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Integration of CT Data into Clinical Workflows: Role of Modern IT Infrastructure Including Cloud Technology

  • Paul SchoenhagenEmail author
  • Mathis Zimmermann
Chapter
Part of the Contemporary Medical Imaging book series (CMI)

Abstract

The ability of 3-D reconstruction has been a key element for the modern use of cardiovascular CT. Importantly, complex image reconstruction is performed by several users, including imaging specialist/radiologist and clinical interventionalist/surgeon. Imaging and image review/reconstruction are increasingly part of a stepwise decision-making process, transforming traditional single-observer reading and reporting to a process involving a team of interdisciplinary clinical specialists. This trend is observed in several subspecialties including oncology and cardiovascular medicine. These developments require a new level of data accessibility and performance of imaging systems, including ability to share data within large healthcare systems. It is supported by novel developments of IT architecture, allowing sharing of a centrally stored dataset between multiple peripheral workstations. Connection of scanners and workstations into a network or “cloud” with integration into the entire electronic health record (EHR) allows exchange of information across healthcare systems and supports multidisciplinary teams working on defined clinical workflows. These “cloud” systems are transforming clinical workflows, exemplified by the examples of acute aortic syndromes (AAS) and transcatheter aortic valve replacement (TAVR), where CT imaging has a central role. However, experience is limited, and further evaluation of the appropriate infrastructure including requirement for reliable patient identification between provider organizations and data safety is critical. Eventually the potential clinical impact needs to be evaluated in clinical trials.

Keywords

3-D reconstruction in cardiovascular CT IT infrastructure in cardiovascular CT Cardiovascular CT and IT structure and cloud technology Cloud technology in cardiovascular CT Computer-aided detection (CAD) systems in diagnostic imaging 

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References

  1. 1.
    Ambrose J, Hounsfield G. Computerized transverse axial tomography. Br J Radiol. 1973;46:148–9.CrossRefGoogle Scholar
  2. 2.
    Hounsfield GN. Computed medical imaging. Science. 1980;210:22–8.CrossRefGoogle Scholar
  3. 3.
    Mettler FA Jr, Bhargavan M, Faulkner K, Gilley DB, Gray JE, Ibbott GS, Lipoti JA, Mahesh M, McCrohan JL, Stabin MG, Thomadsen BR, Yoshizumi TT. Radiologic and nuclear medicine studies in the United States and worldwide: frequency, radiation dose, and comparison with other radiation sources – 1950-2007. Radiology. 2009;253:520–31.CrossRefGoogle Scholar
  4. 4.
    Brenner DJ, Hall EJ. Computed tomography – an increasing source of radiation exposure. N Engl J Med. 2007;357:2277–84.CrossRefGoogle Scholar
  5. 5.
    Kalendar WA, Seissler W, Klotz E, Vock P. Spiral volumetric CT with single-breath-hold technique, continuous transport, and continuous scanner rotation. Radiology. 1990;176:181–3.CrossRefGoogle Scholar
  6. 6.
    Klingenbeck-Regn K, Schaller S, Flohr T, Ohnesorge B, Kopp A, Baum U. Subsecond multi-slice computed tomography: basics and applications. Eur J Radiol. 1999;31:110–24.CrossRefGoogle Scholar
  7. 7.
    Nieman K, Cademartiri F, Lemos PA, Raaijmakers R, Pattynama PMT, de Feyter PJ. Reliable noninvasive coronary angiography with fast submillimeter multislice spiral computed tomography. Circulation. 2002;106:2051–4.CrossRefGoogle Scholar
  8. 8.
    Chao SP, Law WY, Kuo CJ, Hung HF, Cheng JJ, Lo HM, Shyu KG. The diagnostic accuracy of 256-row computed tomographic angiography compared with invasive coronary angiography in patients with suspected coronary artery disease. Eur Heart J. 2010;31:1916–23.CrossRefGoogle Scholar
  9. 9.
    Einstein AJ, Elliston CD, Arai AE, Chen MY, Mather R, Pearson GD, Delapaz RL, Nickoloff E, Dutta A, Brenner DJ. Radiation dose from single-heartbeat coronary CT angiography performed with a 320-detector row volume scanner. Radiology. 2010;254:698–706.CrossRefGoogle Scholar
  10. 10.
    George RT, Arbab-Zadeh A, Miller JM, Vavere AL, Bengel FM, Lardo AC, Lima JA. Computed tomography myocardial perfusion imaging with 320-row detector computed tomography accurately detects myocardial ischemia in patients with obstructive coronary artery disease. Circ Cardiovasc Imaging. 2012;5:333–40.CrossRefGoogle Scholar
  11. 11.
    Flohr TG, CH MC, Bruder H, Petersilka M, Gruber K, Suss C, Grasruck M, Stierstorfer K, Krauss B, Raupach R, Primak AN, Kuttner A, Achenbach S, Becker C, Kopp A, Ohnesorge BM. First performance evaluation of a dual-source CT (DSCT) system. Eur Radiol. 2006;16:256–68.CrossRefGoogle Scholar
  12. 12.
    Achenbach S, Ropers D, Kuettner A, Flohr T, Ohnesorge B, Bruder H, Theessen H, Karakaya M, Daniel WG, Bautz W, Kalender WA, Anders K. Contrast-enhanced coronary artery visualization by dual-source computed tomography – initial experience. Eur J Radiol. 2006;57:331–5.CrossRefGoogle Scholar
  13. 13.
    Schoenhagen P, Zimmermann M, Falkner J. Advanced 3-D analysis, client-server systems, and cloud computing-Integration of cardiovascular imaging data into clinical workflows of transcatheter aortic valve replacement. Cardiovasc Diagn Ther. 2013;3:80–92.PubMedPubMedCentralGoogle Scholar
  14. 14.
    Schoenhagen P, Falkner J, Piraino D. Transcatheter aortic valve repair, imaging, and electronic imaging health record. Curr Cardiol Rep. 2013;15:319.CrossRefGoogle Scholar
  15. 15.
    Liang M, Tang W, Xu DM, Jirapatnakul AC, Reeves AP, Henschke CI, Yankelevitz D. Low-dose CT screening for lung cancer: computer-aided detection of missed lung cancers. Radiology. 2016;281:279–88.CrossRefGoogle Scholar
  16. 16.
    Waljee AK, Higgins PDR. Machine learning in medicine: a primer for physicians. Am J Gastroenterol. 2010;105:1224–6.CrossRefGoogle Scholar
  17. 17.
    Deo RC. Machine learning in medicine. Circulation. 2015;132:1920–30.CrossRefGoogle Scholar
  18. 18.
    Dietterich TG. Ensemble methods in machine learning. Lect Notes Comput Sci. 2000;1857:1–15.CrossRefGoogle Scholar
  19. 19.
    Waljee AK, Joyce JC, Wang S, Saxena A, Hart M, Zhu J, Higgins PD. Algorithms outperform metabolite tests in predicting response of patients with inflammatory bowel disease to thiopurines. Clin Gastroenterol Hepatol. 2010;8:143–50.CrossRefGoogle Scholar
  20. 20.
    Singal AG, Mukherjee A, Elmunzer BJ, Higgins PD, Lok AS, Zhu J, Marrero JA, Waljee AK. Machine learning algorithms outperform conventional regression models in identifying risk factors for hepatocellular carcinoma in patients with cirrhosis. Am J Gastroenterol. 2013;108:1723–30.CrossRefGoogle Scholar
  21. 21.
    Motwani M, Dey D, Berman DS, Germano G, Achenbach S, Al-Mallah M. Machine learning for prediction of all-cause mortality in patient with suspected coronary artery disease: a 5-year multicenter prospective registry analysis. Eur Heart J. 2017;38(7):500–7.PubMedGoogle Scholar
  22. 22.
    Drowning in big data? Reducing information technology complexities and costs for healthcare organizations. http://www.emc.com/collateral/analyst-reports/frost-sullivan-reducing-information-technology-complexities-ar.pdf.
  23. 23.
    Boyd DR, Dunea MM, Flashner BA. The Illinois plan for a statewide system of trauma centers. J Trauma. 1973;13:24–31.CrossRefGoogle Scholar
  24. 24.
    Cowley RA, Hudson F, Scanlan E, Gill W, Lally RJ, Long W, et al. An economical and proved helicopter program for transporting the emergency critically ill and injured patient in Maryland. J Trauma. 1973;13:1029–38.CrossRefGoogle Scholar
  25. 25.
    Harris KM, Strauss CE, Duval S, Unger BT, Kroshus TJ, Inampudi S, et al. Multidisciplinary standardized care for acute aortic dissection: design and initial outcomes of a regional care model. Circ Cardiovasc Qual Outcomes. 2010;3:424–30.CrossRefGoogle Scholar
  26. 26.
    Henry TD, Sharkey SW, Burke MN, Chavez IJ, Graham KJ, Henry CR, et al. A regional system to provide timely access to percutaneous coronary intervention for ST-elevation myocardial infarction. Circulation. 2007;116:721–8.CrossRefGoogle Scholar
  27. 27.
    Jollis JG, Roettig ML, Aluko AO, Anstrom KJ, Applegate RJ, Babb JD, et al., Reperfusion of Acute Myocardial Infarction in North Carolina Emergency Departments (RACE) Investigators. Implementation of a statewide system for coronary reperfusion for ST-segment elevation myocardial infarction. JAMA. 2007;298:2371–80.Google Scholar
  28. 28.
    Aggarwal B, Raymond CE, Randhawa MS, Roselli E, Jacob J, Eagleton M, Kralovic DM, Kormos K, Holloway D, Menon V. Transfer metrics in patients with suspected acute aortic syndrome. Circ Cardiovasc Qual Outcomes. 2014;7:780–2.CrossRefGoogle Scholar
  29. 29.
    Raymond CE, Aggarwal B, Schoenhagen P, Kralovic DM, Kormos K, Holloway D, Menon V. Prevalence and factors associated with false positive suspicion of acute aortic syndrome: experience in a patient population transferred to a specialized aortic treatment center. Cardiovasc Diagn Ther. 2013;3:196–204.PubMedPubMedCentralGoogle Scholar
  30. 30.
    Aggarwal B, Raymond C, Jacob J, Kralovic D, Kormos K, Holloway D, Menon V. Transfer of patients with suspected acute aortic syndrome. Am J Cardiol. 2013;112:430–5.CrossRefGoogle Scholar
  31. 31.
    Schoenhagen P, Mehta N. Big data, smart computer systems, and doctor-patient relationship. Eur Heart J. 2017;38(7):508–10.PubMedGoogle Scholar
  32. 32.
    Cowie MR, Chronaki CE, Vardas P. E-health innovation: time for engagement with the cardiology community. Eur Heart J. 2013;34:1864–8.CrossRefGoogle Scholar
  33. 33.
    Matar R, Renapurkar R, Obuchowski N, Menon V, Piraino D, Schoenhagen P. Utility of hand-held devices in diagnosis and triage of cardiovascular emergencies. Observations during implementation of a PACS-based system in an acute aortic syndrome (AAS) network. J Cardiovasc Comput Tomogr. 2015;9:524–33.CrossRefGoogle Scholar
  34. 34.
    Leon MB, Piazza N, Nikolsky E, et al. Standardized endpoint definitions for transcatheter aortic valve implantation clinical trials: a consensus report from the valve academic research consortium. Eur Heart J. 2011;32:205–17.CrossRefGoogle Scholar
  35. 35.
    Vahanian A, Alfieri OR, Al-Attar N, et al. Transcatheter valve implantation for patients with aortic stenosis: a position statement from the European Association of Cardio-Thoracic Surgery (EACTS) and the European Society of Cardiology (ESC), in collaboration with the European Association of Percutaneous Cardiovascular Interventions (EAPCI). Eur J Cardiothorac Surg. 2008;34:1–8.CrossRefGoogle Scholar
  36. 36.
    Achenbach S, Delgado V, Hausleiter J, et al. SCCT expert consensus document on computed tomography imaging before transcatheter aortic valve implantation (TAVI)/transcatheter aortic valve replacement (TAVR). J Cardiovasc Comput Tomogr. 2012;6:366–80.CrossRefGoogle Scholar
  37. 37.
    Schoenhagen P, Hausleiter J, Achenbach S, et al. Computed tomography in the evaluation for transcatheter aortic valve implantation (TAVI). Cardiovasc Diagn Ther. 2011;1:44–56.PubMedPubMedCentralGoogle Scholar
  38. 38.
    Kurra V, Kapadia SR, Tuzcu EM, et al. Pre-procedural imaging of aortic root orientation and dimensions comparison between X-ray angiographic planar imaging and 3-dimensional multidetector row computed tomography. JACC Cardiovasc Interv. 2010;3:105–13.CrossRefGoogle Scholar
  39. 39.
    Gurvitch R, Wood DA, Leipsic J, et al. Multislice computed tomography for prediction of optimal angiographic deployment projections during transcatheter aortic valve implantation. JACC Cardiovasc Interv. 2010;3:1157–65.CrossRefGoogle Scholar
  40. 40.
    da Gama RV, Vouga L, Markowitz A, et al. Vascular access in transcatheter aortic valve implantation. Int J Cardiovasc Imaging. 2011;27:1235–43.CrossRefGoogle Scholar
  41. 41.
    Petronio AS, De Carlo M, Bedogni F, et al. Safety and efficacy of the subclavian approach for transcatheter aortic valve implantation with the CoreValve revalving system. Circ Cardiovasc Interv. 2010;3:359–66.CrossRefGoogle Scholar
  42. 42.
    Svensson LG, Dewey T, Kapadia S, et al. United States feasibility study of transcatheter insertion of a stented aortic valve by the left ventricular apex. Ann Thorac Surg. 2008;86:46–54; discussion 54–5.CrossRefGoogle Scholar
  43. 43.
    Walther T, Simon P, Dewey T, et al. Transapical minimally invasive aortic valve implantation: multicenter experience. Circulation. 2007;116:I240–5.CrossRefGoogle Scholar
  44. 44.
    Schoenhagen P, Tuzcu EM, Kapadia SR, Desai MY, Svensson LG. Three-dimensional imaging of the aortic valve and aortic root with computed tomography: new standards in an era of transcatheter valve repair/implantation. Eur Heart J. 2009;30:2079–86.CrossRefGoogle Scholar
  45. 45.
    Schoenhagen P, Numburi U, Halliburton SS, Aulbach P, von Roden M, Desai MY, Rodriguez LL, Kapadia SR, Tuzcu EM, Lytle BW. Three-dimensional imaging in the context of minimally invasive and transcatheter cardiovascular interventions using multi-detector computed tomography: from pre-operative planning to intra-operative guidance. Eur Heart J. 2010;31:2727–40.CrossRefGoogle Scholar
  46. 46.
    Cavalcante JL, Lalude OO, Schoenhagen P, Lerakis S. Cardiovascular magnetic resonance imaging for structural and valvular heart disease interventions. JACC Cardiovasc Interv. 2016;9(5):399–425.CrossRefGoogle Scholar
  47. 47.
    Natarajan N, Patel P, Bartel T, Kapadia S, Navia J, Stewart W, Tuzcu EM, Schoenhagen P. Peri-procedural imaging for transcatheter mitral valve replacement. Cardiovasc Diagn Ther. 2016;6(2):144–59.CrossRefGoogle Scholar

Copyright information

© Humana Press 2019

Authors and Affiliations

  1. 1.Imaging Institute, Cleveland Clinic, Lerner College of MedicineClevelandUSA
  2. 2.Digital Health ServicesSiemens HealthineersMalvernUSA

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