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Cardiac MR Guidelines and Clinical Applications: Where Does Artificial Intelligence Fit In?

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Artificial Intelligence in Cardiothoracic Imaging

Part of the book series: Contemporary Medical Imaging ((CMI))

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Abstract

Cardiac magnetic resonance (CMR) has an increasingly recognized value in the management of a broad range of cardiovascular diseases, with consequent inclusion in many clinical practice guidelines (CPGs). However, widespread adoption of CMR is still limited due to many factors, such as the long acquisition time, the lengthy post-processing, the lack of standardization, and the need for highly trained operators. Artificial intelligence (AI) is progressively being adopted in the workflow of CMR examinations, from acquisition to final interpretation, and in the next future, AI will help to improve CMR availability and reproducibility. AI may also impact CMR by transforming it in a fully quantitative diagnostic tool. In this chapter, current and potential applications of AI in the CMR workflow and three exemplificative clinical settings will be discussed.

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References

  1. Leiner T, et al. SCMR position paper (2020) on clinical indications for cardiovascular magnetic resonance. J Cardiovasc Magn Reson. 2020;22:76.

    Article  PubMed  PubMed Central  Google Scholar 

  2. Esposito A, et al. The current landscape of imaging recommendations in cardiovascular clinical guidelines: toward an imaging-guided precision medicine. Radiol Med. 2020;125:1013–23.

    Article  PubMed  PubMed Central  Google Scholar 

  3. Von Knobelsdorff-Brenkenhoff F, Schulz-Menger J. Role of cardiovascular magnetic resonance in the guidelines of the European Society of Cardiology. J Cardiovasc Magn Reson. 2016;18:6.

    Article  Google Scholar 

  4. Von Knobelsdorff-Brenkenhoff F, Pilz G, Schulz-Menger J. Representation of cardiovascular magnetic resonance in the AHA/ACC guidelines. J Cardiovasc Magn Reson. 2017;19:70.

    Article  Google Scholar 

  5. Schulz-Menger 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 Cardiovasc Magn Reson. 2020;22:19.

    Article  PubMed  PubMed Central  Google Scholar 

  6. Lustig M, Donoho D, Pauly JM. Sparse MRI: the application of compressed sensing for rapid MR imaging. Magn Reson Med. 2007;58:1182–95.

    Article  PubMed  Google Scholar 

  7. Ye JC. Compressed sensing MRI: a review from signal processing perspective. BMC Biomed Eng. 2019;1:1–17.

    Article  PubMed  PubMed Central  Google Scholar 

  8. Qin C, et al. Convolutional recurrent neural networks for dynamic MR image reconstruction. IEEE Trans Med Imaging. 2019;38:280–90.

    Article  PubMed  Google Scholar 

  9. Schlemper J, Caballero J, Hajnal JV, Price AN, Rueckert D. A deep cascade of convolutional neural networks for dynamic MR image reconstruction. IEEE Trans Med Imaging. 2018;37:491–503.

    Article  PubMed  Google Scholar 

  10. Blansit K, Retson T, Masutani E, Bahrami N, Hsiao A. Deep learning–based prescription of cardiac MRI planes. Radiol Artif Intell. 2019;1:e180069.

    Article  PubMed  PubMed Central  Google Scholar 

  11. Tarroni G, et al. Learning-based quality control for cardiac MR images. IEEE Trans Med Imaging. 2019;38:1127–38.

    Article  PubMed  Google Scholar 

  12. Küstner T, et al. Retrospective correction of motion-affected MR images using deep learning frameworks. Magn Reson Med. 2019;82:1527–40.

    Article  PubMed  Google Scholar 

  13. Yancy CW, et al. 2013 ACCF/AHA guideline for the management of heart failure: a report of the American college of cardiology foundation/American heart association task force on practice guidelines. Circulation. 2013; https://doi.org/10.1161/CIR.0b013e31829e8776.

  14. Ponikowski P, et al. 2016 ESC guidelines for the diagnosis and treatment of acute and chronic heart failure. Eur Heart J. 2016;37:2129–2200m.

    Article  PubMed  Google Scholar 

  15. Di Cesare E, et al. Multimodality imaging in chronic heart failure. Radiol Med. 2020; https://doi.org/10.1007/s11547-020-01245-4.

  16. Kilner PJ, et al. Recommendations for cardiovascular magnetic resonance in adults with congenital heart disease from the respective working groups of the European Society of Cardiology. Eur Heart J. 2010; https://doi.org/10.1093/eurheartj/ehp586.

  17. Suinesiaputra A, et al. Quantification of LV function and mass by cardiovascular magnetic resonance: multi-center variability and consensus contours. J Cardiovasc Magn Reson. 2015;17:63.

    Article  PubMed  PubMed Central  Google Scholar 

  18. Peng P, et al. A review of heart chamber segmentation for structural and functional analysis using cardiac magnetic resonance imaging. MAGMA. 2016;29:155–95.

    Article  PubMed  PubMed Central  Google Scholar 

  19. Bernard O, et al. Deep learning techniques for automatic MRI cardiac multi-structures segmentation and diagnosis: is the problem solved? IEEE Trans Med Imaging. 2018;37:2514–25.

    Article  PubMed  Google Scholar 

  20. Tao Q, et al. Deep learning–based method for fully automatic quantification of left ventricle function from cine MR images: a multivendor, Multicenter Study. Radiology. 2019;290:81–8.

    Article  PubMed  Google Scholar 

  21. Palmisano A, et al. Early T1 myocardial MRI mapping: value in detecting myocardial hyperemia in acute myocarditis. Radiology. 2020;295:316–25.

    Article  PubMed  Google Scholar 

  22. Engblom H, et al. A new automatic algorithm for quantification of myocardial infarction imaged by late gadolinium enhancement cardiovascular magnetic resonance: experimental validation and comparison to expert delineations in multi-center, multi-vendor patient data. J Cardiovasc Magn Reson. 2016;18:27.

    Article  PubMed  PubMed Central  Google Scholar 

  23. Palmisano A, et al. Late iodine enhancement cardiac computed tomography for detection of myocardial scars: impact of experience in the clinical practice. Radiol Med. 2020;125:128–36.

    Article  PubMed  Google Scholar 

  24. Moccia S, et al. Development and testing of a deep learning-based strategy for scar segmentation on CMR-LGE images. Magn Reson Mater Phys Biol Med. 2019;32:187–95.

    Article  Google Scholar 

  25. Messroghli DR, et al. Clinical recommendations for cardiovascular magnetic resonance mapping of T1, T2, T2 and extracellular volume: a consensus statement by the Society for Cardiovascular Magnetic Resonance (SCMR) endorsed by the European Association for Cardiovascular Imagin. J Cardiovasc Magn Reson. 2017;19:75.

    Article  PubMed  PubMed Central  Google Scholar 

  26. Pan JA, Kerwin MJ, Salerno M. Native T1 mapping, extracellular volume mapping, and late gadolinium enhancement in cardiac amyloidosis: a meta-analysis. JACC Cardiovasc Imaging. 2020;13:1299–310.

    Article  PubMed  PubMed Central  Google Scholar 

  27. Fahmy AS, El-Rewaidy H, Nezafat M, Nakamori S, Nezafat R. Automated analysis of cardiovascular magnetic resonance myocardial native T1 mapping images using fully convolutional neural networks. J Cardiovasc Magn Reson. 2019;21:7.

    Article  PubMed  PubMed Central  Google Scholar 

  28. Liu Y, Hamilton J, Rajagopalan S, Seiberlich N. Cardiac magnetic resonance fingerprinting: technical overview and initial results. JACC Cardiovasc Imaging. 2018;11:1837–53.

    Article  PubMed  PubMed Central  Google Scholar 

  29. Cavallo AU, et al. CMR fingerprinting for myocardial T1, T2, and ECV quantification in patients with nonischemic cardiomyopathy. JACC Cardiovasc Imaging. 2019;12:1584–5.

    Article  PubMed  Google Scholar 

  30. Hamilton JI, Seiberlich N. Machine learning for rapid magnetic resonance fingerprinting tissue property quantification. Proc IEEE. 2020;108:69–85.

    Article  Google Scholar 

  31. Knuuti J, et al. 2019 ESC guidelines for the diagnosis and management of chronic coronary syndromes. Eur Heart J. 2020;41:407–77.

    Article  PubMed  Google Scholar 

  32. Palmisano A, et al. Feature tracking and mapping analysis of myocardial response to improved perfusion reserve in patients with refractory angina treated by coronary sinus reducer implantation: a CMR study. Int J Cardiovasc Imaging. 2020; https://doi.org/10.1007/s10554-020-01964-9.

  33. Knuuti J, et al. The performance of non-invasive tests to rule-in and rule-out significant coronary artery stenosis in patients with stable angina: a meta-analysis focused on post-test disease probability. Eur Heart J. 2018;39:3322–30.

    Article  PubMed  Google Scholar 

  34. Ibanez B, et al. 2017 ESC guidelines for the management of acute myocardial infarction in patients presenting with ST-segment elevation. Eur Heart J. 2018;39:119–77.

    Article  PubMed  Google Scholar 

  35. Stone GW, et al. Relationship between infarct size and outcomes following primary PCI patient-level analysis from 10 randomized trials. J Am Coll Cardiol. 2016; https://doi.org/10.1016/j.jacc.2016.01.069.

  36. Eitel I, et al. Comprehensive prognosis assessment by CMR imaging after ST-segment elevation myocardial infarction. J Am Coll Cardiol. 2014; https://doi.org/10.1016/j.jacc.2014.06.1194.

  37. Gerber BL, et al. Prognostic value of myocardial viability by delayed-enhanced magnetic resonance in patients with coronary artery disease and low ejection fraction: impact of revascularization therapy. J Am Coll Cardiol. 2012; https://doi.org/10.1016/j.jacc.2011.09.073.

  38. Collet J-P, et al. 2020 ESC guidelines for the management of acute coronary syndromes in patients presenting without persistent ST-segment elevation. Eur Heart J. 2020; https://doi.org/10.1093/eurheartj/ehaa575.

  39. Ingkanisorn WP, et al. Prognosis of negative adenosine stress magnetic resonance in patients presenting to an emergency department with chest pain. J Am Coll Cardiol. 2006; https://doi.org/10.1016/j.jacc.2005.11.059.

  40. Smulders MW, et al. Initial imaging-guided strategy versus routine care in patients with non–ST-segment elevation myocardial infarction. J Am Coll Cardiol. 2019;74:2466–77.

    Article  PubMed  Google Scholar 

  41. Kwong RY, et al. Cardiac magnetic resonance stress perfusion imaging for evaluation of patients with chest pain. J Am Coll Cardiol. 2019;74:1741–55.

    Article  PubMed  PubMed Central  Google Scholar 

  42. Knott KD, et al. The prognostic significance of quantitative myocardial perfusion: an artificial intelligence-based approach using perfusion mapping. Circulation. 2020;141:1282–91. https://doi.org/10.1161/CIRCULATIONAHA.119.044666.

    Article  PubMed  PubMed Central  Google Scholar 

  43. Thomson LEJ, et al. Cardiac magnetic resonance myocardial perfusion reserve index is reduced in women with coronary microvascular dysfunction: a national heart, lung, and blood institute-sponsored study from the women’s ischemia syndrome evaluation. Circ Cardiovasc Imaging. 2015;8:e002481.

    Article  PubMed  Google Scholar 

  44. Mordini FE, et al. Diagnostic accuracy of stress perfusion CMR in comparison with quantitative coronary angiography: fully quantitative, semiquantitative, and qualitative assessment. JACC Cardiovasc Imaging. 2014;7:14–22.

    Article  PubMed  PubMed Central  Google Scholar 

  45. Kotecha T, et al. Automated pixel-wise quantitative myocardial perfusion mapping by CMR to detect obstructive coronary artery disease and coronary microvascular dysfunction: validation against invasive coronary physiology. JACC Cardiovasc Imaging. 2019;12:1958–69.

    Article  PubMed  PubMed Central  Google Scholar 

  46. Scannell CM, et al. Deep-learning-based preprocessing for quantitative myocardial perfusion MRI. J Magn Reson Imaging. 2020; https://doi.org/10.1002/jmri.26983.

  47. Rickers C, et al. Utility of cardiac magnetic resonance imaging in the diagnosis of hypertrophic cardiomyopathy. Circulation. 2005;112:855–61.

    Article  PubMed  Google Scholar 

  48. Zamorano JL, et al. 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. 2014; https://doi.org/10.1093/eurheartj/ehu284.

  49. Brownrigg J, Lorenzini M, Lumley M, Elliott P. Diagnostic performance of imaging investigations in detecting and differentiating cardiac amyloidosis: a systematic review and meta-analysis. ESC Hear Fail. 2019;6:1041–51.

    Article  Google Scholar 

  50. De Cobelli F, et al. Delayed-enhanced cardiac MRI for differentiation of fabry’s disease from symmetric hypertrophic cardiomyopathy. Am J Roentgenol. 2009;192:W97.

    Article  Google Scholar 

  51. Perry R, et al. The role of cardiac imaging in the diagnosis and management of Anderson-Fabry disease. JACC Cardiovasc Imaging. 2019;12:1230–42.

    Article  PubMed  Google Scholar 

  52. Brouwer WP, et al. Multiple myocardial crypts on modified long-axis view are a specific finding in pre-hypertrophic HCM mutation carriers. Eur Heart J Cardiovasc Imaging. 2012; https://doi.org/10.1093/ehjci/jes005.

  53. Esposito A, et al. Impaired left ventricular energy metabolism in patients with hypertrophic cardiomyopathy is related to the extension of fibrosis at delayed gadolinium-enhanced magnetic resonance imaging. Heart. 2009;95:228–33.

    Article  CAS  PubMed  Google Scholar 

  54. Fahmy AS, et al. Three-dimensional deep convolutional neural networks for automated myocardial scar quantification in hypertrophic cardiomyopathy: a multicenter multivendor study. Radiology. 2020;294:52–60.

    Article  PubMed  Google Scholar 

  55. Dawes TJW, et al. Machine learning of three-dimensional right ventricular motion enables outcome prediction in pulmonary hypertension: a cardiac MR imaging study. Radiology. 2017;283:381–90.

    Article  PubMed  Google Scholar 

  56. Diller GP, et al. Prediction of prognosis in patients with tetralogy of Fallot based on deep learning imaging analysis. Heart. 2020; https://doi.org/10.1136/heartjnl-2019-315962.

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Vignale, D., Palmisano, A., Esposito, A. (2022). Cardiac MR Guidelines and Clinical Applications: Where Does Artificial Intelligence Fit In?. In: De Cecco, C.N., van Assen, M., Leiner, T. (eds) Artificial Intelligence in Cardiothoracic Imaging. Contemporary Medical Imaging. Humana, Cham. https://doi.org/10.1007/978-3-030-92087-6_32

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  • DOI: https://doi.org/10.1007/978-3-030-92087-6_32

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