Diagnostic Value of Cardiovascular Magnetic Resonance T1 and T2 Mapping in Acute Myocarditis: A Systematic Literature Review
Abstract
:1. Introduction
2. Materials and Methods
2.1. Data Sources and Search Strategy
2.2. Study Selection
- (1)
- The study must involve adult patients with clinically suspected acute myocarditis that was diagnosed within 14 days from symptom onset;
- (2)
- CMR must have been performed with either 1.5 T or 3 T field strength machines;
- (3)
- Qualitative or quantitative reporting of at least one CMR parameter of interest, namely LGE, T1 mapping time, T2 mapping time, or ECV;
- (4)
- The study should be written in English;
- (5)
- The study needs to be published within the past decade.
2.3. Data Collection
2.4. Study Quality
3. Results
4. Discussion
Limitations
5. Conclusions
Supplementary Materials
Funding
Data Availability Statement
Conflicts of Interest
References
- Sagar, S.; Liu, P.P.; Cooper, L.T. Myocarditis. Lancet 2012, 379, 738–747. [Google Scholar] [CrossRef]
- Ammirati, E.; Veronese, G.; Bottiroli, M.; Wang, D.W.; Cipriani, M.; Garascia, A.; Pedrotti, P.; Adler, E.D.; Frigerio, M. Update on acute myocarditis. Trends Cardiovasc. Med. 2021, 31, 370–379. [Google Scholar] [CrossRef]
- Fung, G.; Luo, H.; Qiu, Y.; Yang, D.; McManus, B. Myocarditis. Circ. Res. 2016, 118, 496–514. [Google Scholar] [CrossRef]
- Leone, O.; Pieroni, M.; Rapezzi, C.; Olivotto, I. The spectrum of myocarditis: From pathology to the clinics. Virchows Arch. 2019, 475, 279–301. [Google Scholar] [CrossRef]
- Caforio, A.L.P.; Pankuweit, S.; Arbustini, E.; Basso, C.; Gimeno-Blanes, J.; Felix, S.B.; Fu, M.; Heliö, T.; Heymans, S.; Jahns, R.; et al. Current state of knowledge on aetiology, diagnosis, management, and therapy of myocarditis: A position statement of the European Society of Cardiology Working Group on Myocardial and Pericardial Diseases. Eur. Heart J. 2013, 34, 2636–2648. [Google Scholar] [CrossRef]
- Dai, H.; Lotan, D.; Abu Much, A.; Younis, A.; Lu, Y.; Bragazzi, N.L.; Wu, J. Global, regional, and national burden of myocarditis and cardiomyopathy, 1990–2017. medRxiv 2020. [Google Scholar] [CrossRef]
- Mistrulli, R.; Ferrera, A.; Muthukkattil, M.L.; Volpe, M.; Barbato, E.; Battistoni, A. SARS-CoV-2 Related Myocarditis: What We Know So Far. J. Clin. Med. 2023, 12, 4700. [Google Scholar] [CrossRef]
- Lewis, A.J.M.; Burrage, M.K.; Ferreira, V.M. Cardiovascular magnetic resonance imaging for inflammatory heart diseases. Cardiovasc. Diagn. Ther. 2020, 10, 598–609. [Google Scholar] [CrossRef]
- Friedrich, M.G.; Sechtem, U.; Schulz-Menger, J.; Holmvang, G.; Alakija, P.; Cooper, L.T.; White, J.A.; Abdel-Aty, H.; Gutberlet, M.; Prasad, S.; et al. Cardiovascular Magnetic Resonance in Myocarditis: A JACC White Paper. J. Am. Coll. Cardiol. 2009, 53, 1475–1487. [Google Scholar] [CrossRef]
- Carrabba, N.; Amico, M.A.; Guaricci, A.I.; Carella, M.C.; Maestrini, V.; Monosilio, S.; Pedrotti, P.; Ricci, F.; Monti, L.; Figliozzi, S.; et al. CMR Mapping: The 4th-Era Revolution in Cardiac Imaging. J. Clin. Med. 2024, 13, 337. [Google Scholar] [CrossRef]
- Ferreira, V.M.; Schulz-Menger, J.; Holmvang, G.; Kramer, C.M.; Carbone, I.; Sechtem, U.; Kindermann, I.; Gutberlet, M.; Cooper, L.T.; Liu, P.; et al. Cardiovascular Magnetic Resonance in Nonischemic Myocardial Inflammation: Expert Recommendations. J. Am. Coll. Cardiol. 2018, 72, 3158–3176. [Google Scholar] [CrossRef]
- Alis, D.; Güler, A.; Aşmakutlu, O.; Uygur, B.; Ördekçi, S. Diagnostic values of edema-sensitive T2-weighted imaging, TSE T1-weighted early contrast-enhanced imaging, late gadolinium enhancement, and the Lake Louise criteria in assessing acute myocarditis: A single-center cardiac magnetic resonance study. Turk Kardiyol. Dern. Ars. 2020, 48, 246–254. [Google Scholar] [CrossRef]
- Baeßler, B.; Treutlein, M.; Schaarschmidt, F.; Stehning, C.; Schnackenburg, B.; Michels, G.; Maintz, D.; Bunck, A.C. A novel multiparametric imaging approach to acute myocarditis using T2-mapping and CMR feature tracking. J. Cardiovasc. Magn. Reson. 2016, 19, 71. [Google Scholar] [CrossRef]
- Ferreira, V.M.; Piechnik, S.K.; Dall’Armellina, E.; Karamitsos, T.D.; Francis, J.M.; Ntusi, N.; Holloway, C.; Choudhury, R.P.; Kardos, A.; Robson, M.D.; et al. Native T1-mapping detects the location, extent and patterns of acute myocarditis without the need for gadolinium contrast agents. J. Cardiovasc. Magn. Reson. 2014, 16, 36. [Google Scholar] [CrossRef]
- Hinojar, R.; Foote, L.; Arroyo Ucar, E.; Jackson, T.; Jabbour, A.; Yu, C.Y.; McCrohon, J.; Higgins, D.M.; Carr-White, G.; Mayr, M.; et al. Native T1 in discrimination of acute and convalescent stages in patients with clinical diagnosis of myocarditis: A proposed diagnostic algorithm using CMR. JACC Cardiovasc. Imaging 2015, 8, 37–46. [Google Scholar] [CrossRef]
- Huber, A.T.; Bravetti, M.; Lamy, J.; Bacoyannis, T.; Roux, C.; de Cesare, A.; Rigolet, A.; Benveniste, O.; Allenbach, Y.; Kerneis, M.; et al. Non-invasive differentiation of idiopathic inflammatory myopathy with cardiac involvement from acute viral myocarditis using cardiovascular magnetic resonance imaging T1 and T2 mapping. J. Cardiovasc. Magn. Reson. 2018, 20, 11. [Google Scholar] [CrossRef]
- Jahnke, C.; Sinn, M.; Hot, A.; Cavus, E.; Erley, J.; Schneider, J.; Chevalier, C.; Bohnen, S.; Radunski, U.; Meyer, M.; et al. Differentiation of acute non-ST elevation myocardial infarction and acute infarct-like myocarditis by visual pattern analysis: A head-to-head comparison of different cardiac MR techniques. Eur. Radiol. 2023, 33, 6258–6266. [Google Scholar] [CrossRef]
- Luetkens, J.A.; Homsi, R.; Sprinkart, A.M.; Doerner, J.; Dabir, D.; Kuetting, D.L.; Block, W.; Andrié, R.; Stehning, C.; Fimmers, R. Incremental value of quantitative CMR including parametric mapping for the diagnosis of acute myocarditis. Eur. Heart J.—Cardiovasc. Imaging 2016, 17, 154–161. [Google Scholar] [CrossRef]
- Radunski, U.K.; Lund, G.K.; Stehning, C.; Schnackenburg, B.; Bohnen, S.; Adam, G.; Blankenberg, S.; Muellerleile, K. CMR in patients with severe myocarditis: Diagnostic value of quantitative tissue markers including extracellular volume imaging. JACC Cardiovasc. Imaging 2014, 7, 667–675. [Google Scholar] [CrossRef]
- Schwab, J.; Rogg, H.-J.; Pauschinger, M.; Fessele, K.; Bareiter, T.; Bär, I.; Loose, R. Functional and Morphological Parameters with Tissue Characterization of Cardiovascular Magnetic Imaging in Clinically Verified “Infarct-like Myocarditis”. Rofo 2015, 188, 365–373. [Google Scholar] [CrossRef]
- Vágó, H.; Szabó, L.; Dohy, Z.; Czimbalmos, C.; Tóth, A.; Suhai, F.I.; Bárczi, G.; Gyarmathy, V.A.; Becker, D.; Merkely, B. Early cardiac magnetic resonance imaging in troponin-positive acute chest pain and non-obstructed coronary arteries. Heart 2020, 106, 992–1000. [Google Scholar] [CrossRef] [PubMed]
- Dabir, D.; Vollbrecht, T.M.; Luetkens, J.A.; Kuetting, D.L.R.; Isaak, A.; Feisst, A.; Fimmers, R.; Sprinkart, A.M.; Schild, H.H.; Thomas, D. Multiparametric cardiovascular magnetic resonance imaging in acute myocarditis: A comparison of different measurement approaches. J. Cardiovasc. Magn. Reson. 2019, 21, 54. [Google Scholar] [CrossRef] [PubMed]
- Baeßler, B.; Schaarschmidt, F.; Dick, A.; Stehning, C.; Schnackenburg, B.; Michels, G.; Maintz, D.; Bunck, A.C. Mapping tissue inhomogeneity in acute myocarditis: A novel analytical approach to quantitative myocardial edema imaging by T2-mapping. J. Cardiovasc. Magn. Reson. 2015, 17, 115. [Google Scholar] [CrossRef] [PubMed]
- Bohnen, S.; Radunski, U.K.; Lund, G.K.; Kandolf, R.; Stehning, C.; Schnackenburg, B.; Adam, G.; Blankenberg, S.; Muellerleile, K. Performance of t1 and t2 mapping cardiovascular magnetic resonance to detect active myocarditis in patients with recent-onset heart failure. Circ. Cardiovasc. Imaging 2015, 8, e003073. [Google Scholar] [CrossRef] [PubMed]
- Tijmes, F.S.; Thavendiranathan, P.; Udell, J.A.; Seidman, M.A.; Hanneman, K. Cardiac MRI Assessment of Nonischemic Myocardial Inflammation: State of the Art Review and Update on Myocarditis Associated with COVID-19 Vaccination. Radiol. Cardiothorac. Imaging 2021, 3, e210252. [Google Scholar] [CrossRef] [PubMed]
- Friedrich, M.G. Tissue characterization of acute myocardial infarction and myocarditis by cardiac magnetic resonance. JACC Cardiovasc. Imaging 2008, 1, 652–662. [Google Scholar] [CrossRef] [PubMed]
- Mavrogeni, S.; Apostolou, D.; Argyriou, P.; Velitsista, S.; Papa, L.; Efentakis, S.; Vernardos, E.; Kanoupaki, M.; Kanoupakis, G.; Manginas, A. T1 and T2 Mapping in Cardiology: “Mapping the Obscure Object of Desire”. Cardiology 2017, 138, 207–217. [Google Scholar] [CrossRef] [PubMed]
- Khanna, S.; Amarasekera, A.T.; Li, C.; Bhat, A.; Chen, H.H.; Gan, G.C.; Ugander, M.; Tan, T.C. The utility of cardiac magnetic resonance imaging in the diagnosis of adult patients with acute myocarditis: A systematic review and meta-analysis. Int. J. Cardiol. 2022, 363, 225–239. [Google Scholar] [CrossRef]
- Jia, Z.; Wang, L.; Jia, Y.; Liu, J.; Zhao, H.; Huo, L.; Zheng, B. Detection of acute myocarditis using T1 and T2 mapping cardiovascular magnetic resonance: A systematic review and meta-analysis. J. Appl. Clin. Med. Phys. 2021, 22, 239–248. [Google Scholar] [CrossRef] [PubMed] [PubMed Central]
- Xu, J.; Xu, Y. Meta-analysis of the Value of Cardiac Nuclear Magnetic Resonance in the Diagnosis of Viral Myocarditis. J. Coll. Physicians Surg. Pak. 2020, 30, 1326–1331. [Google Scholar] [CrossRef] [PubMed]
- Pan, J.A.; Lee, Y.J.; Salerno, M. Diagnostic Performance of Extracellular Volume, Native T1, and T2 Mapping Versus Lake Louise Criteria by Cardiac Magnetic Resonance for Detection of Acute Myocarditis. Circ. Cardiovasc. Imaging 2018, 11, e007598. [Google Scholar] [CrossRef] [PubMed]
- Kotanidis, C.P.; Bazmpani, M.-A.; Haidich, A.-B.; Karvounis, C.; Antoniades, C.; Karamitsos, T.D. Diagnostic Accuracy of Cardiovascular Magnetic Resonance in Acute Myocarditis: A Systematic Review and Meta-Analysis. JACC Cardiovasc. Imaging 2018, 11, 1583–1590. [Google Scholar] [CrossRef] [PubMed]
- Zinkovsky, D.; Sood, M.R.; Zinkovsky, D.; Sood, M.R. The Evaluation of Myocarditis in the Post-Covid-19 Era: Pearls and Perils for the Clinician. In Pericarditis-Diagnosis and Management Challenges; IntechOpen: London, UK, 2023; Available online: https://www.intechopen.com/chapters/87004 (accessed on 29 March 2024).
- Militaru, S.; Mihu, A.; Genunche-Dumitrescu, A.V.; Neagoe, C.D.; Avramescu, T.E.; Istratoaie, O.; Gheonea, I.-A.; Militaru, C. Multimodality Cardiac Imaging in COVID-19 Infection. Medicina 2023, 59, 1223. [Google Scholar] [CrossRef]
Patient Baseline Characteristics (Myocarditis Group/Control Group) | CMR Imaging | |||||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
No. | Author and Year | Country | Type of Study | Sample Size (n) | Age | Male (%) | Patient Groups | Index Measured Using CMR | CMR Parameters Analyzed | Field Strenght | Reference Standart | CMR Performed, Days after Symptoms Onset | LGE (+) n, (%) | T1 Map n, (%) | T2 Map n, (%) | ECV n, (%) |
1 | Alis et al., 2020 [12] | Turkey | Retrospective Case-control | n = 68 | 38.15 ± 10.8/35.12 ± 8.9 | 68/67 | suspected AM (n = 44) control group (n = 24) | LGE, EGEr | Edema, EGE, LGE | 1.5 T | Clinical criteria | 5.67 ± 2.95 | 38 (86.4) | N/A | N/A | N/A |
2 | Baeßler et al., 2015 [23] | Germany | Retrospective Case-control | n = 61 | 40 ± 15/36 ± 13 | 81/47 | suspected AM (n = 31) control group (n = 30) | maxT2, madSD, maxSD | T2, T2 mapping | 1.5 T | Clinical criteria | N/A | N/A | N/A | 31 (100) | N/A |
3 | Baeßler et al., 2017 [13] | Germany | Retrospective Case-control | n = 84 | 37 ± 14/36 ± 12 | 73/65 | suspected AM (n = 67) control group (n = 17) | T2 mapping, LGE | T2 mapping | 1.5 T | Clinical criteria | 4.8 ± 4.4 | 67 (100) | N/A | 67 (100) | N/A |
4 | Bohnen et al., 2015 [24] | Germany | Prospective Cohort | n = 31 | 48.5 ± 14.5/49.5 ± 12.5 | 75/80 | EMB verified AM (n = 16) EMB negative group (n = 15) | T1 mapping, ECV, T2 mapping | T1 mapping, ECV, T2 mapping | 1.5 T | EMB | 30 ± 27 | N/A | 16 (100) | 16 (100) | 16 (100) |
5 | Dabir et al., 2019 [22] | Germany | Prospective Case-control | n = 80 | 38 ± 16.3/36.9 ± 13.5 | 77/74 | suspected AM (n = 50) control group (n = 30) | T1&T2 relaxation time, T2 ratio, EGE ratio, LGE, ECV | T1 mapping, T2 mapping, ECV | 1.5 T | Clinical criteria | 2.9 ± 2.2 | N/A | 50 (100) | 50 (100) | 50 (100) |
6 | Ferreira et al., 2014 [14] | United Kingdom | Prospective Case-control | n = 110 | 41 ± 16/41 ± 13 | 75/74 | suspected AM (n = 60) control group (n = 50) | T1 mapping, dark-blood T2, LGE | T1 mapping, dark-blood T2, LGE | 1.5 T | Clinical criteria | 3.5 ± 2.5 | 60 (100) | 60 (100) | N/A | N/A |
7 | Hinojar et al., 2015 [15] | United Kingdom | Prospective Case-control | n = 101 | 48 ± 17/45 ± 15 | 52/53 | suspected AM (n = 61) control group (n = 40) | native T1, post-contrast T1, LGE, T2 signal | native T1, post-contrast T1, LGE, T2 signal | 1.5 T, 3 T | Clinical criteria | 5 ± 7 | N/A | 61 (100) | N/A | 61 (100) |
8 | Huber et al., 2018 [16] | France | Retrospective Case-control | n = 60 | 35 ± 13/47 ± 12 | 80/82 | AM (n = 20) IIM (n = 20) control group (n = 20) | native T1,post-contrast T1, T2, ECV, LGE | native T1,post-contrast T1, T2, ECV, LGE | 1.5 T | Clinical criteria | 5.18 ± 3.96 | 20 (100) | 20 (100) | 20 (100) | 20 (100) |
9 | Jahnke et al., 2023 [17] | Germany | Retrospective Case-control | n = 60 | 37.5 ± 6.5/40.5 ± 5.5 | 85/85 | NSTEMI (n = 20) infarct-like AM (n = 20) control group (n = 20) | cine, T2w, LGE, T1 maps, T2 maps | T2w, LGE, T1 maps, T2 maps | 1.5 T | Clinical criteria | 14.5 ± 12.5 | 20 (100) | 20 (100) | 20 (100) | N/A |
10 | Luetkens et al., 2016 [18] | Germany | Prospective case-control | n = 84 | 44.9 ± 18.7/39.2 ± 17.2 | 50/60 | suspected AM (n = 34) control group (n = 50) | T1, T2 relaxation times, ECV, T2- ratio, LGE, EGE | T1, T2 relaxation times, ECV, T2- ratio, LGE, EGEr | 1.5 T | Clinical criteria | 2.63 ± 1.93 | 34 (100) | 34 (100) | 34 (100) | 34 (100) |
11 | Radunski et al., 2014 [19] | Germany | Retrospective case-control | n = 125 | 45.5 ± 12.5/37.5 ± 9.5 | 76/81 | suspected AM (n = 104) control group (n = 21) | T2w, EGE, LGE, T2 mapping, native T1, EVC | T1, T2, ECV | 1.5 T | Clinical criteria | 28 ± 21 | 104 (100) | 104 (100) | 104 (100) | 104 (100) |
12 | Schwab et al., 2016 [20] | Germany | Retrospective Case- control | n = 78 | 34.7 ± 15.2/35.4 ± 13.8 | 88/89 | clinically verified AM (n = 43) control group (n = 35) | T2w, LGE, EGE | T2w, LGE, EGE | 1.5 T | Clinical criteria | 3 (1–17) | 43 (100) | N/A | N/A | N/A |
13 | Vágó et al., 2020 [21] | Hungary | Retrospective Cohort | N = 250 | 34 ± 10/49 ±14 | 88/51 | AM (n = 136) MI (n = 55) Takotsubo syndrome (n = 26) control group (n = 20) | LGE, T2 signal | LGE, T2 signal | 1.5 T | Clinical criteria | 2.7 | 136 (100) | N/A | N/A | N/A |
Sample Size (n) | Sensitivity (%) | Specificity (%) | NPV (%) | PPV (%) | Diagnostic Accuracy (%) | Cut-Off Value (ms) | |
---|---|---|---|---|---|---|---|
Dabir et al., 2019 [22] | 80 | 85 | 90 | 79 | 93 | 87 | >980 |
Ferreira et al., 2014 [14] | 110 | 90 | 88 | 88 | 90 | 89 | >990 |
Hinojar et al., 2015 [15] | 101 | 98 | 100 | 99 | 100 | 99 | >992 |
Jahnke et al., 2023 [17] | 40 | 85 | 87 | 85 | 85 | 80 | N/A |
Luetkens et al., 2016 [18] | 84 | 85 | 96 | 90 | 94 | 92 | >1000 |
Radunski et al., 2014 [19] | 125 | 64 | 90 | 34 | 97 | 69 | >1074 |
Sample Size (n) | Sensitivity (%) | Specificity (%) | NPV (%) | PPV (%) | |
---|---|---|---|---|---|
Baeßler et al., 2015 [23] | 61 | 67 | 87 | N/A | N/A |
Baeßler et al., 2017 [13] | 84 | 69 | 82 | 40 | 94 |
Bohnen et al., 2015 [24] | 31 | 94 | 60 | 90 | 71 |
Dabir et al., 2019 [22] | 80 | 80 | 87 | 74 | 90 |
Jahnke et al., 2023 [17] | 40 | 48 | 63 | 55 | 56 |
Luetkens et al., 2016 [18] | 84 | 79 | 92 | 87 | 87 |
Radunski et al., 2014 [19] | 125 | 57 | 89 | 35 | 95 |
LGE | Sample Size (n) | Sensitivity (%) | Specificity (%) | NPV (%) | PPV (%) | Diagnostic Accuracy (%) |
---|---|---|---|---|---|---|
Alis et al., 2020 [12] | 68 | 86 | 92 | 80 | 95 | 88.5 |
Baeßler et al., 2017 [13] | 84 | 52 | 100 | 35 | 100 | 62 |
Ferreira et al., 2014 [14] | 110 | 72 | 97 | 67 | 98 | 81 |
Hinojar et al., 2015 [15] | 101 | 72 | 100 | 79 | 100 | 86 |
Jahnke et al., 2023 [17] | 40 | 92 | 77 | 88 | 78 | 74 |
Luetkens et al., 2016 [18] | 84 | 74 | 100 | 85 | 100 | 89 |
Radunski et al., 2014 [19] | 125 | 61 | 100 | 34 | 100 | 67 |
Schwab et al., 2016 [20] | 78 | 86 | 100 | 85 | 100 | 92 |
ECV | Sample Size (n) | Sensitivity (%) | Specificity (%) | NPV (%) | PPV (%) | Diagnostic Accuracy (%) | Cut-Off Value (%) |
---|---|---|---|---|---|---|---|
Dabir et al., 2019 [22] | 80 | 47 | 88 | 49 | 87 | 62 | >31 |
Luetkens et al., 2016 [18] | 84 | 70 | 76 | 79 | 67 | 74 | >28.8 |
Radunski et al., 2014 [19] | 125 | 73 | 90 | 40 | 97 | 76 | ≥29 |
No. | Author and Year | Key Points |
---|---|---|
1 | Alis et al., 2020 [12] | LGE and/or edema as a sole criterion for the diagnosis of acute myocarditis demonstrated better diagnostic accuracy than the LLC |
2 | Baeßler et al., 2015 [23] | The proposed cut-off values for maxT2 and madSD in the setting of acute myocarditis allow edema detection with high sensitivity and specificity and, therefore, have the potential to overcome the hurdles of T2 mapping for its integration into clinical routine |
3 | Baeßler et al., 2017 [13] | A multiparametric CMR imaging model, including the novel T2-mapping-derived parameter madSD, the feature-tracking derived strain parameter, and LGE, yields superior diagnostic sensitivity in suspected acute myocarditis when compared to any imaging parameter alone and to the LLC |
4 | Bohnen et al., 2015 [24] | T2 mapping seems to be superior when compared with standard CMR parameters, global myocardial T1, and ECV values for assessing the activity of myocarditis in patients with recent-onset heart failure and reduced left ventricular function |
5 | Dabir et al., 2019 [22] | Native T1 and T2 mapping allow for accurate detection of acute myocarditis irrespective of the measurement approach used |
6 | Ferreira et al., 2014 [14] | Native T1 mapping can display the typical non-ischemic patterns in acute myocarditis, like LGE imaging, but without the need for contrast agents |
7 | Hinojar et al., 2015 [15] | The new diagnostic algorithm using native T1 can reliably discriminate between health and disease and determine the clinical disease stage in patients with a clinical diagnosis of myocarditis |
8 | Huber et al., 2018 [16] | CMR myocardial mapping detects cardiac inflammation in acute viral myocarditis compared to normal myocardium in healthy controls |
9 | Jahnke et al., 2023 [17] | The conventional approach provided reliable visual discrimination between NSTEMI, myocarditis, and controls |
10 | Luetkens et al., 2016 [18] | Myocardial T1 and T2 relaxation times were the only parameters of active inflammation/edema that could discriminate between myocarditis patients and control subjects, even at a convalescent stage of the disease |
11 | Radunski et al., 2014 [19] | In patients with clinical evidence for subacute, severe myocarditis, ECV quantification with LGE imaging significantly improved the diagnostic accuracy of CMR compared with standard LLC |
12 | Schwab et al., 2016 [20] | Functional and morphological CMR parameters, in addition to tissue characterization, are useful tools in the diagnosis of acute myocarditis |
13 | Vágó et al., 2020 [21] | CMR performed in the early phase establishes the proper diagnosis in patients with troponin-positive acute chest pain and non-obstructed coronary arteries and provides additional prognostic factors |
Parameter | Characteristics | Advantages | Limitations | Diagnostic Accuracy % |
---|---|---|---|---|
T1 mapping | Detection of myocardial edema, inflammation, and diffuse fibrosis | - Quantitative tissue characterization - Non-contrast evaluation - Detection of diffuse fibrosis - Identifies subtle changes in myocardial tissue | - Vendor-specific sequence - No standardized protocols - Breath-holding requirements - Heart rate dependence - Sensitive to motion artifacts | 69–99 |
T2 mapping | - Quantitative evaluation of tissue water content - Diagnosis of myocardial inflammation | - Quantitative tissue characterization - No contrast required | - Vendor-specific sequence - No standardized protocols - Sensitive to motion and susceptibility artifacts - Breath-holding requirements - Heart rate dependence | 47–87 |
LGE | - Detects areas of myocyte necrosis and hyperemia - Delayed imaging: images are taken 10–20 min after gadolinium contrast administration - Areas of fibrosis appear hyperintense compared to normal myocardium | - Distinguishing between ischemic and non-ischemic etiology of heart diseases - Accurately identifies areas of focal fibrosis | - Qualitative or semiquantitative data - Requires contrast agent - Incomplete myocardium nulling - Insensitive to detecting diffuse interstitial fibrosis - Sensitive to motion artifacts - Does not differentiate well between acute and chronic myocardial injury - Contraindicated in patients with severe renal dysfunction - Limited spatial resolution | 62–92 |
ECV | - Calculated using native and post-contrast T1 mapping - Used to assess the cellular and extracellular interstitial matrix compartments, represented as volume proportions | - Quantitative measurement | - Requires contrast agent - Technical variability - No standardized protocols - Influence of hematocrit - Sensitive to motion artifacts | 62–76 |
Combination of the parameters | T1 mapping, T2 mapping, LGE | - Detailed evaluation of myocardial tissue - Enhanced diagnostic accuracy | - Requires advanced imaging protocols and expertise - Increased scan time - Motion artifacts - Standardization variability - Requires contrast agent | 87–96 |
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |
© 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Gaizauskiene, K.; Leketaite, K.; Glaveckaite, S.; Valeviciene, N. Diagnostic Value of Cardiovascular Magnetic Resonance T1 and T2 Mapping in Acute Myocarditis: A Systematic Literature Review. Medicina 2024, 60, 1162. https://doi.org/10.3390/medicina60071162
Gaizauskiene K, Leketaite K, Glaveckaite S, Valeviciene N. Diagnostic Value of Cardiovascular Magnetic Resonance T1 and T2 Mapping in Acute Myocarditis: A Systematic Literature Review. Medicina. 2024; 60(7):1162. https://doi.org/10.3390/medicina60071162
Chicago/Turabian StyleGaizauskiene, Karolina, Kamile Leketaite, Sigita Glaveckaite, and Nomeda Valeviciene. 2024. "Diagnostic Value of Cardiovascular Magnetic Resonance T1 and T2 Mapping in Acute Myocarditis: A Systematic Literature Review" Medicina 60, no. 7: 1162. https://doi.org/10.3390/medicina60071162