cells after myocardial infarction. Radiology. 2016;280:815-25. https://doi.org/10.1148/radiol.2016140049.

    Article  PubMed  PubMed Central  Google Scholar 

  105. 105.

    Miyagawa M, Anton M, Haubner R, Simoes MV, Städele C, Erhardt W, et al. PET of cardiac transgene expression: comparison of 2 approaches based on herpesviral thymidine kinase reporter gene. J Nucl Med. 2004;45:1917-23.

    CAS  PubMed  Google Scholar 

  106. 106.

    Rausch I, Quick HH, Cal-Gonzalez J, Sattler B, Boellaard R, Beyer T. Technical and instrumentational foundations of PET/MRI. Eur J Radiol. 2017;94:A3-13. https://doi.org/10.1016/j.ejrad.2017.04.004.

    Article  PubMed  Google Scholar 

  107. 107.

    Farag A, Thompson RT, Thiessen JD, Butler J, Prato FS, Théberge J. Assessment of a novel 32-channel phased array for cardiovascular hybrid PET/MRI imaging: MRI performance. Eur J Hybrid Imaging. 2019. https://doi.org/10.1186/s41824-019-0061-7.

    Article  PubMed  PubMed Central  Google Scholar 

  108. 108.

    Vontobel J, Liga R, Possner M, Clerc OF, Mikulicic F, Veit-Haibach P, et al. MRbased attenuation correction for cardiac FDG PET on a hybrid PET/MRI scanner: comparison with standard CT attenuation correction. Eur J Nucl Med Mol Imaging. 2015;42:1574-80. https://doi.org/10.1007/s00259-015-3089-3.

    Article  PubMed  Google Scholar 

  109. 109.

    Lau JMC, Laforest R, Sotoudeh H, Nie X, Sharma S, McConathy J, et al. Evaluation of attenuation correction in cardiac PET using PET/MR. J Nucl Cardiol. 2017;24:839-46. https://doi.org/10.1007/s12350-015-0197-1.

    Article  PubMed  Google Scholar 

  110. 110.

    Lewis CE, Prato FS, Drost DJ, Nicholson RL. Comparison of respiratory triggering and gating techniques for the removal of respiratory artifacts in MR imaging. Radiology. 1986;160:803-10. https://doi.org/10.1148/radiology.160.3.3737921.

    CAS  Article  PubMed  Google Scholar 

  111. 111.

    Pang J, Bhat H, Sharif B, Fan Z, Thomson LEJ, Labounty T, et al. Whole-heart coronary MRA with 100% respiratory gating efficiency: Self-navigated threedimensional retrospective image-based motion correction (TRIM). Magn Reson Med. 2014;71:67-74. https://doi.org/10.1002/mrm.24628.

    Article  PubMed  Google Scholar 

  112. 112.

    Kolbitsch C, Ahlman MA, Davies-Venn C, Evers R, Hansen M, Peressutti D, et al. Cardiac and respiratory motion correction for simultaneous cardiac PET/MR. J Nucl Med. 2017;58:846-52. https://doi.org/10.2967/jnumed.115.171728.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  113. 113.

    Munoz C, Neji R, Kunze KP, Nekolla SG, Botnar RM, Prieto C. Respiratory- and cardiac motion-corrected simultaneous whole-heart PET and dual phase coronary MR angiography. Magn Reson Med. 2019;81:1671-84. https://doi.org/10.1002/mrm.27517.

    Article  PubMed  Google Scholar 

  114. 114.

    Feng T, Wang J, Tsui BMW. Dual respiratory and cardiac motion estimation in PET imaging: Methods design and quantitative evaluation. Med Phys. 2018;45:1481-90. https://doi.org/10.1002/mp.12793.

    Article  PubMed  Google Scholar 

  115. 115.

    Robson PM, Trivieri MG, Karakatsanis NA, Padilla M, Abgral R, Dweck MR, et al. Correction of respiratory and cardiac motion in cardiac PET/MR using MR-based motion modeling. Phys Med Biol. 2018;63:225011. https://doi.org/10.1088/1361-6560/aaea97.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  116. 116.

    Petibon Y, Sun T, Han PK, Ma C, El Fakhri G, Ouyang J. MR-based cardiac and respiratory motion correction of PET: Application to static and dynamic cardiac 18FFDG imaging. Phys Med Biol. 2019;64:195009. https://doi.org/10.1088/1361-6560/ab39c2.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  117. 117.

    Klyuzhin IS, Sossi V. PET image reconstruction and deformable motion correction using unorganized point clouds. IEEE Trans Med Imaging. 2017;36:1263-75. https://doi.org/10.1109/TMI.2017.2675989.

    Article  PubMed  Google Scholar 

  118. 118.

    Berg E, Gill H, Marik J, Ogasawara A, Williams SP, van Dongen GAMS, et al. Totalbody PET and highly stable chelators together enable meaningful 89 Zr-antibody-PET studies up to 30 days post-injection. J Nucl Med. 2019. https://doi.org/10.2967/jnumed.119.230961.

    Article  PubMed  PubMed Central  Google Scholar 

  119. 119.

    Barton GP, Vildberg L, Goss K, Aggarwal N, Eldridge M, McMillan AB. Simultaneous determination of dynamic cardiac metabolism and function using PET/MRI. J Nucl Cardiol. 2018. https://doi.org/10.1007/s12350-018-1287-7.

    Article  PubMed  Google Scholar 

  120. 120.

    Wilk B, Wisenberg G, Sykes J, Butler J, Kovacs MS, Thompson RT, et al. Quantifying inflammation in infarcted myocardial tissue with severely reduced flow: A hybrid PET/MRI approach using a prolonged constant infusion of 18F-FDG and Gd-DTPA. In: Society of Nuclear Medicine and Molecular Imaging 2018 annual meet; 2018

  121. 121.

    Kunze KP, Nekolla SG, Rischpler C, Zhang SHL, Hayes C, Langwieser N, et al. Myocardial perfusion quantification using simultaneously acquired 13NH3- ammonia PET and dynamic contrast-enhanced MRI in patients at rest and stress. Magn Reson Med. 2018. https://doi.org/10.1002/mrm.27213.

    Article  PubMed  PubMed Central  Google Scholar 

  122. 122.

    Feng L, Grimm R, Block KT, Chandarana H, Kim S, Xu J, et al. Golden-angle radial sparse parallel MRI: Combination of compressed sensing, parallel imaging, and golden-angle radial sampling for fast and flexible dynamic volumetric MRI. Magn Reson Med. 2014;72:707-17. https://doi.org/10.1002/mrm.24980.

    Article  PubMed  Google Scholar 

  123. 123.

    Piekarski E, Chitiboi T, Ramb R, Feng L, Axel L. Use of self-gated radial cardiovascular magnetic resonance to detect and classify arrhythmias (atrial fibrillation and premature ventricular contraction). J Cardiovasc Magn Reson. 2016;18:83. https://doi.org/10.1186/s12968-016-0306-6.

    Article  PubMed  PubMed Central  Google Scholar 

  124. 124.

    Piekarski E, Chitiboi T, Ramb R, Latson LA, Bhatla P, Feng L, et al. Two-dimensional XD-GRASP provides better image quality than conventional 2D cardiac cine MRI for patients who cannot suspend respiration. Magn Reson Mater Phys Biol Med. 2018;31:49-59. https://doi.org/10.1007/s10334-017-0655-7.

    Article  Google Scholar 

  125. 125.

    Piccini D, Feng L, Bonanno G, Coppo S, Yerly J, Lim RP, et al. Four-dimensional respiratory motion-resolved whole heart coronary MR angiography. Magn Reson Med. 2017;77:1473-84. https://doi.org/10.1002/mrm.26221.

    Article  PubMed  Google Scholar 

  126. 126.

    Heydari B, Kwong RY, Jerosch-Herold M. Technical advances and clinical applications of quantitative myocardial blood flow imaging with cardiac MRI. Prog Cardiovasc Dis. 2015;57:615-22. https://doi.org/10.1016/j.pcad.2015.02.003.

    Article  PubMed  Google Scholar 

  127. 127.

    Yang H-J, Christodoulou AG, Sykes J, Bi X, Cokic I, Prato FS, et al. Beat-by-beat dynamic assessment of myocardial oxygenation with highly time-resolved free breathing, ungated cardiac T2 BOLD MRI using a low-rank tensor formulation. In: International Society of Magnetic Resonance in Medicine 2018 annual meet; 2018

  128. 128.

    Davidson CQ, Phenix CP, Tai T, Khaper N, Lees SJ. Searching for novel PET radiotracers: Imaging cardiac perfusion, metabolism and inflammation. Am J Nucl Med Mol Imaging. 2018;8:200-27.

    CAS  PubMed  PubMed Central  Google Scholar 

  129. 129.

    Tofts PS. Modeling tracer kinetics in dynamic Gd-DTPA MR imaging. J Magn Reson Imaging. 1997;7:91-101. https://doi.org/10.1002/jmri.1880070113.

    CAS  Article  PubMed  Google Scholar 

  130. 130.

    Richard MA, Fouquet JP, Lebel R, Lepage M. MRI-guided derivation of the input function for PET kinetic modeling. PET Clin. 2016;11:193-202. https://doi.org/10.1016/j.cpet.2015.09.003.

    Article  PubMed  Google Scholar 

  131. 131.

    Qi Q, Fox MS, Bartha R, Hoffman L, Lee TY, Thiessen JD. Comparison of glucose-CEST with perfusion and glycolysis measurements in a C6 rat model of glioma. In: World molecular imaging congress 2018; 2018

  132. 132.

    Dey D, Slomka PJ, Leeson P, Comaniciu D, Shrestha S, Sengupta PP, et al. Artificial intelligence in cardiovascular imaging. J Am Coll Cardiol. 2019;73:1317-35. https://doi.org/10.1016/j.jacc.2018.12.054.

    Article  PubMed  PubMed Central  Google Scholar 

  133. 133.

    Tan LK, McLaughlin RA, Lim E, Abdul Aziz YF, Liew YM. Fully automated segmentation of the left ventricle in cine cardiac MRI using neural network regression. J Magn Reson Imaging. 2018;48:140-52. https://doi.org/10.1002/jmri.25932.

    Article  PubMed  Google Scholar 

  134. 134.

    Couzin-Frankel J. When mice mislead. Science. 2013;342:922-3. https://doi.org/10.1126/science.342.6161.922.

    CAS  Article  PubMed  Google Scholar 

  135. 135.

    Gore JC, Yankeelov TE, Peterson TE, Avison MJ. Molecular imaging without radiopharmaceuticals? J Nucl Med. 2009;50:999-1007. https://doi.org/10.2967/jnumed.108.059576.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  136. 136.

    Meikle SR, Kench P, Kassiou M, Banati RB. Small animal SPECT and its place in the matrix of molecular imaging technologies. Phys Med Biol. 2005;50:R45-61. https://doi.org/10.1088/0031-9155/50/22/R01.

    CAS  Article  PubMed  Google Scholar 

  137. 137.

    Moses WW. Fundamental limits of spatial resolution in PET. Nucl Instruments Methods Phys Res Sect A. 2011;648:S236-40. https://doi.org/10.1016/j.nima.2010.11.092.

    CAS  Article  Google Scholar 

  138. 138.

    Grimm D. From bark to bedside. Science. 2016;353:638-40. https://doi.org/10.1126/science.353.6300.638.

    CAS  Article  PubMed  Google Scholar 

  139. 139.

    Thompson K, Wisenberg G, Sykes J, Terry Thompson R. MRI/MRS evaluation of cariporide in a canine long-term model of reperfused ischemic insults. J Magn Reson Imaging. 2003;17:136-41. https://doi.org/10.1002/jmri.10222.

    Article  PubMed  Google Scholar 

  140. 140.

    Diesbourg LD, Prato FS, Wisenberg G, Drost DJ, Marshall TP, Carroll SE, et al. Quantification of myocardial blood flow and extracellular volumes using a bolus injection of Gd-DTPA: Kinetic modeling in canine ischemic disease. Magn Reson Med. 1992;23:239-53. https://doi.org/10.1002/mrm.1910230205.

    CAS  Article  PubMed  Google Scholar 

  141. 141.

    Thornhill RE, Prato FS, Wisenberg G. The assessment of myocardial viability: A review of current diagnostic imaging approaches. J Cardiovasc Magn Reson. 2002;4:381-410. https://doi.org/10.1081/jcmr-120013301.

    Article  PubMed  Google Scholar 

  142. 142.

    Weinsaft JW, Klem I, Judd RM. MRI for the assessment of myocardial viability. Magn Reson Imaging Clin N Am. 2007;25:35-6. https://doi.org/10.1016/j.mric.2007.08.007.

    Article  Google Scholar 

  143. 143.

    Saeed M, Lund G, Wendland MF, Bremerich J, Weinmann HJ, Higgins CB. Magnetic resonance characterization of the peri-infarction zone of reperfused myocardial infarction with necrosis-specific and extracellular nonspecific contrast media. Circulation. 2001;103:871-6. https://doi.org/10.1161/01.CIR.103.6.871.

    CAS  Article  PubMed  Google Scholar 

  144. 144.

    Frangogiannis NG, Mendoza LH, Ren G, Akrivakis S, Jackson PL, Michael LH, et al. MCSF expression is induced in healing myocardial infarcts and may regulate monocyte and endothelial cell phenotype. Am J Physiol Circ Physiol. 2003;285:H483-92. https://doi.org/10.1152/ajpheart.01016.2002.

    CAS  Article  Google Scholar 

  145. 145.

    Frantz S, Nahrendorf M. Cardiac macrophages and their role in ischaemic heart disease. Cardiovasc Res. 2014;102:240-8. https://doi.org/10.1093/cvr/cvu025.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  146. 146.

    Lee WW, Marinelli B, Van Der Laan AM, Sena BF, Gorbatov R, Leuschner F, et al. PET/MRI of inflammation in myocardial infarction. J Am Coll Cardiol. 2012;59:153-63. https://doi.org/10.1016/j.jacc.2011.08.066.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  147. 147.

    Hamirani YS, Wong A, Kramer CM, Salerno M. Effect of microvascular obstruction and intramyocardial hemorrhage by CMR on LV remodeling and outcomes after myocardial infarction: A systematic review and meta-analysis. JACC Cardiovasc Imaging. 2014;7:940-52. https://doi.org/10.1016/j.jcmg.2014.06.012.

    Article  PubMed  PubMed Central  Google Scholar 

  148. 148.

    Maxwell MP, Hearse DJ, Yellon DM. Species variation in the coronary collateral circulation during regional myocardial ischaemia: A critical determinant of the rate of evolution and extent of myocardial infarction. Cardiovasc Res. 1987;21:737-46. https://doi.org/10.1093/cvr/21.10.737.

    CAS  Article  PubMed  Google Scholar 

  149. 149.

    Phelps ME, Huang SC, Hoffman EJ, Selin C, Sokoloff L, Kuhl DE. Tomographic measurement of local cerebral glucose metabolic rate in humans with (F-18)2-fluoro-2-deoxy-d-glucose: Validation of method. Ann Neurol. 1979;6:371-88. https://doi.org/10.1002/ana.410060502.

    CAS  Article  PubMed  Google Scholar 

  150. 150.

    Dweck MR, Abgral R, Trivieri MG, Robson PM, Karakatsanis N, Mani V, et al. Hybrid magnetic resonance imaging and positron emission tomography with fluorodeoxyglucose to diagnose active cardiac sarcoidosis. JACC Cardiovasc Imaging. 2018;11:94-107. https://doi.org/10.1016/j.jcmg.2017.02.021.

    Article  PubMed  Google Scholar 

  151. 151.

    Cherry SR, Sorenson JA, Phelps ME. Physics in nuclear medecine. Philadelphia: Elsevier Science; 2003.

    Google Scholar 

  152. 152.

    Chen BC, Legant WR, Wang K, Shao L, Milkie DE, Davidson MW, et al. Lattice light-sheet microscopy: Imaging molecules to embryos at high spatiotemporal resolution. Science. 2014. https://doi.org/10.1126/science.1257998.

    Article  PubMed  PubMed Central  Google Scholar 

  153. 153.

    Ephrat P, Albert GC, Roumeliotis MB, Belton M, Prato FS, Carson JJL. Localization of spherical lesions in tumor-mimicking phantoms by 3D sparse array photoacoustic imaging. Med Phys. 2010;37:1619-28. https://doi.org/10.1118/1.3352785.

    Article  PubMed  Google Scholar 

  154. 154.

    James ML, Gambhir SS. A molecular imaging primer: Modalities, imaging agents, and applications. Physiol Rev. 2012;92:897-965. https://doi.org/10.1152/physrev.00049.2010.

    CAS  Article  PubMed  Google Scholar 

Download references

Disclosures

All authors have no potential conflicts of interest.

Author information

Affiliations

Authors

Corresponding author

Correspondence to B. Wilk BSc.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

The authors of this article have provided a PowerPoint file, available for download at SpringerLink, which summarizes the contents of the paper and is free for re-use at meetings and presentations. Search for the article DOI on SpringerLink.com.

The authors have also provided an audio summary of the article, which is available to download as ESM, or to listen to via the JNC/ASNC Podcast.

Funding

Wilk, B. is supported by an Ontario Graduate scholarship and a Lawson Internal Research Fund. This work was supported in part by Ontario Research Fund RS7-021 and Canadian Foundation for Innovation No. 11358.

Electronic supplementary material

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Wilk, B., Wisenberg, G., Dharmakumar, R. et al. Hybrid PET/MR imaging in myocardial inflammation post-myocardial infarction. J. Nucl. Cardiol. 27, 2083–2099 (2020). https://doi.org/10.1007/s12350-019-01973-9

Download citation

Keywords

  • Myocardial biology
  • inflammation
  • myocardial ischemia and infarction
  • MRI
  • PET
  • hybrid imaging