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• Special Focus •
Development of MRI of cardiomyopathy in the last 40 years: More precise and intelligent
ZHANG Wenbo  CHENG Jingliang 

Cite this article as: Zhang WB, Cheng JL. Development of MRI of cardiomyopathy in the last 40 years: More precise and intelligent[J]. Chin J Magn Reson Imaging, 2022, 13(12): 1-5, 12. DOI:10.12015/issn.1674-8034.2022.12.001.


[Abstract] Since the 1980s when MRI technology was applied to the cardiomyopathy imaging in China, the ability to identify cardiomyopathies and their subtypes has been greatly enhanced by advances in cardiovascular MRI. In this article, we first introduce the classification and evolution of cardiomyopathies and then discuss the value of quantitative MRI, the use of radiomics and artificial intelligence technologies in differential diagnosis, risk stratification, and prognostic evaluation of various cardiomyopathies, and concludes by describing the opportunities and challenges of applying new technologies. We expect to establish standard imaging methods and reference values in the future through large-scale, multicenter cohort studies, meta-analyses, and reviews with large sample sizes in order to serve the clinical treatment process and protect individuals' health.
[Keywords] cardiomyopathy；quantitative techniques；magnetic resonance imaging；radiomics；artificial intelligence

ZHANG Wenbo   CHENG Jingliang*  

Department of MRI, the First Affiliated Hospital of Zhengzhou University, Zhengzhou 450052, China

Cheng JL, E-mail: fccchengjl@zzu.edu.cn

Conflicts of interest   None.

Received  2022-08-12
Accepted  2022-12-12
DOI: 10.12015/issn.1674-8034.2022.12.001
Cite this article as: Zhang WB, Cheng JL. Development of MRI of cardiomyopathy in the last 40 years: More precise and intelligent[J]. Chin J Magn Reson Imaging, 2022, 13(12): 1-5, 12. DOI:10.12015/issn.1674-8034.2022.12.001.

[1]
Pelliccia F, Limongelli G, Autore C, et al. Sex-related differences in cardiomyopathies[J]. Int J Cardiol, 2019, 286: 239-243. DOI: 10.1016/j.ijcard.2018.10.091.
[2]
Kayvanpour E, Sedaghat-Hamedani F, Gi WT, et al. Clinical and genetic insights into non-compaction:a meta-analysis and systematic review on 7598 individuals[J].Clin Res Cardiol, 2019, 108(11): 1297-1308. DOI: 10.1007/s00392-019-01465-3.
[3]
Schulz-Menger J, Bluemke DA, Bremerich J, et al. Standardized image interpretation and post-processing in cardiovascular magnetic resonance-2020 update: society for cardiovascular magnetic resonance (SCMR): board of trustees task force on standardized post-processing[J/OL]. J Cardiovasc Magn Reson, 2020, 22(1): 19 [2022-12-11]. https://jcmr-online.biomedcentral.com/articles/10.1186/s12968-020-00682-4. DOI: 10.1186/s12968-020-00610-6.
[4]
Lu MJ, Zhao SH. CMR in China of the last 30 years[J]. Chin J Magn Reson Imaging, 2019, 10(10): 727-731. DOI: 10.12015/issn.1674-8034.2019.10.002.
[5]
Zhao PJ, Li HJ, Xia LM. Strengthen and expand the clinical application and research of cardiac MR quantitative technology[J]. Chin J Radiol, 2021, 55(12): 1235-1240. DOI: 10.3760/cma.j.cn112149-20210128-00079.
[6]
Dass S, Suttie JJ, Piechnik SK, et al. Myocardial tissue characterization using magnetic resonance noncontrast t1 mapping in hypertrophic and dilated cardiomyopathy[J]. Circ Cardiovasc Imaging, 2012, 5(6): 726-733. DOI: 10.1161/CIRCIMAGING.112.976738.
[7]
Puntmann VO, Carr-White G, Jabbour A, et al. T1-Mapping and Outcome in Nonischemic Cardiomyopathy: All-Cause Mortality and Heart Failure[J]. JACC Cardiovasc Imaging, 2016, 9(1): 40-50. DOI: 10.1016/j.jcmg.2015.12.001.
[8]
Mehta BB, Chen X, Bilchick KC, et al. Accelerated and navigator-gated look-locker imaging for cardiac T1 estimation (ANGIE): Development and application to T1 mapping of the right ventricle[J]. Magn Reson Med, 2015, 73(1): 150-160. DOI: 10.1002/mrm.25100.
[9]
Li Y, Liu X, Yang F, et al. Prognostic value of myocardial extracellular volume fraction evaluation based on cardiac magnetic resonance T1 mapping with T1 long and short in hypertrophic cardiomyopathy[J]. Eur Radiol, 2021, 1(7): 4557-4567. DOI: 10.1007/s00330-020-07650-7.
[10]
Taha K, Bourfiss M, Te Riele ASJM, et al. A head-to-head comparison of speckle tracking echocardiography and feature tracking cardiovascular magnetic resonance imaging in right ventricular deformation[J]. Eur Heart J Cardiovasc Imaging, 2021, 22(8): 950-958. DOI: 10.1093/ehjci/jeaa088.
[11]
Yang LP, Zhang LB, Cao SD, et al. Advanced myocardial characterization in hypertrophic cardiomyopathy: feasibility of CMR-based feature tracking strain analysis in a case-control study[J]. Eur Radiol, 2020, 30(11): 6118-6128. DOI: 10.1007/s00330-020-06922-6.
[12]
Romano S, Judd RM, Kim RJ. Feature-tracking global longitudinal strain predicts death in a multicenter population of patients with ischemic and nonischemic dilated cardiomyopathy incremental to ejection fraction and late gadolinium enhancement[J]. JACC Cardiovasc Imaging, 2018, 11(10): 1419-1429. DOI: 10.1016/j.jcmg.2017.10.024.
[13]
Kowallick JT, Kutty S, Edelmann F, et al. Quantification of left atrial strain and strain rate using Cardiovascular Magnetic Resonance myocardial feature tracking: a feasibility study[J]. J Cardiovasc Magn Reson, 2014, 16(1): 60 [2022-12-11]. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4422260/pdf/s12968-014-0060-6.pdf. DOI: 10.1186/s12968-014-0060-6.
[14]
Mou A, Zhang C, Li M, et al. Evaluation of myocardial microcirculation using intravoxel incoherent motion imaging[J]. J Magn Reson Imaging, 2017, 46(6): 1818-1828. DOI: 10.1002/jmri.25706.
[15]
Khalique Z, Ferreira PF, Scott AD, et al. Diffusion Tensor Cardiovascular Magnetic Resonance Imaging: A Clinical Perspective[J]. JACC Cardiovasc Imaging, 2020, 13(5): 1235-1255. DOI: 10.1016/j.jcmg.2019.07.016.
[16]
Nielles-Vallespin S, Khalique Z, Ferreira PF, et al. Assessment of Myocardial Microstructural Dynamics by In Vivo Diffusion Tensor Cardiac Magnetic Resonance[J]. J Am Coll Cardiol, 2017, 69(6): 661-676. DOI: 10.1016/j.jacc.2016.11.051.
[17]
Nguyen C, Lu M, Fan Z, et al. Contrast-free detection of myocardial fibrosis in hypertrophic cardiomyopathy patients with diffusion-weighted cardiovascular magnetic resonance[J/OL]. J Cardiovasc Magn Reson, 2015, 17: 107 [2022-12-11]. https://jcmr-online.biomedcentral.com/articles/10.1186/s12968-015-0214-1. DOI: 10.1186/s12968-015-0214-1.
[18]
Khalique Z, Ferreira PF, Scott AD, et al. Diffusion Tensor Cardiovascular Magnetic Resonance of Microstructural Recovery in Dilated Cardiomyopathy[J]. JACC Cardiovasc Imaging, 2018, 11(10): 1548-1550. DOI: 10.1016/j.jcmg.2018.01.025.
[19]
Sree Raman K, Nucifora G, Selvanayagam JB. Novel cardiovascular magnetic resonance oxygenation approaches in understanding pathophysiology of cardiac diseases[J]. Clin Exp Pharmacol Physiol, 2018, 45(5): 475-480. DOI: 10.1111/1440-1681.12916.
[20]
Karamitsos TD, Dass S, Suttie J, et al. Blunted myocardial oxygenation response during vasodilator stress in patients with hypertrophic cardiomyopathy[J]. J Am Coll Cardiol, 2013, 61(11): 1169-1176. DOI: 10.1016/j.jacc.2012.12.024.
[21]
Dass S, Holloway CJ, Cochlin LE, et al. No Evidence of Myocardial Oxygen Deprivation in Nonischemic Heart Failure[J]. Circ Heart Fail, 2015, 8(6): 1088-1093. DOI: 10.1161/CIRCHEARTFAILURE.114.002169.
[22]
Eck BL, Flamm SD, Kwon DH, et al. Cardiac Magnetic Resonance Fingerprining: Trends in Technical Development and Potential Clinical Applications[J]. Prog Nucl Magn Reson Spectrosc, 2021, 122: 11-22. DOI: 10.1016/j.pnmrs.2020.10.001.
[23]
Cavallo AU, Liu Y, Patterson A, et al. CMR fingerprinting for myocardial T1, T2, and ECV quantification in patients with nonischemic cardiomyopathy[J]. JACC Cardiovasc Imaging, 2019, 12(8Pt 1): 1584-1585. DOI: 10.1016/j.jcmg.2019.01.034.
[24]
Liu Y, Hamilton J, Rajagopalan S, et al. Cardiac magnetic resonance fingerprinting: technical overview and initial results[J]. JACC Cardiovasc Imaging, 2018, 11(12): 1837-1853. DOI: 10.1016/j.jcmg.2018.08.028.
[25]
Stoll VM, Clarke WT, Levelt E, et al. Dilated Cardiomyopathy: Phosphorus 31 MR Spectroscopy at 7 T[J]. Radiology, 2016, 281(2): 409-417. DOI: 10.1148/radiol.2016152629.
[26]
Rayner JJ, Peterzan MA, Clarke WT, et al. Obesity modifies the energetic phenotype of dilated cardiomyopathy[J]. Eur Heart J, 2021, 43(9): 868-877. DOI: 10.1093/eurheartj/ehab663.
[27]
Antman EM, Loscalzo J. Precision medicine in cardiology[J]. Nat Rev Cardiol, 2016, 13(10): 591-602. DOI: 10.1038/nrcardio.2016.101.
[28]
Hassani C, Saremi F, Varghese BA, et al. Myocardial radiomics in cardiac MRI[J]. AJR Am J Roentgenol, 2020, 214(3): 536-545. DOI: 10.2214/AJR.19.21986.
[29]
Raisi-Estabragh Z, Izquierdo C, Campello VM, et al. Cardiac magnetic resonance radiomics: basic principles and clinical perspectives[J]. Eur Heart J, 2020, 21(4): 349-356. DOI: 10.1093/ehjci/jeaa028.
[30]
Shi RY, Wu R, An DL, et al. Texture analysis applied in T1 maps and extracellular volume obtained using cardiac MRI in the diagnosis of hypertrophic cardiomyopathy and hypertensive heart disease compared with normal controls[J]. Clin Radiol, 2021, 76(3): 236-239. DOI: 10.1016/j.crad.2020.11.001.
[31]
Baeßler B, Mannil M, Maintz D, et al. Texture analysis and machine learning of non-contrast T1-weighted MR images in patients with hypertrophic cardiomyopathy—Preliminary results[J]. Eur J Radiol, 2018, 102: 61-67. DOI: 10.1016/j.ejrad.2018.03.013.
[32]
Wang J, Yang FY, Liu WT, et al. Radiomic analysis of native T1 mapping images discriminates between MYH7 and MYBPC3-related hypertrophic cardiomyopathy[J]. J Magn Reson Imaging, 2020, 52(6): 1714-1721. DOI: 10.1002/jmri.27209.
[33]
Cheng S, Fang M, Cui C, et al. LGE-CMR-derived texture features reflect poor prognosis in hypertrophic cardiomyopathy patients with systolic dysfunction: preliminary results[J]. Eur Radiol, 2018, 28(11): 4615-4624. DOI: 10.1007/s00330-018-5391-5.
[34]
Wang J, Bravo L, Zhang J, et al. Radiomics Analysis Derived From LGE-MRI Predict Sudden Cardiac Death in Participants With Hypertrophic Cardiomyopathy[J/OL]. Front Cardiovasc Med, 2021, 8: 766287 [2022-12-11]. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8702805/pdf/fcvm-08-766287.pdf. DOI: 10.3389/fcvm.2021.766287.
[35]
Bello GA, Dawes TJW, Duan J, et al. Deep-learning cardiac motion analysis for human survival prediction[J]. Nat Mach Intell, 2019, 1: 95-104. DOI: 10.1038/s42256-019-0019-2.
[36]
Satriano A, Afzal Y, Sarim Afzal M, et al. Neural-Network-Based Diagnosis Using 3-Dimensional Myocardial Architecture and Deformation: Demonstration for the Differentiation of Hypertrophic Cardiomyopathy[[J/OL]. Front Cardiovasc Med, 2020, 7: 584727 [2022-12-11]. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7693650/pdf/fcvm-07-584727.pdf. DOI: 10.3389/fcvm.2020.584727.
[37]
Zhou H, Li L, Liu Z, et al. Deep learning algorithm to improve hypertrophic cardiomyopathy mutation prediction using cardiac cine images[J]. Eur Radiol, 2021, 31(6): 3931-3940. DOI: 10.1007/s00330-020-07454-9.
[38]
Mancio J, Pashakhanloo F, El-Rewaidy H, et al. Machine learning phenotyping of scarred myocardium from cine in hypertrophic cardiomyopathy[J]. Eur Heart J Cardiovasc Imaging, 2022, 23(4): 532-542. DOI: 10.1093/ehjci/jeab056.
[39]
Zhang Q, Burrage MK, Lukaschuk E, et al. Toward Replacing Late Gadolinium Enhancement With Artificial Intelligence Virtual Native Enhancement for Gadolinium-Free Cardiovascular Magnetic Resonance Tissue Characterization in Hypertrophic Cardiomyopathy[J]. Circulation, 2021, 144(8): 589-599. DOI: 10.1161/CIRCULATIONAHA.121.054432.

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