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The International Journal of Cardiovascular Imaging

Open Access 06-09-2024 | Computed Tomography | Review Paper

Advanced myocardial characterization and function with cardiac CT

Authors: Costanza Lisi, Lukas J. Moser, Victor Mergen, Konstantin Klambauer, Eda Uçar, Matthias Eberhard, Hatem Alkadhi

Published in: The International Journal of Cardiovascular Imaging

Abstract

Non-invasive imaging with characterization and quantification of the myocardium with computed tomography (CT) became feasible owing to recent technical developments in CT technology. Cardiac CT can serve as an alternative modality when cardiac magnetic resonance imaging and/or echocardiography are contraindicated, not feasible, inconclusive, or non-diagnostic. This review summarizes the current and potential future role of cardiac CT for myocardial characterization including a summary of late enhancement techniques, extracellular volume quantification, and strain analysis. In addition, this review highlights potential fields for research about myocardial characterization with CT to possibly include it in clinical routine in the future.

Graphical Abstract

Cardiac computed tomography as an increasingly important alternative for myocardial tissue characterization and functional assessment.

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de Carvalho FP, Azevedo CF (2020) Comprehensive assessment of endomyocardial fibrosis with cardiac MRI: morphology, function, and tissue characterization. Radiographics 40:336–353. https://doi.org/10.1148/rg.2020190148CrossRefPubMed

Mahrholdt H, Wagner A, Judd RM et al (2005) Delayed enhancement cardiovascular magnetic resonance assessment of non-ischaemic cardiomyopathies. Eur Heart J 26:1461–1474. https://doi.org/10.1093/eurheartj/ehi258CrossRefPubMed

Arbelo E, Protonotarios A, Gimeno JR et al (2023) 2023 ESC Guidelines for the management of cardiomyopathies. Eur Heart J 44:3503–3626. https://doi.org/10.1093/eurheartj/ehad194CrossRefPubMed

Tamarappoo B, Han D, Tyler J et al (2020) Prognostic Value of computed tomography-derived extracellular volume in TAVR patients with low-flow low-gradient aortic stenosis. JACC Cardiovasc Imaging 13:2591–2601. https://doi.org/10.1016/j.jcmg.2020.07.045CrossRefPubMedPubMedCentral

Amzulescu MS, De Craene M, Langet H et al (2019) Myocardial strain imaging: review of general principles, validation, and sources of discrepancies. Eur Heart J Cardiovasc Imaging 20:605–619. https://doi.org/10.1093/ehjci/jez041CrossRefPubMedPubMedCentral

Fischer K, Obrist SJ, Erne SA et al (2020) Feature tracking myocardial strain incrementally improves prognostication in myocarditis beyond traditional CMR imaging features. JACC Cardiovasc Imaging 13:1891–1901. https://doi.org/10.1016/j.jcmg.2020.04.025CrossRefPubMed

Bernhard B, Grogg H, Zurkirchen J et al (2022) Reproducibility of 4D cardiac computed tomography feature tracking myocardial strain and comparison against speckle-tracking echocardiography in patients with severe aortic stenosis. J Cardiovasc Comput Tomogr 16:309–318. https://doi.org/10.1016/j.jcct.2022.01.003CrossRefPubMed

Park JJ, Park J-B, Park J-H, Cho G-Y (2018) Global longitudinal strain to predict mortality in patients with acute heart failure. J Am Coll Cardiol 71:1947–1957. https://doi.org/10.1016/j.jacc.2018.02.064CrossRefPubMed

Kalam K, Otahal P, Marwick TH (2014) Prognostic implications of global LV dysfunction: a systematic review and meta-analysis of global longitudinal strain and ejection fraction. Heart 100:1673–1680. https://doi.org/10.1136/heartjnl-2014-305538CrossRefPubMed

10.

Lisi C, Catapano F, Rondi P et al (2023) Multimodality imaging in cardio-oncology: the added value of CMR and CCTA. Br J Radiol 96:20220999. https://doi.org/10.1259/bjr.20220999CrossRefPubMedPubMedCentral

11.

Cundari G, Marchitelli L, Pambianchi G et al (2024) Imaging biomarkers in cardiac CT: moving beyond simple coronary anatomical assessment. Radiol Med 129:380–400. https://doi.org/10.1007/s11547-024-01771-5CrossRefPubMedPubMedCentral

12.

Bernhard B, Leib Z, Dobner S et al (2023) Routine 4D cardiac CT to identify concomitant transthyretin amyloid cardiomyopathy in older adults with severe aortic stenosis. Radiology 309:e230425. https://doi.org/10.1148/radiol.230425CrossRefPubMed

13.

Andreini D, Conte E, Mushtaq S et al (2023) Comprehensive evaluation of left ventricle dysfunction by a new computed tomography scanner: the E-PLURIBUS study. JACC Cardiovasc Imaging 16:175–188. https://doi.org/10.1016/j.jcmg.2022.08.005CrossRefPubMed

14.

Han D, Lin A, Kuronuma K et al (2023) Cardiac computed tomography for quantification of myocardial extracellular volume fraction: a systematic review and meta-analysis. JACC Cardiovasc Imaging 16:1306–1317. https://doi.org/10.1016/j.jcmg.2023.03.021CrossRefPubMed

15.

Rajiah PS, Alkadhi H, Van Mieghem NM, Budde RPJ (2024) Utility of photon counting CT in transcatheter structural heart disease interventions. Semin Roentgenol 59:32–43. https://doi.org/10.1053/j.ro.2023.11.005CrossRefPubMed

16.

Mergen V, Ghouse S, Sartoretti T et al (2023) Cardiac virtual noncontrast images for calcium quantification with photon-counting detector CT. Radiol Cardiothorac Imaging 5:e220307. https://doi.org/10.1148/ryct.220307CrossRefPubMedPubMedCentral

17.

Corrado D, Perazzolo Marra M, Zorzi A et al (2020) Diagnosis of arrhythmogenic cardiomyopathy: the Padua criteria. Int J Cardiol 319:106–114. https://doi.org/10.1016/j.ijcard.2020.06.005CrossRefPubMed

18.

Members WC, Ommen SR, Mital S et al (2021) 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. J Thorac Cardiovasc Surg 162:e23–e106. https://doi.org/10.1016/j.jtcvs.2021.04.001CrossRef

19.

Authors/Task Force members, Elliott PM, Anastasakis A, et al (2014) 2014 ESC Guidelines on diagnosis and management of hypertrophic cardiomyopathy: the Task Force for the Diagnosis and Management of Hypertrophic Cardiomyopathy of the European Society of Cardiology (ESC). Eur Heart J 35:2733–2779. https://doi.org/10.1093/eurheartj/ehu284CrossRef

20.

Mahrholdt H, Wagner A, Judd RM, Sechtem U (2002) Assessment of myocardial viability by cardiovascular magnetic resonance imaging. Eur Heart J 23:602–619. https://doi.org/10.1053/euhj.2001.3038CrossRefPubMed

21.

Halliday BP, Baksi AJ, Gulati A et al (2019) Outcome in dilated cardiomyopathy related to the extent, location, and pattern of late gadolinium enhancement. JACC Cardiovasc Imaging 12:1645–1655. https://doi.org/10.1016/j.jcmg.2018.07.015CrossRefPubMedPubMedCentral

22.

Axelsson Raja A, Farhad H, Valente AM et al (2018) Prevalence and progression of late gadolinium enhancement in children and adolescents with hypertrophic cardiomyopathy. Circulation 138:782–792. https://doi.org/10.1161/CIRCULATIONAHA.117.032966CrossRefPubMed

23.

Fontana M, Pica S, Reant P et al (2015) Prognostic value of late gadolinium enhancement cardiovascular magnetic resonance in cardiac amyloidosis. Circulation 132:1570–1579. https://doi.org/10.1161/CIRCULATIONAHA.115.016567CrossRefPubMedPubMedCentral

24.

Kwong RY, Chan AK, Brown KA et al (2006) Impact of unrecognized myocardial scar detected by cardiac magnetic resonance imaging on event-free survival in patients presenting with signs or symptoms of coronary artery disease. Circulation 113:2733–2743. https://doi.org/10.1161/CIRCULATIONAHA.105.570648CrossRefPubMed

25.

Allard M, Doucet D, Kien P et al (1988) Experimental study of DOTA-gadolinium. Pharmacokinetics and pharmacologic properties. Invest Radiol 23(Suppl 1):S271–S274. https://doi.org/10.1097/00004424-198809001-00059CrossRefPubMed

26.

Gerber BL, Belge B, Legros GJ et al (2006) Characterization of acute and chronic myocardial infarcts by multidetector computed tomography: comparison with contrast-enhanced magnetic resonance. Circulation 113:823–833. https://doi.org/10.1161/CIRCULATIONAHA.104.529511CrossRefPubMed

27.

Lardo AC, Cordeiro MAS, Silva C et al (2006) Contrast-enhanced multidetector computed tomography viability imaging after myocardial infarction: characterization of myocyte death, microvascular obstruction, and chronic scar. Circulation 113:394–404. https://doi.org/10.1161/CIRCULATIONAHA.105.521450CrossRefPubMedPubMedCentral

28.

Mahnken AH, Koos R, Katoh M et al (2005) Assessment of myocardial viability in reperfused acute myocardial infarction using 16-slice computed tomography in comparison to magnetic resonance imaging. J Am Coll Cardiol 45:2042–2047. https://doi.org/10.1016/j.jacc.2005.03.035CrossRefPubMed

29.

Nacif MS, Kawel N, Lee JJ et al (2012) Interstitial myocardial fibrosis assessed as extracellular volume fraction with low-radiation-dose cardiac CT. Radiology 264:876–883. https://doi.org/10.1148/radiol.12112458CrossRefPubMedPubMedCentral

30.

Bandula S, White SK, Flett AS et al (2013) Measurement of myocardial extracellular volume fraction by using equilibrium contrast-enhanced CT: validation against histologic findings. Radiology 269:396–403. https://doi.org/10.1148/radiology.13130130CrossRefPubMed

31.

Rajiah PS, Kambadakone A, Ananthakrishnan L et al (2023) Vascular applications of dual-energy computed tomography. Radiol Clin North Am 61:1011–1029. https://doi.org/10.1016/j.rcl.2023.05.005CrossRefPubMed

32.

Euler A, Higashigaito K, Mergen V et al (2022) High-pitch photon-counting detector computed tomography angiography of the aorta: intraindividual comparison to energy-integrating detector computed tomography at equal radiation dose. Invest Radiol 57:115–121. https://doi.org/10.1097/RLI.0000000000000816CrossRefPubMed

33.

Ohta Y, Kitao S, Yunaga H et al (2018) Myocardial delayed enhancement CT for the evaluation of heart failure: comparison to MRI. Radiology 288:682–691. https://doi.org/10.1148/radiol.2018172523CrossRefPubMed

34.

Oda S, Emoto T, Nakaura T et al (2019) Myocardial late iodine enhancement and extracellular volume quantification with dual-layer spectral detector dual-energy cardiac CT. Radiol Cardiothorac Imaging 1:e180003. https://doi.org/10.1148/ryct.2019180003CrossRefPubMedPubMedCentral

35.

Chang S, Han K, Youn J-C et al (2018) Utility of dual-energy CT-based monochromatic imaging in the assessment of myocardial delayed enhancement in patients with cardiomyopathy. Radiology 287:442–451. https://doi.org/10.1148/radiol.2017162945CrossRefPubMed

36.

Hammer Y, Talmor-Barkan Y, Abelow A et al (2021) Myocardial extracellular volume quantification by computed tomography predicts outcomes in patients with severe aortic stenosis. PLoS ONE 16:e0248306. https://doi.org/10.1371/journal.pone.0248306CrossRefPubMedPubMedCentral

37.

Scully PR, Patel KP, Saberwal B et al (2020) Identifying cardiac amyloid in aortic stenosis: ECV quantification by CT in TAVR patients. JACC Cardiovasc Imaging 13:2177–2189. https://doi.org/10.1016/j.jcmg.2020.05.029CrossRefPubMedPubMedCentral

38.

Qi R-X, Jiang J-S, Shao J et al (2022) Measurement of myocardial extracellular volume fraction in patients with heart failure with preserved ejection fraction using dual-energy computed tomography. Eur Radiol 32:4253–4263. https://doi.org/10.1007/s00330-021-08514-4CrossRefPubMed

39.

Cundari G, Galea N, Mergen V et al (2023) Myocardial extracellular volume quantification with computed tomography-current status and future outlook. Insights Imaging 14:156. https://doi.org/10.1186/s13244-023-01506-6CrossRefPubMedPubMedCentral

40.

Hamdy A, Kitagawa K, Goto Y et al (2019) Comparison of the different imaging time points in delayed phase cardiac CT for myocardial scar assessment and extracellular volume fraction estimation in patients with old myocardial infarction. Int J Cardiovasc Imaging 35:917–926. https://doi.org/10.1007/s10554-018-1513-zCrossRefPubMed

41.

Brodoefel H, Klumpp B, Reimann A et al (2007) Late myocardial enhancement assessed by 64-MSCT in reperfused porcine myocardial infarction: diagnostic accuracy of low-dose CT protocols in comparison with magnetic resonance imaging. Eur Radiol 17:475–483. https://doi.org/10.1007/s00330-006-0334-yCrossRefPubMed

42.

Jacquier A, Boussel L, Amabile N et al (2008) Multidetector computed tomography in reperfused acute myocardial infarction. Assessment of infarct size and no-reflow in comparison with cardiac magnetic resonance imaging. Invest Radiol 43:773–781. https://doi.org/10.1097/RLI.0b013e318181c8ddCrossRefPubMed

43.

Nishii T, Kobayashi T, Tanaka H et al (2022) Deep learning-based post hoc CT denoising for myocardial delayed enhancement. Radiology 305:82–91. https://doi.org/10.1148/radiol.220189CrossRefPubMed

44.

Cummings KW, Bhalla S, Javidan-Nejad C et al (2009) A pattern-based approach to assessment of delayed enhancement in nonischemic cardiomyopathy at MR imaging. Radiographics 29:89–103. https://doi.org/10.1148/rg.291085052CrossRefPubMed

45.

Palmisano A, Vignale D, Tadic M et al (2022) Myocardial late contrast enhancement CT in troponin-positive acute chest pain syndrome. Radiology 302:545–553. https://doi.org/10.1148/radiol.211288CrossRefPubMed

46.

Wildberger JE, Alkadhi H (2023) New horizons in vascular imaging with photon-counting detector CT. Invest Radiol 58:499–504. https://doi.org/10.1097/RLI.0000000000000957CrossRefPubMed

47.

Jost G, McDermott M, Gutjahr R et al (2023) New contrast media for K-edge imaging with photon-counting detector CT. Invest Radiol 58:515–522. https://doi.org/10.1097/RLI.0000000000000978CrossRefPubMedPubMedCentral

48.

Scully PR, Bastarrika G, Moon JC, Treibel TA (2018) Myocardial extracellular volume quantification by cardiovascular magnetic resonance and computed tomography. Curr Cardiol Rep 20:15. https://doi.org/10.1007/s11886-018-0961-3CrossRefPubMedPubMedCentral

49.

Treibel TA, Bandula S, Fontana M et al (2015) Extracellular volume quantification by dynamic equilibrium cardiac computed tomography in cardiac amyloidosis. J Cardiovasc Comput Tomogr 9:585–592. https://doi.org/10.1016/j.jcct.2015.07.001CrossRefPubMedPubMedCentral

50.

Emoto T, Oda S, Kidoh M et al (2021) Myocardial extracellular volume quantification using cardiac computed tomography: a comparison of the dual-energy iodine method and the standard subtraction method. Acad Radiol 28:e119–e126. https://doi.org/10.1016/j.acra.2020.03.019CrossRefPubMed

51.

Kim NY, Im DJ, Youn J-C et al (2022) Synthetic extracellular volume fraction derived using virtual unenhanced attenuation of blood on contrast-enhanced cardiac dual-energy CT in nonischemic cardiomyopathy. AJR Am J Roentgenol 218:454–461. https://doi.org/10.2214/AJR.21.26654CrossRefPubMed

52.

Mergen V, Sartoretti T, Klotz E et al (2022) Extracellular volume quantification with cardiac late enhancement scanning using dual-source photon-counting detector CT. Invest Radiol 57:406–411. https://doi.org/10.1097/RLI.0000000000000851CrossRefPubMedPubMedCentral

53.

Dubourg B, Dacher J-N, Durand E et al (2021) Single-source dual energy CT to assess myocardial extracellular volume fraction in aortic stenosis before transcatheter aortic valve implantation (TAVI). Diagn Interv Imaging 102:561–570. https://doi.org/10.1016/j.diii.2021.03.003CrossRefPubMed

54.

Lee H-J, Im DJ, Youn J-C et al (2016) Myocardial extracellular volume fraction with dual-energy equilibrium contrast-enhanced cardiac CT in nonischemic cardiomyopathy: a prospective comparison with cardiac MR imaging. Radiology 280:49–57. https://doi.org/10.1148/radiol.2016151289CrossRefPubMed

55.

Suzuki M, Toba T, Izawa Y et al (2021) Prognostic impact of myocardial extracellular volume fraction assessment using dual-energy computed tomography in patients treated with aortic valve replacement for severe aortic stenosis. J Am Heart Assoc 10:e020655. https://doi.org/10.1161/JAHA.120.020655CrossRefPubMedPubMedCentral

56.

Hayashi H, Oda S, Emoto T et al (2022) Myocardial extracellular volume quantification by cardiac CT in pulmonary hypertension: comparison with cardiac MRI. Eur J Radiol 153:110386. https://doi.org/10.1016/j.ejrad.2022.110386CrossRefPubMed

57.

Ohta Y, Kishimoto J, Kitao S et al (2020) Investigation of myocardial extracellular volume fraction in heart failure patients using iodine map with rapid-kV switching dual-energy CT: Segmental comparison with MRI T1 mapping. J Cardiovasc Comput Tomogr 14:349–355. https://doi.org/10.1016/j.jcct.2019.12.032CrossRefPubMed

58.

Baggiano A, Conte E, Spiritigliozzi L et al (2023) Quantification of extracellular volume with cardiac computed tomography in patients with dilated cardiomyopathy. J Cardiovasc Comput Tomogr 17:261–268. https://doi.org/10.1016/j.jcct.2023.04.005CrossRefPubMed

59.

Ishiyama M, Kurita T, Takafuji M et al (2023) The cardiac computed tomography-derived extracellular volume fraction predicts patient outcomes and left ventricular mass reductions after transcatheter aortic valve implantation for aortic stenosis. J Cardiol 81:476–484. https://doi.org/10.1016/j.jjcc.2022.12.002CrossRefPubMed

60.

Treibel TA, Patel KP, Cavalcante JL (2020) Extracellular volume imaging in aortic stenosis during routine pre-TAVR cardiac computed tomography. JACC Cardiovasc Imaging 13:2602–2604. https://doi.org/10.1016/j.jcmg.2020.08.023CrossRefPubMed

61.

Koike H, Fukui M, Treibel T et al (2024) Comprehensive myocardial assessment by computed tomography: impact on short-term outcomes after transcatheter aortic valve replacement. JACC Cardiovasc Imaging 17:396–407. https://doi.org/10.1016/j.jcmg.2023.08.008CrossRefPubMed

62.

Gama F, Rosmini S, Bandula S et al (2022) Extracellular volume fraction by computed tomography predicts long-term prognosis among patients with cardiac amyloidosis. JACC Cardiovasc Imaging 15:2082–2094. https://doi.org/10.1016/j.jcmg.2022.08.006CrossRefPubMed

63.

Scully PR, Patel KP, Klotz E et al (2022) Myocardial fibrosis quantified by cardiac CT predicts outcome in severe aortic stenosis after transcatheter intervention. JACC Cardiovasc Imaging 15:542–544. https://doi.org/10.1016/j.jcmg.2021.10.016CrossRefPubMedPubMedCentral

64.

Vignale D, Palmisano A, Gnasso C et al (2023) Extracellular volume fraction (ECV) derived from pre-operative computed tomography predicts prognosis in patients undergoing transcatheter aortic valve implantation (TAVI). Eur Heart J Cardiovasc Imaging 24:887–896. https://doi.org/10.1093/ehjci/jead040CrossRefPubMedPubMedCentral

65.

Yashima S, Takaoka H, Iwahana T et al (2023) Evaluation of extracellular volume by computed tomography is useful for prediction of prognosis in dilated cardiomyopathy. Heart Vessels 38:185–194. https://doi.org/10.1007/s00380-022-02154-4CrossRefPubMed

66.

Schöndube H, Allmendinger T, Stierstorfer K et al (2013) Temporal resolution and motion artifacts in single-source and dual-source cardiac CT. Med Phys 40:031112. https://doi.org/10.1118/1.4790695CrossRefPubMed

67.

Aquino GJ, O’Doherty J, Schoepf UJ et al (2023) Myocardial characterization with extracellular volume mapping with a first-generation photon-counting detector CT with MRI reference. Radiology 307:e222030. https://doi.org/10.1148/radiol.2220CrossRefPubMed

68.

Decker JA, Huber A, Senel F et al (2022) Anemia detection by hemoglobin quantification on contrast-enhanced photon-counting CT data sets. Radiology 305:650–652. https://doi.org/10.1148/radiol.220063CrossRefPubMed

69.

Decker JA, Huber A, Senel F et al (2023) Anemia detection and quantification in contrast-enhanced CT scans on a novel photon-counting detector CT: a prospective proof-of-concept study. Eur J Radiol 166:110967. https://doi.org/10.1016/j.ejrad.2023.110967CrossRefPubMed

70.

Mergen V, Ehrbar N, Moser LJ et al (2024) Synthetic hematocrit from virtual non-contrast images for myocardial extracellular volume evaluation with photon-counting detector CT. Eur Radiol. https://doi.org/10.1007/s00330-024-10865-7CrossRefPubMed

71.

Chen W, Doeblin P, Al-Tabatabaee S et al (2022) Synthetic extracellular volume in cardiac magnetic resonance without blood sampling: a reliable tool to replace conventional extracellular volume. Circ Cardiovasc Imaging 15:e013745. https://doi.org/10.1161/CIRCIMAGING.121.013745CrossRefPubMedPubMedCentral

72.

Cheng S, Larson MG, McCabe EL et al (2013) Age- and sex-based reference limits and clinical correlates of myocardial strain and synchrony: the Framingham Heart Study. Circ Cardiovasc Imaging 6:692–699. https://doi.org/10.1161/CIRCIMAGING.112.000627CrossRefPubMed

73.

Streeter DD, Spotnitz HM, Patel DP et al (1969) Fiber orientation in the canine left ventricle during diastole and systole. Circ Res 24:339–347. https://doi.org/10.1161/01.res.24.3.339CrossRefPubMed

74.

Simpson RM, Keegan J, Firmin DN (2013) MR assessment of regional myocardial mechanics. J Magn Reson Imaging 37:576–599. https://doi.org/10.1002/jmri.23756CrossRefPubMed

75.

Collier P, Phelan D, Klein A (2017) A test in context: myocardial strain measured by speckle-tracking echocardiography. J Am Coll Cardiol 69:1043–1056. https://doi.org/10.1016/j.jacc.2016.12.012CrossRefPubMed

76.

Hor KN, Baumann R, Pedrizzetti G et al (2011) Magnetic resonance derived myocardial strain assessment using feature tracking. J Vis Exp. https://doi.org/10.3791/2356CrossRefPubMedPubMedCentral

77.

Kukulski T, Jamal F, Herbots L et al (2003) Identification of acutely ischemic myocardium using ultrasonic strain measurements. A clinical study in patients undergoing coronary angioplasty. J Am Coll Cardiol 41:810–819. https://doi.org/10.1016/s0735-1097(02)02934-0CrossRefPubMed

78.

Biering-Sørensen T, Hoffmann S, Mogelvang R et al (2014) Myocardial strain analysis by 2-dimensional speckle tracking echocardiography improves diagnostics of coronary artery stenosis in stable angina pectoris. Circ Cardiovasc Imaging 7:58–65. https://doi.org/10.1161/CIRCIMAGING.113.000989CrossRefPubMed

79.

Eek C, Grenne B, Brunvand H et al (2010) Strain echocardiography and wall motion score index predicts final infarct size in patients with non-ST-segment-elevation myocardial infarction. Circ Cardiovasc Imaging 3:187–194. https://doi.org/10.1161/CIRCIMAGING.109.910521CrossRefPubMed

80.

Marcus FI, McKenna WJ, Sherrill D et al (2010) Diagnosis of arrhythmogenic right ventricular cardiomyopathy/dysplasia: proposed modification of the task force criteria. Eur Heart J 31:806–814. https://doi.org/10.1093/eurheartj/ehq025CrossRefPubMedPubMedCentral

81.

Lyon AR, López-Fernández T, Couch LS et al (2022) 2022 ESC Guidelines on cardio-oncology developed in collaboration with the European Hematology Association (EHA), the European Society for Therapeutic Radiology and Oncology (ESTRO) and the International Cardio-Oncology Society (IC-OS). Eur Heart J 43:4229–4361. https://doi.org/10.1093/eurheartj/ehac244CrossRefPubMed

82.

Kusunose K, Agarwal S, Marwick TH et al (2014) Decision making in asymptomatic aortic regurgitation in the era of guidelines: incremental values of resting and exercise cardiac dysfunction. Circ Cardiovasc Imaging 7:352–362. https://doi.org/10.1161/CIRCIMAGING.113.001177CrossRefPubMed

83.

Witkowski TG, Thomas JD, Debonnaire PJMR et al (2013) Global longitudinal strain predicts left ventricular dysfunction after mitral valve repair. Eur Heart J Cardiovasc Imaging 14:69–76. https://doi.org/10.1093/ehjci/jes155CrossRefPubMed

84.

Yingchoncharoen T, Gibby C, Rodriguez LL et al (2012) Association of myocardial deformation with outcome in asymptomatic aortic stenosis with normal ejection fraction. Circ Cardiovasc Imaging 5:719–725. https://doi.org/10.1161/CIRCIMAGING.112.977348CrossRefPubMed

85.

Fukui M, Annabi M-S, Rosa VEE et al (2022) Comprehensive myocardial characterization using cardiac magnetic resonance associates with outcomes in low gradient severe aortic stenosis. Eur Heart J Cardiovasc Imaging 24:46–58. https://doi.org/10.1093/ehjci/jeac089CrossRefPubMed

86.

Khanna S, Bhat A, Chen HHL et al (2023) Characterization of subclinical cardiac dysfunction by speckle tracking echocardiography in patients with non-severe acute-phase myocarditis. Am J Cardiol 207:285–291. https://doi.org/10.1016/j.amjcard.2023.08.142CrossRefPubMed

87.

Gao Q, Yi W, Gao C et al (2023) Cardiac magnetic resonance feature tracking myocardial strain analysis in suspected acute myocarditis: diagnostic value and association with severity of myocardial injury. BMC Cardiovasc Disord 23:162. https://doi.org/10.1186/s12872-023-03201-2CrossRefPubMedPubMedCentral

88.

Kinoshita M, Tanabe Y, Yoshida K et al (2022) Left ventricular longitudinal strain is a major determinant of CT-derived three-dimensional maximum principal strain: comparison with two-dimensional speckle tracking echocardiography. Heart Vessels 37:31–39. https://doi.org/10.1007/s00380-021-01901-3CrossRefPubMed

89.

Ammon F, Bittner D, Hell M et al (2019) CT-derived left ventricular global strain: a head-to-head comparison with speckle tracking echocardiography. Int J Cardiovasc Imaging 35:1701–1707. https://doi.org/10.1007/s10554-019-01596-8CrossRefPubMed

90.

Buss SJ, Schulz F, Mereles D et al (2014) Quantitative analysis of left ventricular strain using cardiac computed tomography. Eur J Radiol 83:e123–e130. https://doi.org/10.1016/j.ejrad.2013.11.026CrossRefPubMed

91.

Tavakoli V, Sahba N (2014) Cardiac motion and strain detection using 4D CT images: comparison with tagged MRI, and echocardiography. Int J Cardiovasc Imaging 30:175–184. https://doi.org/10.1007/s10554-013-0305-8CrossRefPubMed

92.

Tee MW, Won S, Raman FS et al (2015) Regional strain analysis with multidetector CT in a swine cardiomyopathy model: relationship to cardiac MR tagging and myocardial fibrosis. Radiology 277:88–94. https://doi.org/10.1148/radiol.2015142339CrossRefPubMed

93.

Manohar A, Colvert GM, Ortuño JE et al (2022) Regional left ventricular endocardial strains estimated from low-dose 4DCT: comparison with cardiac magnetic resonance feature tracking. Med Phys 49:5841–5854. https://doi.org/10.1002/mp.15818CrossRefPubMed

94.

Wang R, Fang Z, Wang H et al (2021) Quantitative analysis of three-dimensional left ventricular global strain using coronary computed tomography angiography in patients with heart failure: comparison with 3T cardiac MR. Eur J Radiol 135:109485. https://doi.org/10.1016/j.ejrad.2020.109485CrossRefPubMed

95.

Li N, Zhang L, Wu H et al (2023) Quantifying left ventricular myocardial strain in patients with different CAD-RADS levels based on computed tomography feature tracking technology. Sci Rep 13:17199. https://doi.org/10.1038/s41598-023-44530-8CrossRefPubMedPubMedCentral

96.

Members WC, Otto CM, Nishimura RA et al (2021) 2020 ACC/AHA guideline for the management of patients with valvular heart disease: a report of the American College of Cardiology/American Heart Association Joint Committee on clinical practice guidelines. J Am Coll Cardiol 77:e25–e197. https://doi.org/10.1016/j.jacc.2020.11.018CrossRef

97.

Fukui M, Hashimoto G, Lopes BBC et al (2022) Association of baseline and change in global longitudinal strain by computed tomography with post-transcatheter aortic valve replacement outcomes. Eur Heart J Cardiovasc Imaging 23:476–484. https://doi.org/10.1093/ehjci/jeab229CrossRefPubMed

98.

Yang W, Li H, He J et al (2022) Left Ventricular strain measurements derived from MR feature tracking: a head-to-head comparison of a higher temporal resolution method with a conventional method. J Magn Reson Imaging 56:801–811. https://doi.org/10.1002/jmri.28053CrossRefPubMed

99.

Chen J, Tang M, Wang J-Q et al (2023) Influence of temporal resolution on computed tomography feature-tracking strain measurements. Eur J Radiol 158:110644. https://doi.org/10.1016/j.ejrad.2022.110644CrossRefPubMed

100.

Mannil M, von Spiczak J, Muehlematter UJ et al (2019) Texture analysis of myocardial infarction in CT: comparison with visual analysis and impact of iterative reconstruction. Eur J Radiol 113:245–250. https://doi.org/10.1016/j.ejrad.2019.02.037CrossRefPubMed

101.

Mannil M, von Spiczak J, Manka R, Alkadhi H (2018) Texture analysis and machine learning for detecting myocardial infarction in noncontrast low-dose computed tomography: unveiling the invisible. Invest Radiol 53:338–343. https://doi.org/10.1097/RLI.0000000000000448CrossRefPubMed

102.

Mannil M, Eberhard M, von Spiczak J et al (2020) Artificial intelligence and texture analysis in cardiac imaging. Curr Cardiol Rep 22:131. https://doi.org/10.1007/s11886-020-01402-1CrossRefPubMed

Title: Advanced myocardial characterization and function with cardiac CT
Authors: Costanza Lisi
Lukas J. Moser
Victor Mergen
Konstantin Klambauer
Eda Uçar
Matthias Eberhard
Hatem Alkadhi
Publication date: 06-09-2024
Publisher: Springer Netherlands
Keywords: Computed Tomography
Computed Tomography
Cardiac MRI
Magnetic Resonance Imaging
Magnetic Resonance Imaging
Echocardiography
Echocardiography
Published in: The International Journal of Cardiovascular Imaging
Print ISSN: 1569-5794
Electronic ISSN: 1875-8312
DOI: https://doi.org/10.1007/s10554-024-03229-1

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Advanced myocardial characterization and function with cardiac CT

Abstract

Graphical Abstract

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Noise pollution dangerous for heart health

Post-TAVI follow up: deploying virtual assistants

Antihypertensives: more effective at morning or night?

Low Lp(a) levels unlikely to increase the risk of diabetes

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A quick guide to ECGs

Keynote webinar | Spotlight on medication adherence

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Abstract

Graphical Abstract

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2024 ESC Congress

Noise pollution dangerous for heart health

Post-TAVI follow up: deploying virtual assistants

Antihypertensives: more effective at morning or night?

Low Lp(a) levels unlikely to increase the risk of diabetes

» View more highlights on the 2024 ESC Congress hub page

A quick guide to ECGs

Keynote webinar | Spotlight on medication adherence