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14.02.2024 | Review

Recent Progress of Cardiac MRI for Nuclear Medicine Professionals

verfasst von: Yeon Hyeon Choe, Sung Mok Kim

Erschienen in: Nuclear Medicine and Molecular Imaging

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Abstract

Recent technical innovation enables faster and more reliable cardiac magnetic resonance (CMR) imaging than before. Artificial intelligence is used in improving image resolution, fast scanning, and automated analysis of CMR. Fast CMR techniques such as compressed sensing technique enable fast cine, perfusion, and late gadolinium-enhanced imaging and improve patient throughput and widening CMR indications. CMR feature-tracking technique gives insight on diastolic function parameters of ventricles and atria with prognostic implications. Myocardial parametric mapping became to be included in the routine CMR protocol. CMR fingerprinting enables simultaneous quantification of myocardial T1 and T2. These parameters may give information on myocardial alteration in the preclinical stages in various myocardial diseases. Four-dimensional flow imaging shows hemodynamic characteristics in or through the cardiovascular structures visually and gives quantitative values of vortex, kinetic energy, and wall-shear stress. In conclusion, CMR is an essential modality in the diagnosis of various cardiovascular diseases, especially myocardial diseases. Recent progress in CMR techniques promotes more widespread use of CMR in clinical practice. This review summarizes recent updates in CMR technologies and clinical research.

Busse A, Rajagopal R, Yücel S, Beller E, Öner A, Streckenbach F, et al. Cardiac MRI-update 2020. Radiologe. 2020;60(Suppl 1):33–40. https://doi.org/10.1007/s00117-020-00687-1.CrossRefPubMed

Daubert MA, Tailor T, James O, Shaw LJ, Douglas PS, Koweek L. Multimodality cardiac imaging in the 21st century: evolution, advances and future opportunities for innovation. Br J Radiol. 2021;94(1117):20200780. https://doi.org/10.1259/bjr.20200780.CrossRefPubMed

Dodd JD, Leipsic J. Cardiovascular CT and MRI in 2019: review of key articles. Radiology. 2020;297(1):17–30. https://doi.org/10.1148/radiol.2020200605.CrossRefPubMed

Chowdhary A, Garg P, Das A, Nazir MS, Plein S. Cardiovascular magnetic resonance imaging: emerging techniques and applications. Heart. 2021. https://doi.org/10.1136/heartjnl-2019-315669.CrossRefPubMed

Eck BL, Flamm SD, Kwon DH, Tang WHW, Vasquez CP, Seiberlich N. Cardiac magnetic resonance fingerprinting: trends in technical development and potential clinical applications. Prog Nucl Magn Reson Spectrosc. 2021;122:11–22. https://doi.org/10.1016/j.pnmrs.2020.10.001.CrossRefPubMed

Seraphim A, Knott KD, Augusto J, Bhuva AN, Manisty C, Moon JC. Quantitative cardiac MRI. J Magn Reson Imaging. 2020;51(3):693–711. https://doi.org/10.1002/jmri.26789.CrossRefPubMed

Tang F, Bai C, Zhao XX, Yuan WF. Artificial intelligence and myocardial contrast enhancement pattern. Curr Cardiol Rep. 2020;22(8):77. https://doi.org/10.1007/s11886-020-01306-0.CrossRefPubMed

Velasco C, Fletcher TJ, Botnar RM, Prieto C. Artificial intelligence in cardiac magnetic resonance fingerprinting. Front Cardiovasc Med. 2022;9:1009131. https://doi.org/10.3389/fcvm.2022.1009131.CrossRefPubMedPubMedCentral

Nielles-Vallespin S, Scott A, Ferreira P, Khalique Z, Pennell D, Firmin D. Cardiac diffusion: technique and practical applications. J Magn Reson Imaging. 2020;52(2):348–68. https://doi.org/10.1002/jmri.26912.CrossRefPubMed

10.

Liu Y, Hamilton J, Jiang Y, Seiberlich N. Cardiac MRF using rosette trajectories for simultaneous myocardial T(1), T(2), and proton density fat fraction mapping. Front Cardiovasc Med. 2022;9: 977603. https://doi.org/10.3389/fcvm.2022.977603.CrossRefPubMedPubMedCentral

11.

Weingärtner S, Demirel ÖB, Gama F, Pierce I, Treibel TA, Schulz-Menger J, et al. Cardiac phase-resolved late gadolinium enhancement imaging. Front Cardiovasc Med. 2022;9: 917180. https://doi.org/10.3389/fcvm.2022.917180.CrossRefPubMedPubMedCentral

12.

Dong Z, Si G, Zhu X, Li C, Hua R, Teng J, et al. Diagnostic performance and safety of a novel ferumoxytol-enhanced coronary magnetic resonance angiography. Circ Cardiovasc Imaging. 2023;16(7):580–90. https://doi.org/10.1161/circimaging.123.015404.CrossRefPubMed

13.

Ayala C, Luo H, Godines K, Alghuraibawi W, Ahn S, Rehwald W, et al. Individually tailored spatial-spectral pulsed CEST MRI for ratiometric mapping of myocardial energetic species at 3T. Magn Reson Med. 2023. https://doi.org/10.1002/mrm.29801.CrossRefPubMed

14.

Buechel RR, Ciancone D, Bakula A, von Felten E, Schmidt GA, Patriki D, et al. Long-term impact of myocardial inflammation on quantitative myocardial perfusion-a descriptive PET/MR myocarditis study. Eur J Nucl Med Mol Imaging. 2023. https://doi.org/10.1007/s00259-023-06314-0.CrossRefPubMedPubMedCentral

15.

Bakermans AJ, Boekholdt SM, de Vries DK, Reckman YJ, Farag ES, de Heer P, et al. Quantification of myocardial creatine and triglyceride content in the human heart: precision and accuracy of in vivo proton magnetic resonance spectroscopy. J Magn Reson Imaging. 2021. https://doi.org/10.1002/jmri.27531.CrossRefPubMedPubMedCentral

16.

Abulaiti A, Zhang Q, Huang H, Ding S, Shayiti M, Wang S, et al. The value of the cardiac magnetic resonance intravoxel incoherent motion technique in evaluating microcirculatory dysfunction in hypertrophic cardiomyopathy. J Interv Cardiol. 2023;2023:4611602. https://doi.org/10.1155/2023/4611602.CrossRefPubMedPubMedCentral

17.

Lara Hernandez KA, Rienmüller T, Baumgartner D, Baumgartner C. Deep learning in spatiotemporal cardiac imaging: a review of methodologies and clinical usability. Comput Biol Med. 2021;130: 104200. https://doi.org/10.1016/j.compbiomed.2020.104200.CrossRefPubMed

18.

Alskaf E, Dutta U, Scannell CM, Chiribiri A. Deep learning applications in myocardial perfusion imaging, a systematic review and meta-analysis. Inform Med Unlocked. 2022;32: 101055. https://doi.org/10.1016/j.imu.2022.101055.CrossRefPubMedPubMedCentral

19.

Wang ZC, Fan ZZ, Liu XY, Zhu MJ, Jiang SS, Tian S, et al. Deep learning for discrimination of hypertrophic cardiomyopathy and hypertensive heart disease on MRI native T1 Maps. J Magn Reson Imaging. 2023. https://doi.org/10.1002/jmri.28904.CrossRefPubMed

20.

Chen BH, Wu CW, An DA, Zhang JL, Zhang YH, Yu LZ, et al. A deep learning method for the automated assessment of paradoxical pulsation after myocardial infarction using multicenter cardiac MRI data. Eur Radiol. 2023. https://doi.org/10.1007/s00330-023-09807-6.CrossRefPubMedPubMedCentral

21.

Kim YC, Kim KR, Choe YH. Automatic myocardial segmentation in dynamic contrast enhanced perfusion MRI using Monte Carlo dropout in an encoder-decoder convolutional neural network. Comput Methods Programs Biomed. 2020;185: 105150. https://doi.org/10.1016/j.cmpb.2019.105150.CrossRefPubMed

22.

Kim YC, Kim KR, Choi K, Kim M, Chung Y, Choe YH. EVCMR: a tool for the quantitative evaluation and visualization of cardiac MRI data. Comput Biol Med. 2019;111: 103334. https://doi.org/10.1016/j.compbiomed.2019.103334.CrossRefPubMed

23.

Xu B, Kocyigit D, Grimm R, Griffin BP, Cheng F. Applications of artificial intelligence in multimodality cardiovascular imaging: a state-of-the-art review. Prog Cardiovasc Dis. 2020;63(3):367–76. https://doi.org/10.1016/j.pcad.2020.03.003.CrossRefPubMed

24.

Fan L, Shen D, Haji-Valizadeh H, Naresh NK, Carr JC, Freed BH, et al. Rapid dealiasing of undersampled, non-Cartesian cardiac perfusion images using U-net. NMR Biomed. 2020;33(5): e4239. https://doi.org/10.1002/nbm.4239.CrossRefPubMedPubMedCentral

25.

Unal HB, Beaulieu T, Rivero LZ, Dharmakumar R, Sharif B. Retrospective detection and suppression of dark-rim artifacts in first-pass perfusion cardiac MRI enabled by deep learning. Annu Int Conf IEEE Eng Med Biol Soc. 2021;2021:4079–85. https://doi.org/10.1109/embc46164.2021.9630270.CrossRefPubMedPubMedCentral

26.

Yan X, Luo Y, Chen X, Chen EZ, Liu Q, Zou L, et al. From compressed-sensing to deep learning MR: comparative biventricular cardiac function analysis in a patient cohort. J Magn Reson Imaging. 2023. https://doi.org/10.1002/jmri.28899.CrossRefPubMed

27.

Küstner T, Armanious K, Yang J, Yang B, Schick F, Gatidis S. Retrospective correction of motion-affected MR images using deep learning frameworks. Magn Reson Med. 2019;82(4):1527–40. https://doi.org/10.1002/mrm.27783.CrossRefPubMed

28.

Fahmy AS, Rowin EJ, Chan RH, Manning WJ, Maron MS, Nezafat R. Improved quantification of myocardium scar in late gadolinium enhancement images: deep learning based image fusion approach. J Magn Reson Imaging. 2021. https://doi.org/10.1002/jmri.27555.CrossRefPubMedPubMedCentral

29.

Zabihollahy F, Rajan S, Ukwatta E. Machine learning-based segmentation of left ventricular myocardial fibrosis from magnetic resonance imaging. Curr Cardiol Rep. 2020;22(8):65. https://doi.org/10.1007/s11886-020-01321-1.CrossRefPubMed

30.

Gao Y, Zhou Z, Zhang B, Guo S, Bo K, Li S, et al. Deep learning-based prognostic model using non-enhanced cardiac cine MRI for outcome prediction in patients with heart failure. Eur Radiol. 2023. https://doi.org/10.1007/s00330-023-09785-9.CrossRefPubMedPubMedCentral

31.

Moustafa A, Khan MS, Alsamman MA, Jamal F, Atalay MK. Prognostic significance of T1 mapping parameters in heart failure with preserved ejection fraction: a systematic review. Heart Fail Rev. 2021;26(6):1325–31. https://doi.org/10.1007/s10741-020-09958-4.CrossRefPubMed

32.

Pan C, Zhang Z, Luo L, Wu W, Jia T, Lu L, et al. Cardiac T1 and T2 mapping showed myocardial involvement in recovered COVID-19 patients initially considered devoid of cardiac damage. J Magn Reson Imaging. 2021. https://doi.org/10.1002/jmri.27534.CrossRefPubMedPubMedCentral

33.

Pan JA, Kerwin MJ, Salerno M. Native T1 Mapping, extracellular volume mapping, and late gadolinium enhancement in cardiac amyloidosis: a meta-analysis. JACC Cardiovasc Imaging. 2020;13(6):1299–310. https://doi.org/10.1016/j.jcmg.2020.03.010.CrossRefPubMedPubMedCentral

34.

Wheen P, Armstrong R, Daly CA. Recent advances in T1 and T2 mapping in the assessment of fulminant myocarditis by cardiac magnetic resonance. Curr Cardiol Rep. 2020;22(7):47. https://doi.org/10.1007/s11886-020-01295-0.CrossRefPubMed

35.

Yang MX, He Y, Ma M, Zhao Q, Xu HY, Xia CC, et al. Characterization of infarcted myocardium by T1-mapping and its association with left ventricular remodeling. Eur J Radiol. 2021;137: 109590. https://doi.org/10.1016/j.ejrad.2021.109590.CrossRefPubMed

36.

Yue P, Xu Z, Wan K, Tan Y, Xu Y, Xie X, et al. Multiparametric mapping by cardiovascular magnetic resonance imaging in cardiac tumors. J Cardiovasc Magn Reson. 2023;25(1):37. https://doi.org/10.1186/s12968-023-00938-9.CrossRefPubMedPubMedCentral

37.

Roller FC, Fuest S, Meyer M, Harth S, Gündüz D, Bauer P, et al. Assessment of cardiac involvement in Fabry Disease (FD) with native T1 mapping. Rofo. 2019;191(10):932–9. https://doi.org/10.1055/a-0836-2723.CrossRefPubMed

38.

Krittayaphong R, Zhang S, Saiviroonporn P, Viprakasit V, Tanapibunpon P, Komoltri C, et al. Detection of cardiac iron overload with native magnetic resonance T1 and T2 mapping in patients with thalassemia. Int J Cardiol. 2017;248:421–6. https://doi.org/10.1016/j.ijcard.2017.06.100.CrossRefPubMed

39.

Ferreira VM, Piechnik SK. CMR Parametric mapping as a tool for myocardial tissue characterization. Korean Circ J. 2020;50(8):658–76. https://doi.org/10.4070/kcj.2020.0157.CrossRefPubMedPubMedCentral

40.

Shin SH, Kim SM, Cho SJ, Choe YH. Longitudinal changes in the myocardial T1 relaxation time, extracellular volume fraction, and left ventricular function in asymptomatic men. J Cardiovasc Dev Dis. 2023;10(6). https://doi.org/10.3390/jcdd10060252.

41.

Warnica W, Al-Arnawoot A, Stanimirovic A, Thavendiranathan P, Wald RM, Pakkal M, et al. Clinical Impact of Cardiac MRI T1 and T2 Parametric mapping in patients with suspected cardiomyopathy. Radiology. 2022;305(2):319–26. https://doi.org/10.1148/radiol.220067.CrossRefPubMed

42.

Messroghli DR, Moon JC, Ferreira VM, Grosse-Wortmann L, He T, Kellman P, et al. Clinical recommendations for cardiovascular magnetic resonance mapping of T1, T2, T2* and extracellular volume: a consensus statement by the Society for Cardiovascular Magnetic Resonance (SCMR) endorsed by the European Association for Cardiovascular Imaging (EACVI). J Cardiovasc Magn Reson. 2017;19(1):75. https://doi.org/10.1186/s12968-017-0389-8.CrossRefPubMedPubMedCentral

43.

Ferreira VM, Schulz-Menger J, Holmvang G, Kramer CM, Carbone I, Sechtem U, et al. Cardiovascular magnetic resonance in nonischemic myocardial inflammation: expert recommendations. J Am Coll Cardiol. 2018;72(24):3158–76. https://doi.org/10.1016/j.jacc.2018.09.072.CrossRefPubMed

44.

Kalapos A, Szabó L, Dohy Z, Kiss M, Merkely B, Gyires-Tóth B, et al. Automated T1 and T2 mapping segmentation on cardiovascular magnetic resonance imaging using deep learning. Front Cardiovasc Med. 2023;10:1147581. https://doi.org/10.3389/fcvm.2023.1147581.CrossRefPubMedPubMedCentral

45.

Kim YC, Kim KR, Lee H, Choe YH. Fast calculation software for modified Look-Locker inversion recovery (MOLLI) T1 mapping. BMC Med Imaging. 2021;21(1):26. https://doi.org/10.1186/s12880-021-00558-8.CrossRefPubMedPubMedCentral

46.

Qi H, Lv Z, Hu J, Xu J, Botnar R, Prieto C, et al. Accelerated 3D free-breathing high-resolution myocardial T(1ρ) mapping at 3 Tesla. Magn Reson Med. 2022;88(6):2520–31. https://doi.org/10.1002/mrm.29417.CrossRefPubMed

47.

Si D, Kong X, Guo R, Cheng L, Ning Z, Chen Z, et al. Single breath-hold three-dimensional whole-heart T(2) mapping with low-rank plus sparse reconstruction. NMR Biomed. 2023;36(8): e4924. https://doi.org/10.1002/nbm.4924.CrossRefPubMed

48.

Bustin A, Witschey WRT, van Heeswijk RB, Cochet H, Stuber M. Magnetic resonance myocardial T1ρ mapping : technical overview, challenges, emerging developments, and clinical applications. J Cardiovasc Magn Reson. 2023;25(1):34. https://doi.org/10.1186/s12968-023-00940-1.CrossRefPubMedPubMedCentral

49.

Piechnik SK, Neubauer S, Ferreira VM. State-of-the-art review: stress T1 mapping-technical considerations, pitfalls and emerging clinical applications. MAGMA. 2018;31(1):131–41. https://doi.org/10.1007/s10334-017-0649-5.CrossRefPubMed

50.

Liu A, Wijesurendra RS, Francis JM, Robson MD, Neubauer S, Piechnik SK, et al. Adenosine stress and rest T1 mapping can differentiate between ischemic, infarcted, remote, and normal myocardium without the need for gadolinium contrast agents. JACC Cardiovasc Imaging. 2016;9(1):27–36. https://doi.org/10.1016/j.jcmg.2015.08.018.CrossRefPubMedPubMedCentral

51.

Ma P, Liu J, Hu Y, Zhou X, Shang Y, Wang J. Histologic validation of stress cardiac magnetic resonance T1-mapping techniques for detection of coronary microvascular dysfunction in rabbits. Int J Cardiol. 2022;347:76–82. https://doi.org/10.1016/j.ijcard.2021.10.137.CrossRefPubMed

52.

Ma P, Liu J, Hu Y, Chen L, Liang H, Zhou X, et al. Stress CMR T1-mapping technique for assessment of coronary microvascular dysfunction in a rabbit model of type II diabetes mellitus: Validation against histopathologic changes. Front Cardiovasc Med. 2022;9:1066332. https://doi.org/10.3389/fcvm.2022.1066332.CrossRefPubMed

53.

Halfmann MC, Müller L, von Henning U, Kloeckner R, Schöler T, Kreitner KF, et al. Cardiac MRI-based right-to-left ventricular blood pool T2 relaxation times ratio correlates with exercise capacity in patients with chronic heart failure. J Cardiovasc Magn Reson. 2023;25(1):33. https://doi.org/10.1186/s12968-023-00943-y.CrossRefPubMedPubMedCentral

54.

Rizk J. 4D flow MRI applications in congenital heart disease. Eur Radiol. 2021;31(2):1160–74. https://doi.org/10.1007/s00330-020-07210-z.CrossRefPubMed

55.

Jamalidinan F, Hassanabad AF, François CJ, Garcia J. Four-dimensional-flow magnetic resonance imaging of the aortic valve and thoracic aorta. Radiol Clin North Am. 2020;58(4):753–63. https://doi.org/10.1016/j.rcl.2020.02.008.CrossRefPubMed

56.

Corrias G, Cocco D, Suri JS, Meloni L, Cademartiri F, Saba L. Heart applications of 4D flow. Cardiovasc Diagn Ther. 2020;10(4):1140–9. https://doi.org/10.21037/cdt.2020.02.08.CrossRefPubMedPubMedCentral

57.

Juffermans JF, Minderhoud SCS, Wittgren J, Kilburg A, Ese A, Fidock B, et al. Multicenter consistency assessment of valvular flow quantification with automated valve tracking in 4D flow CMR. JACC Cardiovasc Imaging. 2021. https://doi.org/10.1016/j.jcmg.2020.12.014.CrossRefPubMed

58.

Garcia J, Barker AJ, Markl M. The role of imaging of flow patterns by 4D flow MRI in aortic stenosis. JACC Cardiovasc Imaging. 2019;12(2):252–66. https://doi.org/10.1016/j.jcmg.2018.10.034.CrossRefPubMed

59.

Warmerdam E, Krings GJ, Leiner T, Grotenhuis HB. Three-dimensional and four-dimensional flow assessment in congenital heart disease. Heart. 2020;106(6):421–6. https://doi.org/10.1136/heartjnl-2019-315797.CrossRefPubMed

60.

Soulat G, Alattar Y, Ladouceur M, Craiem D, Pascaner A, Gencer U, et al. Discordance between 2D and 4D flow in the assessment of pulmonary regurgitation severity: a right ventricular remodeling follow-up study. Eur Radiol. 2023;33(8):5455–64. https://doi.org/10.1007/s00330-023-09502-6.CrossRefPubMed

61.

Bissell MM, Raimondi F, Ait Ali L, Allen BD, Barker AJ, Bolger A, et al. 4D Flow cardiovascular magnetic resonance consensus statement: 2023 update. J Cardiovasc Magn Reson. 2023;25(1):40. https://doi.org/10.1186/s12968-023-00942-z.CrossRefPubMedPubMedCentral

62.

Khalique Z, Ferreira PF, Scott AD, Nielles-Vallespin S, Firmin DN, Pennell DJ. Diffusion tensor cardiovascular magnetic resonance imaging: a clinical perspective. JACC Cardiovasc Imaging. 2020;13(5):1235–55. https://doi.org/10.1016/j.jcmg.2019.07.016.CrossRefPubMed

63.

Das A, Kelly C, Teh I, Stoeck CT, Kozerke S, Chowdhary A et al. Acute microstructural changes after ST-segment elevation myocardial infarction assessed with diffusion tensor imaging. Radiology. 2021:203208. https://doi.org/10.1148/radiol.2021203208.

64.

Ponsiglione A, Stanzione A, Cuocolo R, Ascione R, Gambardella M, De Giorgi M, et al. Cardiac CT and MRI radiomics: systematic review of the literature and radiomics quality score assessment. Eur Radiol. 2022;32(4):2629–38. https://doi.org/10.1007/s00330-021-08375-x.CrossRefPubMed

65.

Ma Q, Ma Y, Wang X, Li S, Yu T, Duan W, et al. A radiomic nomogram for prediction of major adverse cardiac events in ST-segment elevation myocardial infarction. Eur Radiol. 2021;31(2):1140–50. https://doi.org/10.1007/s00330-020-07176-y.CrossRefPubMed

66.

Wang J, Bravo L, Zhang J, Liu W, Wan K, Sun J, et al. Radiomics analysis derived from LGE-MRI predict sudden cardiac death in participants with hypertrophic cardiomyopathy. Front Cardiovasc Med. 2021;8: 766287. https://doi.org/10.3389/fcvm.2021.766287.CrossRefPubMedPubMedCentral

67.

Prakken NHJ, Besson FL, Borra RJH, Büther F, Buechel RR, Catana C, et al. PET/MRI in practice: a clinical centre survey endorsed by the European Association of Nuclear Medicine (EANM) and the EANM Forschungs GmbH (EARL). Eur J Nucl Med Mol Imaging. 2023;50(10):2927–34. https://doi.org/10.1007/s00259-023-06308-y.CrossRefPubMed

68.

Rajiah PS, Kalisz K, Broncano J, Goerne H, Collins JD, François CJ, et al. Myocardial strain evaluation with cardiovascular MRI: physics, principles, and clinical applications. Radiographics. 2022;42(4):968–90. https://doi.org/10.1148/rg.210174.CrossRefPubMed

69.

Amzulescu MS, De Craene M, Langet H, Pasquet A, Vancraeynest D, Pouleur AC, et al. Myocardial strain imaging: review of general principles, validation, and sources of discrepancies. Eur Heart J Cardiovasc Imaging. 2019;20(6):605–19. https://doi.org/10.1093/ehjci/jez041.CrossRefPubMedPubMedCentral

70.

Wang Y, Sun C, Ghadimi S, Auger DC, Croisille P, Viallon M, et al. StrainNet: improved myocardial strain analysis of cine MRI by deep learning from DENSE. Radiol Cardiothorac Imaging. 2023;5(3): e220196. https://doi.org/10.1148/ryct.220196.CrossRefPubMedPubMedCentral

71.

Wang TKM, Kwon DH, Griffin BP, Flamm SD, Popović ZB. Defining the reference range for left ventricular strain in healthy patients by cardiac MRI measurement techniques: systematic review and meta-analysis. AJR Am J Roentgenol. 2020. https://doi.org/10.2214/ajr.20.24264.CrossRefPubMed

72.

Oka S, Kai T, Hoshino K, Watanabe K, Nakamura J, Abe M, et al. Effects of empagliflozin in different phases of diabetes mellitus-related cardiomyopathy: a prospective observational study. BMC Cardiovasc Disord. 2021;21(1):217. https://doi.org/10.1186/s12872-021-02024-3.CrossRefPubMedPubMedCentral

73.

Erley J, Genovese D, Tapaskar N, Alvi N, Rashedi N, Besser SA, et al. Echocardiography and cardiovascular magnetic resonance based evaluation of myocardial strain and relationship with late gadolinium enhancement. J Cardiovasc Magn Reson. 2019;21(1):46. https://doi.org/10.1186/s12968-019-0559-y.CrossRefPubMedPubMedCentral

74.

Chen Y, Qian W, Liu W, Zhu Y, Zhou X, Xu Y, et al. Feasibility of single-shot compressed sensing cine imaging for analysis of left ventricular function and strain in cardiac MRI. Clin Radiol. 2021. https://doi.org/10.1016/j.crad.2020.12.024.CrossRefPubMed

75.

Tian D, Sun Y, Guo JJ, Zhao SH, Lu HF, Chen YY et al. 3.0 T unenhanced Dixon water-fat separation whole-heart coronary magnetic resonance angiography: compressed-sensing sensitivity encoding imaging versus conventional 2D sensitivity encoding imaging. Int J Cardiovasc Imaging. 2023. https://doi.org/10.1007/s10554-023-02878-y.

76.

Varga-Szemes A, Halfmann M, Schoepf UJ, Jin N, Kilburg A, Dargis DM, et al. Highly accelerated compressed-sensing 4D flow for intracardiac flow assessment. J Magn Reson Imaging. 2023;58(2):496–507. https://doi.org/10.1002/jmri.28484.CrossRefPubMed

77.

Jenista ER, Wendell DC, Azevedo CF, Klem I, Judd RM, Kim RJ, et al. Revisiting how we perform late gadolinium enhancement CMR: insights gleaned over 25 years of clinical practice. J Cardiovasc Magn Reson. 2023;25(1):18. https://doi.org/10.1186/s12968-023-00925-0.CrossRefPubMedPubMedCentral

78.

Si D, Wu Y, Xiao J, Qin X, Guo R, Liu B, et al. Three-dimensional high-resolution dark-blood late gadolinium enhancement imaging for improved atrial scar evaluation. Radiology. 2023;307(5): e222032. https://doi.org/10.1148/radiol.222032.CrossRefPubMed

79.

Ohta Y, Tateishi E, Morita Y, Nishii T, Kotoku A, Horinouchi H, et al. Optimization of null point in Look-Locker images for myocardial late gadolinium enhancement imaging using deep learning and a smartphone. Eur Radiol. 2023;33(7):4688–97. https://doi.org/10.1007/s00330-023-09465-8.CrossRefPubMed

80.

Kato S, Azuma M, Nakayama N, Fukui K, Ito M, Saito N, et al. Diagnostic accuracy of whole heart coronary magnetic resonance angiography: a systematic review and meta-analysis. J Cardiovasc Magn Reson. 2023;25(1):36. https://doi.org/10.1186/s12968-023-00949-6.CrossRefPubMedPubMedCentral

81.

Yue X, Yang L, Wang R, Chan Q, Yang Y, Wu X, et al. The diagnostic value of multiparameter cardiovascular magnetic resonance for early detection of light-chain amyloidosis from hypertrophic cardiomyopathy patients. Front Cardiovasc Med. 2022;9:1017097. https://doi.org/10.3389/fcvm.2022.1017097.CrossRefPubMedPubMedCentral

82.

Agibetov A, Kammerlander A, Duca F, Nitsche C, Koschutnik M, Donà C et al. Convolutional neural networks for fully automated diagnosis of cardiac amyloidosis by cardiac magnetic resonance imaging. J Pers Med. 2021;11(12). https://doi.org/10.3390/jpm11121268.

83.

Blissett S, Chocron Y, Kovacina B, Afilalo J. Diagnostic and prognostic value of cardiac magnetic resonance in acute myocarditis: a systematic review and meta-analysis. Int J Cardiovasc Imaging. 2019;35(12):2221–9. https://doi.org/10.1007/s10554-019-01674-x.CrossRefPubMed

84.

Baessler B, Luecke C, Lurz J, Klingel K, Das A, von Roeder M, et al. Cardiac MRI and texture analysis of myocardial T1 and T2 maps in myocarditis with acute versus chronic symptoms of heart failure. Radiology. 2019;292(3):608–17. https://doi.org/10.1148/radiol.2019190101.CrossRefPubMed

85.

Ojha V, Verma M, Pandey NN, Mani A, Malhi AS, Kumar S, et al. Cardiac magnetic resonance imaging in coronavirus disease 2019 (COVID-19): A systematic review of cardiac magnetic resonance imaging findings in 199 patients. J Thorac Imaging. 2021;36(2):73–83. https://doi.org/10.1097/rti.0000000000000574.CrossRefPubMed

86.

Vago H, Szabo L, Szabo Z, Ulakcsai Z, Szogi E, Budai G, et al. Immunological response and temporal associations in myocarditis after COVID-19 vaccination using cardiac magnetic resonance imaging: an amplified T-cell response at the heart of it? Front Cardiovasc Med. 2022;9: 961031. https://doi.org/10.3389/fcvm.2022.961031.CrossRefPubMedPubMedCentral

87.

Cavalcante JL, Shaw KE, Gössl M. Cardiac magnetic resonance imaging midterm follow up of COVID-19 vaccine-associated myocarditis. JACC Cardiovasc Imaging. 2022;15(10):1821–4. https://doi.org/10.1016/j.jcmg.2022.01.008.CrossRefPubMedPubMedCentral

88.

Zhang J, Li Y, Xu Q, Xu B, Wang H. Cardiac magnetic resonance imaging for diagnosis of cardiac sarcoidosis: a meta-analysis. Can Respir J. 2018;2018:7457369. https://doi.org/10.1155/2018/7457369.CrossRefPubMedPubMedCentral

89.

Cheung E, Ahmad S, Aitken M, Chan R, Iwanochko RM, Balter M, et al. Combined simultaneous FDG-PET/MRI with T1 and T2 mapping as an imaging biomarker for the diagnosis and prognosis of suspected cardiac sarcoidosis. Eur J Hybrid Imaging. 2021;5(1):24. https://doi.org/10.1186/s41824-021-00119-w.CrossRefPubMedPubMedCentral

90.

Aitken M, Chan MV, Urzua Fresno C, Farrell A, Islam N, McInnes MDF, et al. Diagnostic accuracy of cardiac MRI versus FDG PET for cardiac sarcoidosis: a systematic review and meta-analysis. Radiology. 2022;304(3):566–79. https://doi.org/10.1148/radiol.213170.CrossRefPubMed

91.

Xiao Z, Zhong J, Zhong L, Dai S, Lu W, Song L, et al. The prognostic value of myocardial salvage index by cardiac magnetic resonance in ST-segment elevation myocardial infarction patients: a systematic review and meta-analysis. Eur Radiol. 2023. https://doi.org/10.1007/s00330-023-09739-1.CrossRefPubMedPubMedCentral

92.

Bodi V, Gavara J, Lopez-Lereu MP, Monmeneu JV, de Dios E, Perez-Sole N, et al. Impact of persistent microvascular obstruction late after STEMI on adverse LV remodeling: a CMR study. JACC Cardiovasc Imaging. 2023;16(7):919–30. https://doi.org/10.1016/j.jcmg.2023.01.021.CrossRefPubMed

93.

Smulders MW, Van Assche LMR, Bekkers S, Nijveldt R, Beijnink CWH, Kim HW, et al. Epicardial surface area of infarction: a stable surrogate of microvascular obstruction in acute myocardial infarction. Circ Cardiovasc Imaging. 2021;14(2): e010918. https://doi.org/10.1161/circimaging.120.010918.CrossRefPubMed

94.

Cha MJ, Lee JH, Jung HN, Kim Y, Choe YH, Kim SM. Cardiac magnetic resonance-tissue tracking for the early prediction of adverse left ventricular remodeling after ST-segment elevation myocardial infarction. Int J Cardiovasc Imaging. 2019;35(11):2095–102. https://doi.org/10.1007/s10554-019-01659-w.CrossRefPubMed

95.

Leung SW, Charnigo RJ, Ratajczak T, Abo-Aly M, Shokri E, Abdel-Latif A, et al. End-systolic circumferential strain derived from cardiac magnetic resonance feature-tracking as a predictor of functional recovery in patients with ST-segment elevation myocardial infarction. J Magn Reson Imaging. 2021;54(6):2000–3. https://doi.org/10.1002/jmri.27772.CrossRefPubMedPubMedCentral

96.

Cui J, Zhao Y, Qian G, Yue X, Luo C, Li T. Cardiac magnetic resonance for the early prediction of reverse left ventricular remodeling in patients with ST-segment elevation myocardial infarction. Eur Radiol. 2023. https://doi.org/10.1007/s00330-023-09907-3.CrossRefPubMed

97.

Wang J, Meng Y, Han S, Hu C, Lu Y, Wu P, et al. Predictive value of total ischaemic time and T1 mapping after emergency percutaneous coronary intervention in acute ST-segment elevation myocardial infarction. Clin Radiol. 2023. https://doi.org/10.1016/j.crad.2023.06.010.CrossRefPubMed

98.

Bergamaschi L, Foà A, Paolisso P, Renzulli M, Angeli F, Fabrizio M, et al. Prognostic role of early cardiac magnetic resonance in myocardial infarction with nonobstructive coronary arteries. JACC Cardiovasc Imaging. 2023. https://doi.org/10.1016/j.jcmg.2023.05.016.CrossRefPubMed

99.

Li XM, Jiang L, Min CY, Yan WF, Shen MT, Liu XJ, et al. Myocardial perfusion imaging by cardiovascular magnetic resonance: research progress and current implementation. Curr Probl Cardiol. 2023;48(6): 101665. https://doi.org/10.1016/j.cpcardiol.2023.101665.CrossRefPubMed

100.

Rahman H, Scannell CM, Demir OM, Ryan M, McConkey H, Ellis H, et al. High-resolution cardiac magnetic resonance imaging techniques for the identification of coronary microvascular dysfunction. JACC Cardiovasc Imaging. 2021;14(5):978–86. https://doi.org/10.1016/j.jcmg.2020.10.015.CrossRefPubMed

101.

Jogiya R, Kozerke S, Morton G, De Silva K, Redwood S, Perera D, et al. Validation of dynamic 3-dimensional whole heart magnetic resonance myocardial perfusion imaging against fractional flow reserve for the detection of significant coronary artery disease. J Am Coll Cardiol. 2012;60(8):756–65. https://doi.org/10.1016/j.jacc.2012.02.075.CrossRefPubMed

102.

Károlyi M, Gotschy A, Polacin M, Plein S, Paetsch I, Jahnke C, et al. Diagnostic performance of 3D cardiac magnetic resonance perfusion in elderly patients for the detection of coronary artery disease as compared to fractional flow reserve. Eur Radiol. 2023;33(1):339–47. https://doi.org/10.1007/s00330-022-09040-7.CrossRefPubMed

103.

Arai AE, Schulz-Menger J, Shah DJ, Han Y, Bandettini WP, Abraham A, et al. Stress perfusion cardiac magnetic resonance vs SPECT imaging for detection of coronary artery disease. J Am Coll Cardiol. 2023;82(19):1828–38. https://doi.org/10.1016/j.jacc.2023.08.046.CrossRefPubMed

104.

Wang S, Patel H, Miller T, Ameyaw K, Miller P, Narang A, et al. Relation of myocardial perfusion reserve and left ventricular ejection fraction in ischemic and nonischemic cardiomyopathy. Am J Cardiol. 2022;174:143–50. https://doi.org/10.1016/j.amjcard.2022.02.022.CrossRefPubMedPubMedCentral

105.

Nagel E, Carerj ML, Arendt CT, Puntmann VO. After ISCHEMIA: is cardiac MRI a reliable gatekeeper for invasive angiography and myocardial revascularization? Herz. 2020;45(5):446–52. https://doi.org/10.1007/s00059-020-04936-w.CrossRefPubMed

106.

Nagel E, Greenwood JP, McCann GP, Bettencourt N, Shah AM, Hussain ST, et al. Magnetic resonance perfusion or fractional flow reserve in coronary disease. N Engl J Med. 2019;380(25):2418–28. https://doi.org/10.1056/NEJMoa1716734.CrossRefPubMed

107.

Miller CD, Mahler SA, Snavely AC, Raman SV, Caterino JM, Clark CL, et al. Cardiac magnetic resonance imaging versus invasive-based strategies in patients with chest pain and detectable to mildly elevated serum troponin: a randomized clinical trial. Circ Cardiovasc Imaging. 2023;16(6): e015063. https://doi.org/10.1161/circimaging.122.015063.CrossRefPubMed

108.

Ommen SR, Mital S, Burke MA, Day SM, Deswal A, Elliott P, et al. 2020 AHA/ACC Guideline for the diagnosis and treatment of patients with hypertrophic cardiomyopathy: a report of the American College of Cardiology/American Heart Association Joint Committee on Clinical Practice Guidelines. Circulation. 2020;142(25):e558–631. https://doi.org/10.1161/cir.0000000000000937.CrossRefPubMed

109.

Freitas P, Ferreira AM, Arteaga-Fernández E, de Oliveira AM, Mesquita J, Abecasis J, et al. The amount of late gadolinium enhancement outperforms current guideline-recommended criteria in the identification of patients with hypertrophic cardiomyopathy at risk of sudden cardiac death. J Cardiovasc Magn Reson. 2019;21(1):50. https://doi.org/10.1186/s12968-019-0561-4.CrossRefPubMedPubMedCentral

110.

Suwa K, Sato R, Iguchi K, Maekawa Y. Four-dimensional flow cardiac MRI for hemodynamic assessment of alcohol septal ablation for hypertrophic obstructive cardiomyopathy with multiple obstructions. Radiology: Cardiothoracic Imaging. 2023;5(5):230074. https://doi.org/10.1148/ryct.230074.

111.

Becker MAJ, van der Lingen ACJ, Cornel JH, van de Ven PM, van Rossum AC, Allaart CP, et al. Septal midwall late gadolinium enhancement in ischemic cardiomyopathy and nonischemic dilated cardiomyopathy-characteristics and prognosis. Am J Cardiol. 2023;201:294–301. https://doi.org/10.1016/j.amjcard.2023.06.042.CrossRefPubMed

112.

Yuan Y, Sun J, Jin D, Zhao S. Quantitative left ventricular mechanical dyssynchrony by magnetic resonance imaging predicts the prognosis of dilated cardiomyopathy. Eur J Radiol. 2023;164: 110847. https://doi.org/10.1016/j.ejrad.2023.110847.CrossRefPubMed

113.

Liu T, Zhou Z, Bo K, Gao Y, Wang H, Wang R, et al. Association between left ventricular global function index and outcomes in patients with dilated cardiomyopathy. Front Cardiovasc Med. 2021;8: 751907. https://doi.org/10.3389/fcvm.2021.751907.CrossRefPubMedPubMedCentral

114.

Shen MT, Li Y, Guo YK, Gao Y, Jiang L, Shi R, et al. The impact of hypertension on left ventricular function and remodeling in non-ischemic dilated cardiomyopathy patients: A 3.0 T MRI Study. J Magn Reson Imaging. 2023;58(1):159–71. https://doi.org/10.1002/jmri.28475.CrossRefPubMed

115.

Seraphim A, Westwood M, Bhuva AN, Crake T, Moon JC, Menezes LJ, et al. Advanced imaging modalities to monitor for cardiotoxicity. Curr Treat Options Oncol. 2019;20(9):73. https://doi.org/10.1007/s11864-019-0672-z.CrossRefPubMedPubMedCentral

116.

Galán-Arriola C, Lobo M, Vílchez-Tschischke JP, López GJ, de Molina-Iracheta A, Pérez-Martínez C, et al. Serial magnetic resonance imaging to identify early stages of anthracycline-induced cardiotoxicity. J Am Coll Cardiol. 2019;73(7):779–91. https://doi.org/10.1016/j.jacc.2018.11.046.CrossRefPubMed

117.

Mahmood SS, Fradley MG, Cohen JV, Nohria A, Reynolds KL, Heinzerling LM, et al. Myocarditis in patients treated with immune checkpoint inhibitors. J Am Coll Cardiol. 2018;71(16):1755–64. https://doi.org/10.1016/j.jacc.2018.02.037.CrossRefPubMedPubMedCentral

118.

Mabudian L, Jordan JH, Bottinor W, Hundley WG. Cardiac MRI assessment of anthracycline-induced cardiotoxicity. Front Cardiovasc Med. 2022;9: 903719. https://doi.org/10.3389/fcvm.2022.903719.CrossRefPubMedPubMedCentral

119.

Jang SY, Kim J, Kim YS, Chang YA, Jung W, Kim HO et al. Clinical features and test indications of 11,087 patients undergoing cardiac magnetic resonance imaging during a decade in a tertiary referral center: a retrospective observational study. Precis Future Med. 2023;7(2):62–73. https://doi.org/10.23838/pfm.2023.00023.

120.

Kozor R, Walker S, Parkinson B, Younger J, Hamilton-Craig C, Selvanayagam JB, et al. Cost-effectiveness of cardiovascular magnetic resonance in diagnosing coronary artery disease in the Australian health care system. Heart Lung Circ. 2021;30(3):380–7. https://doi.org/10.1016/j.hlc.2020.07.008.CrossRefPubMed

121.

Kwong RY, Ge Y, Steel K, Bingham S, Abdullah S, Fujikura K, et al. Cardiac magnetic resonance stress perfusion imaging for evaluation of patients with chest pain. J Am Coll Cardiol. 2019;74(14):1741–55. https://doi.org/10.1016/j.jacc.2019.07.074.CrossRefPubMedPubMedCentral

122.

Pontone G, Andreini D, Guaricci AI, Rota C, Guglielmo M, Mushtaq S et al. The STRATEGY Study (Stress Cardiac Magnetic Resonance Versus Computed Tomography Coronary Angiography for the Management of Symptomatic Revascularized Patients): resources and outcomes impact. Circ Cardiovasc Imaging. 2016;9(10). https://doi.org/10.1161/CIRCIMAGING.116.005171.

123.

Hegde VA, Biederman RW, Mikolich JR. Cardiovascular magnetic resonance imaging-incremental value in a series of 361 patients demonstrating cost savings and clinical benefits: an outcome-based study. Clin Med Insights Cardiol. 2017;11:1179546817710026. https://doi.org/10.1177/1179546817710026.CrossRefPubMedPubMedCentral

Titel: Recent Progress of Cardiac MRI for Nuclear Medicine Professionals
verfasst von: Yeon Hyeon Choe
Sung Mok Kim
Publikationsdatum: 14.02.2024
Verlag: Springer Nature Singapore
Erschienen in: Nuclear Medicine and Molecular Imaging
Print ISSN: 1869-3474
Elektronische ISSN: 1869-3482
DOI: https://doi.org/10.1007/s13139-024-00850-9

Springer Medizin

Abstract

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