Advertisement

Machine Learning and Artificial Intelligence in Cardiovascular Imaging

  • Marwen Eid
  • James V. Spearman
  • Marly van Assen
  • Domenico De Santis
  • Pooyan Sahbaee
  • Scott P. Landreth
  • Brian Jacobs
  • Carlo N. De Cecco
Chapter
Part of the Contemporary Medical Imaging book series (CMI)

Abstract

Cardiovascular imaging is playing an increasingly important role in the management of cardiovascular disease. As new imaging technologies are constantly being introduced, this has only been bolstered. Computer systems have always provided assistance to radiologists in their clinical routine, but recent advancements in computational power and the improvement in machine learning algorithms have introduced new possibilities and applications for these systems. In this chapter, a brief overview of machine learning and artificial intelligence will be presented, discussing its applications in medicine and radiology, with a special focus on cardiovascular imaging applications.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Erickson BJ, Korfiatis P, Akkus Z, Kline TL. Machine learning for medical imaging. Radiographics. 2017;37(2):505–15.  https://doi.org/10.1148/rg.2017160130.PubMedPubMedCentralGoogle Scholar
  2. 2.
    Friedman J, Hastie T, Tibshirani R. The elements of statistical learning: Springer series in statistics. Berlin: Springer; 2001.Google Scholar
  3. 3.
    Abu-Mostafa YS, Magdon-Ismail M, Lin H-T. Learning from data. New York: AMLBook; 2012.Google Scholar
  4. 4.
    Jordan M, Mitchell T. Machine learning: trends, perspectives, and prospects. Science. 2015;349(6245):255–60.PubMedGoogle Scholar
  5. 5.
    Kohli M, Prevedello LM, Filice RW, Geis JR. Implementing machine learning in radiology practice and research. AJR Am J Roentgenol. 2017;208(4):754–60.PubMedGoogle Scholar
  6. 6.
    Martinez TR, Zeng X. Feature weighting using neural networks. In: Proceedings of the IEEE International Joint Conference on Neural Networks IJCNN’04; 2004. p. 1327–30.Google Scholar
  7. 7.
    Alpaydin E. Introduction to machine learning. Cambridge: The MIT Press; 2014.Google Scholar
  8. 8.
    Langley P. Machine learning as an experimental science. Mach Learn. 1988;3(1):5–8.Google Scholar
  9. 9.
    Özöğür-Akyüz S, Ünay D, Smola A. Guest editorial: model selection and optimization in machine learning. Mach Learn. 2011;85(1):1.Google Scholar
  10. 10.
    Motwani M, Dey D, Berman DS, Germano G, Achenbach S, Al-Mallah MH, et al. Machine learning for prediction of all-cause mortality in patients with suspected coronary artery disease: a 5-year multicentre prospective registry analysis. Eur Heart J. 2017;38(7):500–7.PubMedGoogle Scholar
  11. 11.
    Greenspan H, van Ginneken B, Summers RM. Guest editorial deep learning in medical imaging: Overview and future promise of an exciting new technique. IEEE Trans Med Imaging. 2016;35(5):1153–9.Google Scholar
  12. 12.
    Petersen SE, Matthews PM, Bamberg F, Bluemke DA, Francis JM, Friedrich MG, et al. Imaging in population science: cardiovascular magnetic resonance in 100,000 participants of UK Biobank-rationale, challenges and approaches. J Cardiovasc Magn Reson. 2013;15(1):46.PubMedPubMedCentralGoogle Scholar
  13. 13.
    Fonseca CG, Backhaus M, Bluemke DA, Britten RD, Do Chung J, Cowan BR, et al. The Cardiac Atlas Project—an imaging database for computational modeling and statistical atlases of the heart. Bioinformatics. 2011;27(16):2288–95.PubMedPubMedCentralGoogle Scholar
  14. 14.
    del Toro OAJ, Goksel O, Menze B, Müller H, Langs G, Weber M-A, et al. VISCERAL–VISual Concept Extraction challenge in RAdioLogy: ISBI 2014 challenge organization. Proceedings of the VISCERAL Challenge at ISBI. 2014;1194:6–15.Google Scholar
  15. 15.
    Kirişli H, Schaap M, Metz C, Dharampal A, Meijboom WB, Papadopoulou S-L, et al. Standardized evaluation framework for evaluating coronary artery stenosis detection, stenosis quantification and lumen segmentation algorithms in computed tomography angiography. Med Image Anal. 2013;17(8):859–76.PubMedGoogle Scholar
  16. 16.
    Grand Challenges in Biomedical Imaging. 2016. Available from: https://grand-challenge.org/All_Challenges/.
  17. 17.
    Data Science Bowl. 2016. Available from: https://www.kaggle.com/c/second-annual-data-science-bowl.
  18. 18.
    Seidman AD, Pilewskie ML, Robson ME, Kelvin JF, Zauderer MG, Epstein AS, et al. Integration of multi-modality treatment planning for early stage breast cancer (BC) into Watson for Oncology, a Decision Support System: Seeing the forest and the trees. J Clin Oncol. 2015;33(15_suppl):e12042–e.Google Scholar
  19. 19.
    Johnson AE, Ghassemi MM, Nemati S, Niehaus KE, Clifton DA, Clifford GD. Machine learning and decision support in critical care. Proc IEEE. 2016;104(2):444–66.Google Scholar
  20. 20.
    Gultepe E, Green JP, Nguyen H, Adams J, Albertson T, Tagkopoulos I. From vital signs to clinical outcomes for patients with sepsis: a machine learning basis for a clinical decision support system. J Am Med Inform Assoc. 2014;21(2):315–25.PubMedGoogle Scholar
  21. 21.
    Gulshan V, Peng L, Coram M, Stumpe MC, Wu D, Narayanaswamy A, et al. Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. JAMA. 2016;316(22):2402–10.PubMedGoogle Scholar
  22. 22.
    Long E, Lin H, Liu Z, Wu X, Wang L, Jiang J, et al. An artificial intelligence platform for the multihospital collaborative management of congenital cataracts. Nat Biomed Eng. 2017;1:0024.Google Scholar
  23. 23.
    Esteva A, Kuprel B, Novoa RA, Ko J, Swetter SM, Blau HM, et al. Dermatologist-level classification of skin cancer with deep neural networks. Nature. 2017;542(7639):115–8.PubMedGoogle Scholar
  24. 24.
    Beck AH, Sangoi AR, Leung S, Marinelli RJ, Nielsen TO, van de Vijver MJ, et al. Systematic analysis of breast cancer morphology uncovers stromal features associated with survival. Sci Transl Med. 2011;3(108):108ra13.Google Scholar
  25. 25.
    Yu KH, Zhang C, Berry GJ, Altman RB, Re C, Rubin DL, et al. Predicting non-small cell lung cancer prognosis by fully automated microscopic pathology image features. Nat Commun. 2016;7:12474.PubMedPubMedCentralGoogle Scholar
  26. 26.
  27. 27.
    Artificial Intelligence Used To Detect Rare Leukemia Type In Japan. 2016. Available from: http://www.ndtv.com/health/artificial-intelligence-used-to-detect-rare-leukemia-type-in-japan-1440789.
  28. 28.
    Sharpless N. As seen on 60 minutes: Watson accelerates precision oncology. 2016. Available from: https://www.ibm.com/blogs/think/2016/10/sharpless/.
  29. 29.
    McDonald JF, Mezencev R, Long TQ, Benigno B, Bonta I, Priore GD. Accurate prediction of optimal cancer drug therapies from molecular profiles by a machine-learning algorithm. J Clin Oncol. 2015;33(15_suppl):e22182–e.Google Scholar
  30. 30.
    Ramarajan N, Badwe RA, Perry P, Srivastava G, Nair NS, Gupta S. A machine learning approach to enable evidence based oncology practice: ranking grade and applicability of RCTs to individual patients. J Clin Oncol. 2016;34(15_suppl):e18165–e.Google Scholar
  31. 31.
    Sidaway P. Immunotherapy: genomic and immunological features predict a response. Nat Rev Clin Oncol. 2017;14(5):263.PubMedGoogle Scholar
  32. 32.
    Shah SJ, Katz DH, Selvaraj S, Burke MA, Yancy CW, Gheorghiade M, et al. Phenomapping for novel classification of heart failure with preserved ejection fraction. Circulation. 2015;131(3):269–79.  https://doi.org/10.1161/CIRCULATIONAHA.114.010637.
  33. 33.
    Wang S, Summers RM. Machine learning and radiology. Med Image Anal. 2012;16(5):933–51.PubMedPubMedCentralGoogle Scholar
  34. 34.
    Akkus Z, Galimzianova A, Hoogi A, Rubin DL, Erickson BJ. Deep learning for brain MRI segmentation: state of the art and future directions. J Digit Imaging. 2017;30:449.PubMedPubMedCentralGoogle Scholar
  35. 35.
    Kamnitsas K, Ledig C, Newcombe VFJ, Simpson JP, Kane AD, Menon DK, et al. Efficient multi-scale 3D CNN with fully connected CRF for accurate brain lesion segmentation. Med Image Anal. 2017;36:61–78.PubMedGoogle Scholar
  36. 36.
    Havaei M, Davy A, Warde-Farley D, Biard A, Courville A, Bengio Y, et al. Brain tumor segmentation with deep neural networks. Med Image Anal. 2017;35:18–31.PubMedGoogle Scholar
  37. 37.
    van Ginneken B. Fifty years of computer analysis in chest imaging: rule-based, machine learning, deep learning. Radiol Phys Technol. 2017;10(1):23–32.PubMedPubMedCentralGoogle Scholar
  38. 38.
    Doel T, Gavaghan DJ, Grau V. Review of automatic pulmonary lobe segmentation methods from CT. Comput Med Imaging Graph. 2015;40:13–29.PubMedGoogle Scholar
  39. 39.
    Mansoor A, Bagci U, Foster B, Xu Z, Papadakis GZ, Folio LR, et al. Segmentation and image analysis of abnormal lungs at CT: current approaches, challenges, and future trends. Radiographics. 2015;35(4):1056–76.PubMedPubMedCentralGoogle Scholar
  40. 40.
    Ikushima K, Arimura H, Jin Z, Yabu-Uchi H, Kuwazuru J, Shioyama Y, et al. Computer-assisted framework for machine-learning-based delineation of GTV regions on datasets of planning CT and PET/CT images. J Radiat Res. 2017;58(1):123–34.PubMedPubMedCentralGoogle Scholar
  41. 41.
    Ibragimov B, Xing L. Segmentation of organs-at-risks in head and neck CT images using convolutional neural networks. Med Phys. 2017;44(2):547–57.PubMedPubMedCentralGoogle Scholar
  42. 42.
    Belharbi S, Chatelain C, Herault R, Adam S, Thureau S, Chastan M, et al. Spotting L3 slice in CT scans using deep convolutional network and transfer learning. Comput Biol Med. 2017;87:95–103.PubMedGoogle Scholar
  43. 43.
    Summers RM. Progress in fully automated abdominal CT interpretation. AJR Am J Roentgenol. 2016;207(1):67–79.PubMedPubMedCentralGoogle Scholar
  44. 44.
    Roth HR, Lu L, Liu J, Yao J, Seff A, Cherry K, et al. Improving computer-aided detection using convolutional neural networks and random view aggregation. IEEE Trans Med Imaging. 2016;35(5):1170–81.PubMedGoogle Scholar
  45. 45.
    Näppi JJ, Hironaka T, Regge D, Yoshida H, editors. Deep transfer learning of virtual endoluminal views for the detection of polyps in CT colonography. San Diego: Proc SPIE; 2016.Google Scholar
  46. 46.
    Qi D, Hao C, Lequan Y, Lei Z, Jing Q, Defeng W, et al. Automatic detection of cerebral microbleeds from MR images via 3D convolutional neural networks. IEEE Trans Med Imaging. 2016;35(5):1182–95.Google Scholar
  47. 47.
    Anthimopoulos M, Christodoulidis S, Ebner L, Christe A, Mougiakakou S. Lung pattern classification for interstitial lung diseases using a deep convolutional neural network. IEEE Trans Med Imaging. 2016;35(5):1207–16.PubMedGoogle Scholar
  48. 48.
    Bergtholdt M, Wiemker R, Klinder T, editors. Pulmonary nodule detection using a cascaded SVM classifier. SPIE Medical Imaging; 2016. International Society for Optics and Photonics.Google Scholar
  49. 49.
    Setio AA, Ciompi F, Litjens G, Gerke P, Jacobs C, van Riel SJ, et al. Pulmonary nodule detection in CT images: false positive reduction using multi-view convolutional networks. IEEE Trans Med Imaging. 2016;35(5):1160–9.PubMedGoogle Scholar
  50. 50.
    Ciompi F, Chung K, Van Riel SJ, Setio AAA, Gerke PK, Jacobs C, et al. Towards automatic pulmonary nodule management in lung cancer screening with deep learning. Sci Rep. 2017;7:46479.PubMedPubMedCentralGoogle Scholar
  51. 51.
    Rucco M, Sousa-Rodrigues D, Merelli E, Johnson JH, Falsetti L, Nitti C, et al. Neural hypernetwork approach for pulmonary embolism diagnosis. BMC Res Notes. 2015;8:617.PubMedPubMedCentralGoogle Scholar
  52. 52.
    Rajkomar A, Lingam S, Taylor AG, Blum M, Mongan J. High-throughput classification of radiographs using deep convolutional neural networks. J Digit Imaging. 2017;30(1):95–101.PubMedGoogle Scholar
  53. 53.
    Rampun A, Tiddeman B, Zwiggelaar R, Malcolm P. Computer aided diagnosis of prostate cancer: a texton based approach. Med Phys. 2016;43(10):5412.PubMedPubMedCentralGoogle Scholar
  54. 54.
    Dhungel N, Carneiro G, Bradley AP. A deep learning approach for the analysis of masses in mammograms with minimal user intervention. Med Image Anal. 2017;37:114–28.PubMedGoogle Scholar
  55. 55.
    Kooi T, Litjens G, van Ginneken B, Gubern-Mérida A, Sánchez CI, Mann R, et al. Large scale deep learning for computer aided detection of mammographic lesions. Med Image Anal. 2017;35:303–12.PubMedGoogle Scholar
  56. 56.
    Lassen B, van Rikxoort EM, Schmidt M, Kerkstra S, van Ginneken B, Kuhnigk J-M. Automatic segmentation of the pulmonary lobes from chest CT scans based on fissures, vessels, and bronchi. IEEE Trans Med Imaging. 2013;32(2):210–22.PubMedGoogle Scholar
  57. 57.
    Wang J, Yang X, Cai H, Tan W, Jin C, Li L. Discrimination of breast cancer with microcalcifications on mammography by deep learning. Sci Rep. 2016;6:27327.PubMedPubMedCentralGoogle Scholar
  58. 58.
    Dilsizian SE, Siegel EL. Artificial intelligence in medicine and cardiac imaging: harnessing big data and advanced computing to provide personalized medical diagnosis and treatment. Curr Cardiol Rep. 2013;16(1):441.Google Scholar
  59. 59.
    Krittanawong C, Zhang H, Wang Z, Aydar M, Kitai T. Artificial intelligence in precision cardiovascular medicine. J Am Coll Cardiol. 2017;69(21):2657–64.PubMedGoogle Scholar
  60. 60.
    Slomka PJ, Dey D, Sitek A, Motwani M, Berman DS, Germano G. Cardiac imaging: working towards fully-automated machine analysis & interpretation. Expert Rev Med Devices. 2017;14(3):197–212.PubMedPubMedCentralGoogle Scholar
  61. 61.
    Douglas PS, Cerqueira MD, Berman DS, Chinnaiyan K, Cohen MS, Lundbye JB, et al. The future of cardiac imaging: report of a think tank convened by the American College of Cardiology. JACC Cardiovasc Imaging. 2016;9(10):1211–23.Google Scholar
  62. 62.
    Nagueh SF. Unleashing the potential of machine-based learning for the diagnosis of cardiac diseases. Am Heart Assoc; 2016; 9(6).Google Scholar
  63. 63.
    Tesche C, De Cecco CN, Albrecht MH, Duguay TM, Bayer RR 2nd, Litwin SE, et al. Coronary CT angiography-derived fractional flow reserve. Radiology. 2017;285(1):17–33.PubMedGoogle Scholar
  64. 64.
    Avendi MR, Kheradvar A, Jafarkhani H. A combined deep-learning and deformable-model approach to fully automatic segmentation of the left ventricle in cardiac MRI. Med Image Anal. 2016;30:108–19.PubMedGoogle Scholar
  65. 65.
    Germano G, Kavanagh PB, Slomka PJ, Van Kriekinge SD, Pollard G, Berman DS. Quantitation in gated perfusion SPECT imaging: the Cedars-Sinai approach. J Nucl Cardiol. 2007;14(4):433–54.PubMedGoogle Scholar
  66. 66.
    Garcia EV, Faber TL, Cooke CD, Folks RD, Chen J, Santana C. The increasing role of quantification in clinical nuclear cardiology: the Emory approach. J Nucl Cardiol. 2007;14(4):420–32.PubMedGoogle Scholar
  67. 67.
    Arsanjani R, Xu Y, Hayes SW, Fish M, Lemley M, Gerlach J, et al. Comparison of fully automated computer analysis and visual scoring for detection of coronary artery disease from myocardial perfusion SPECT in a large population. J Nucl Med. 2013;54(2):221–8.PubMedPubMedCentralGoogle Scholar
  68. 68.
    Betancur J, Rubeaux M, Fuchs T, Otaki Y, Arnson Y, Slipczuk L, et al. Automatic valve plane localization in myocardial perfusion SPECT/CT by machine learning: anatomical and clinical validation. J Nucl Med. 2017;58(6):961–7.PubMedPubMedCentralGoogle Scholar
  69. 69.
    Arnoldi E, Gebregziabher M, Schoepf UJ, Goldenberg R, Ramos-Duran L, Zwerner PL, et al. Automated computer-aided stenosis detection at coronary CT angiography: initial experience. Eur Radiol. 2010;20(5):1160–7.PubMedGoogle Scholar
  70. 70.
    Halpern EJ, Halpern DJ. Diagnosis of coronary stenosis with CT angiography: comparison of automated computer diagnosis with expert readings. Acad Radiol. 2011;18(3):324–33.PubMedGoogle Scholar
  71. 71.
    Goldenberg R, Eilot D, Begelman G, Walach E, Ben-Ishai E, Peled N. Computer-aided simple triage (CAST) for coronary CT angiography (CCTA). Int J Comput Assist Radiol Surg. 2012;7(6):819–27.PubMedGoogle Scholar
  72. 72.
    Kang K-W, Chang H-J, Shim H, Kim Y-J, Choi B-W, Yang W-I, et al. Feasibility of an automatic computer-assisted algorithm for the detection of significant coronary artery disease in patients presenting with acute chest pain. Eur J Radiol. 2012;81(4):e640–e6.PubMedGoogle Scholar
  73. 73.
    Kang D, Slomka P, Nakazato R, Cheng VY, Min JK, Li D, et al., editors. Automatic detection of significant and subtle arterial lesions from coronary CT angiography. SPIE Medical Imaging; 2012. International Society for Optics and Photonics.Google Scholar
  74. 74.
    Kang D, Dey D, Slomka PJ, Arsanjani R, Nakazato R, Ko H, et al. Structured learning algorithm for detection of nonobstructive and obstructive coronary plaque lesions from computed tomography angiography. J Med Imaging (Bellingham, Wash). 2015;2(1):014003.Google Scholar
  75. 75.
    Agatston AS, Janowitz WR, Hildner FJ, Zusmer NR, Viamonte M Jr, Detrano R. Quantification of coronary artery calcium using ultrafast computed tomography. J Am Coll Cardiol. 1990;15(4):827–32.PubMedGoogle Scholar
  76. 76.
    Kondos GT, Hoff JA, Sevrukov A, Daviglus ML, Garside DB, Devries SS, et al. Electron-beam tomography coronary artery calcium and cardiac events: a 37-month follow-up of 5635 initially asymptomatic low- to intermediate-risk adults. Circulation. 2003;107(20):2571–6.PubMedGoogle Scholar
  77. 77.
    Wolterink JM, Leiner T, de Vos BD, van Hamersvelt RW, Viergever MA, Išgum I. Automatic coronary artery calcium scoring in cardiac CT angiography using paired convolutional neural networks. Med Image Anal. 2016;34:123–36.PubMedGoogle Scholar
  78. 78.
    Sengupta PP, Huang YM, Bansal M, Ashrafi A, Fisher M, Shameer K, et al. Cognitive machine-learning algorithm for cardiac imaging: a pilot study for differentiating constrictive pericarditis from restrictive cardiomyopathy. Circ Cardiovasc Imaging. 2016;9(6):e004330.PubMedPubMedCentralGoogle Scholar
  79. 79.
    Mahmood SS, Levy D, Vasan RS, Wang TJ. The Framingham Heart Study and the epidemiology of cardiovascular disease: a historical perspective. Lancet. 2014;383(9921):999–1008.PubMedGoogle Scholar
  80. 80.
    Budoff MJ, Dowe D, Jollis JG, Gitter M, Sutherland J, Halamert E, et al. Diagnostic performance of 64-multidetector row coronary computed tomographic angiography for evaluation of coronary artery stenosis in individuals without known coronary artery disease: results from the prospective multicenter ACCURACY (Assessment by Coronary Computed Tomographic Angiography of Individuals Undergoing Invasive Coronary Angiography) trial. J Am Coll Cardiol. 2008;52(21):1724–32.PubMedGoogle Scholar
  81. 81.
    Chow BJ, Small G, Yam Y, Chen L, Achenbach S, Al-Mallah M, et al. Incremental prognostic value of cardiac computed tomography in coronary artery disease using CONFIRMClinical perspective. Circ Cardiovasc Imaging. 2011;4(5):463–72.PubMedGoogle Scholar
  82. 82.
    Hadamitzky M, Freißmuth B, Meyer T, Hein F, Kastrati A, Martinoff S, et al. Prognostic value of coronary computed tomographic angiography for prediction of cardiac events in patients with suspected coronary artery disease. JACC Cardiovasc Imaging. 2009;2(4):404–11.PubMedGoogle Scholar
  83. 83.
    Hadamitzky M, Taubert S, Deseive S, Byrne RA, Martinoff S, Schomig A, et al. Prognostic value of coronary computed tomography angiography during 5 years of follow-up in patients with suspected coronary artery disease. Eur Heart J. 2013;34(42):3277–85.PubMedGoogle Scholar
  84. 84.
    Dawes TJ, de Marvao A, Shi W, Fletcher T, Watson GM, Wharton J, et al. Machine learning of three-dimensional right ventricular motion enables outcome prediction in pulmonary hypertension: a cardiac MR imaging study. Radiology. 2017;283(2):381–90.PubMedPubMedCentralGoogle Scholar
  85. 85.
    Kotu LP, Engan K, Borhani R, Katsaggelos AK, Orn S, Woie L, et al. Cardiac magnetic resonance image-based classification of the risk of arrhythmias in post-myocardial infarction patients. Artif Intell Med. 2015;64(3):205–15.PubMedGoogle Scholar
  86. 86.
    Arsanjani R, Dey D, Khachatryan T, Shalev A, Hayes SW, Fish M, et al. Prediction of revascularization after myocardial perfusion SPECT by machine learning in a large population. J Nucl Cardiol. 2015;22(5):877–84.PubMedGoogle Scholar
  87. 87.
    Jha S, Topol EJ. Adapting to artificial intelligence: radiologists and pathologists as information specialists. JAMA. 2016;316(22):2353–4.PubMedGoogle Scholar
  88. 88.
    Chinthapalli K. The hackers holding hospitals to ransom. BMJ. 2017;357:j 2214.Google Scholar
  89. 89.
    Moss TJ, Calland JF, Enfield KB, Gomez-Manjarres DC, Ruminski C, DiMarco JP, et al. New-onset atrial fibrillation in the critically ill. Crit Care Med. 2017;45(5):790.PubMedPubMedCentralGoogle Scholar
  90. 90.
    Chockley K, Emanuel E. The end of radiology? three threats to the future practice of radiology. J Am Coll Radiol. 2016;13(12):1415–20.PubMedGoogle Scholar
  91. 91.
    Obermeyer Z, Emanuel EJ. Predicting the future – big data, machine learning, and clinical medicine. N Engl J Med. 2016;375(13):1216–9.PubMedPubMedCentralGoogle Scholar

Copyright information

© Humana Press 2019

Authors and Affiliations

  • Marwen Eid
    • 1
  • James V. Spearman
    • 1
  • Marly van Assen
    • 1
  • Domenico De Santis
    • 2
    • 3
  • Pooyan Sahbaee
    • 4
  • Scott P. Landreth
    • 1
  • Brian Jacobs
    • 1
  • Carlo N. De Cecco
    • 1
    • 5
  1. 1.Department of Radiology and Radiological ScienceMedical University of South CarolinaCharlestonUSA
  2. 2.Department of Radiological Sciences, Oncological and Pathological SciencesUniversity of Rome “Sapienza”LatinaItaly
  3. 3.Division of Cardiovascular Imaging, Department of Radiology and Radiological ScienceMedical University of South CarolinaCharlestonUSA
  4. 4.Siemens Medical Solutions USA, Inc.MalvernUSA
  5. 5.Department of Radiology and Imaging SciencesEmory UniversityAtlantaUSA

Personalised recommendations