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 map** 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.
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs13139-024-00850-9/MediaObjects/13139_2024_850_Fig1_HTML.jpg)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs13139-024-00850-9/MediaObjects/13139_2024_850_Fig2_HTML.png)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs13139-024-00850-9/MediaObjects/13139_2024_850_Fig3_HTML.png)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs13139-024-00850-9/MediaObjects/13139_2024_850_Fig4_HTML.png)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs13139-024-00850-9/MediaObjects/13139_2024_850_Fig5_HTML.png)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs13139-024-00850-9/MediaObjects/13139_2024_850_Fig6_HTML.png)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs13139-024-00850-9/MediaObjects/13139_2024_850_Fig7_HTML.png)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs13139-024-00850-9/MediaObjects/13139_2024_850_Fig8_HTML.png)
Similar content being viewed by others
Data Availability
Not applicable.
Abbreviations
- AI:
-
Artificial intelligence
- CMR:
-
Cardiac magnetic resonance imaging
- CNN:
-
Convolutional neural network
- CAD:
-
Coronary artery disease
- DL:
-
Deep learning
- ECV:
-
Extracellular volume fraction
- HCM:
-
Hypertrophic cardiomyopathy
- LV:
-
Left ventricular
- MACE:
-
Major adverse cardiac events
- MF:
-
Myocardial fibrosis
- LGE:
-
Late gadolinium enhancement
- PET:
-
Positron emission tomography
- STEMI:
-
ST-elevation myocardial infarction
References
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.
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.
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.
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.
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.
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.
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.
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.
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.
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 map**. Front Cardiovasc Med. 2022;9: 977603. https://doi.org/10.3389/fcvm.2022.977603.
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.
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.
Ayala C, Luo H, Godines K, Alghuraibawi W, Ahn S, Rehwald W, et al. Individually tailored spatial-spectral pulsed CEST MRI for ratiometric map** of myocardial energetic species at 3T. Magn Reson Med. 2023. https://doi.org/10.1002/mrm.29801.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
Moustafa A, Khan MS, Alsamman MA, Jamal F, Atalay MK. Prognostic significance of T1 map** 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.
Pan C, Zhang Z, Luo L, Wu W, Jia T, Lu L, et al. Cardiac T1 and T2 map** 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.
Pan JA, Kerwin MJ, Salerno M. Native T1 Map**, extracellular volume map**, 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.
Wheen P, Armstrong R, Daly CA. Recent advances in T1 and T2 map** 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.
Yang MX, He Y, Ma M, Zhao Q, Xu HY, ** and its association with left ventricular remodeling. Eur J Radiol. 2021;137: 109590. https://doi.org/10.1016/j.ejrad.2021.109590.
Yue P, Xu Z, Wan K, Tan Y, Xu Y, ** 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.
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 map**. Rofo. 2019;191(10):932–9. https://doi.org/10.1055/a-0836-2723.
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 map** in patients with thalassemia. Int J Cardiol. 2017;248:421–6. https://doi.org/10.1016/j.ijcard.2017.06.100.
Ferreira VM, Piechnik SK. CMR Parametric map** as a tool for myocardial tissue characterization. Korean Circ J. 2020;50(8):658–76. https://doi.org/10.4070/kcj.2020.0157.
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.
Warnica W, Al-Arnawoot A, Stanimirovic A, Thavendiranathan P, Wald RM, Pakkal M, et al. Clinical Impact of Cardiac MRI T1 and T2 Parametric map** in patients with suspected cardiomyopathy. Radiology. 2022;305(2):319–26. https://doi.org/10.1148/radiol.220067.
Messroghli DR, Moon JC, Ferreira VM, Grosse-Wortmann L, He T, Kellman P, et al. Clinical recommendations for cardiovascular magnetic resonance map** 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.
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.
Kalapos A, Szabó L, Dohy Z, Kiss M, Merkely B, Gyires-Tóth B, et al. Automated T1 and T2 map** segmentation on cardiovascular magnetic resonance imaging using deep learning. Front Cardiovasc Med. 2023;10:1147581. https://doi.org/10.3389/fcvm.2023.1147581.
Kim YC, Kim KR, Lee H, Choe YH. Fast calculation software for modified Look-Locker inversion recovery (MOLLI) T1 map**. BMC Med Imaging. 2021;21(1):26. https://doi.org/10.1186/s12880-021-00558-8.
Qi H, Lv Z, Hu J, Xu J, Botnar R, Prieto C, et al. Accelerated 3D free-breathing high-resolution myocardial T(1ρ) map** at 3 Tesla. Magn Reson Med. 2022;88(6):2520–31. https://doi.org/10.1002/mrm.29417.
Si D, Kong X, Guo R, Cheng L, Ning Z, Chen Z, et al. Single breath-hold three-dimensional whole-heart T(2) map** with low-rank plus sparse reconstruction. NMR Biomed. 2023;36(8): e4924. https://doi.org/10.1002/nbm.4924.
Bustin A, Witschey WRT, van Heeswijk RB, Cochet H, Stuber M. Magnetic resonance myocardial T1ρ map** : 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.
Piechnik SK, Neubauer S, Ferreira VM. State-of-the-art review: stress T1 map**-technical considerations, pitfalls and emerging clinical applications. MAGMA. 2018;31(1):131–41. https://doi.org/10.1007/s10334-017-0649-5.
Liu A, Wijesurendra RS, Francis JM, Robson MD, Neubauer S, Piechnik SK, et al. Adenosine stress and rest T1 map** 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.
Ma P, Liu J, Hu Y, Zhou X, Shang Y, Wang J. Histologic validation of stress cardiac magnetic resonance T1-map** 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.
Ma P, Liu J, Hu Y, Chen L, Liang H, Zhou X, et al. Stress CMR T1-map** 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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
Varga-Szemes A, Halfmann M, Schoepf UJ, ** 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.
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.
Si D, Wu Y, **ao 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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
Cheung E, Ahmad S, Aitken M, Chan R, Iwanochko RM, Balter M, et al. Combined simultaneous FDG-PET/MRI with T1 and T2 map** 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.
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.
**ao 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.
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.
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.
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.
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.
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.
Wang J, Meng Y, Han S, Hu C, Lu Y, Wu P, et al. Predictive value of total ischaemic time and T1 map** 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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
Yuan Y, Sun J, ** 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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
Author information
Authors and Affiliations
Contributions
Conceptualization: Yeon Hyeon Choe and Sung Mok Kim; writing—original draft: Yeon Hyeon Choe; writing—review and editing: Yeon Hyeon Choe and Sung Mok Kim.
Corresponding author
Ethics declarations
Conflict of Interest
Yeon Hyeon Choe and Sung Mok Kim declare that they have no conflict of interest.
Ethics Approval
All content in this editorial was in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards.
Consent to Participate
As an editorial article obtaining informed consent was waived.
Consent for Publication
Not applicable.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Choe, Y.H., Kim, S.M. Recent Progress of Cardiac MRI for Nuclear Medicine Professionals. Nucl Med Mol Imaging (2024). https://doi.org/10.1007/s13139-024-00850-9
Received:
Revised:
Accepted:
Published:
DOI: https://doi.org/10.1007/s13139-024-00850-9