Korean Journal of Radiology

  • 제14권1호
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  • Pages.21-29
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  • 2013
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  • 1229-6929(pISSN)
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  • 2005-8330(eISSN)
대한영상의학회 (The Korean Society of Radiology)
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DOI QR코드

DOI QR Code

Time Efficiency and Diagnostic Accuracy of New Automated Myocardial Perfusion Analysis Software in 320-Row CT Cardiac Imaging

  • Rief, Matthias (Department of Radiology, Charite - Universitatsmedizin Berlin) ;
  • Stenzel, Fabian (Department of Radiology, Charite - Universitatsmedizin Berlin) ;
  • Kranz, Anisha (Department of Radiology, Charite - Universitatsmedizin Berlin) ;
  • Schlattmann, Peter (Department of Radiology, Charite - Universitatsmedizin Berlin) ;
  • Dewey, Marc (Department of Radiology, Charite - Universitatsmedizin Berlin)
  • 발행 : 2013.02.01
⟨ 이전 논문 다음 논문 ⟩

초록

Objective: We aimed to evaluate the time efficiency and diagnostic accuracy of automated myocardial computed tomography perfusion (CTP) image analysis software. Materials and Methods: 320-row CTP was performed in 30 patients, and analyses were conducted independently by three different blinded readers by the use of two recent software releases (version 4.6 and novel version 4.71GR001, Toshiba, Tokyo, Japan). Analysis times were compared, and automated epi- and endocardial contour detection was subjectively rated in five categories (excellent, good, fair, poor and very poor). As semi-quantitative perfusion parameters, myocardial attenuation and transmural perfusion ratio (TPR) were calculated for each myocardial segment and agreement was tested by using the intraclass correlation coefficient (ICC). Conventional coronary angiography served as reference standard. Results: The analysis time was significantly reduced with the novel automated software version as compared with the former release (Reader 1: 43:08 ${\pm}$ 11:39 min vs. 09:47 ${\pm}$ 04:51 min, Reader 2: 42:07 ${\pm}$ 06:44 min vs. 09:42 ${\pm}$ 02:50 min and Reader 3: 21:38 ${\pm}$ 3:44 min vs. 07:34 ${\pm}$ 02:12 min; p < 0.001 for all). Epi- and endocardial contour detection for the novel software was rated to be significantly better (p < 0.001) than with the former software. ICCs demonstrated strong agreement (${\geq}$ 0.75) for myocardial attenuation in 93% and for TPR in 82%. Diagnostic accuracy for the two software versions was not significantly different (p = 0.169) as compared with conventional coronary angiography. Conclusion: The novel automated CTP analysis software offers enhanced time efficiency with an improvement by a factor of about four, while maintaining diagnostic accuracy.

키워드

참고문헌

  1. Miller, JM, Rochitte, CE, Dewey, M, Arbab-Zadeh, A, Niinuma, H, Gottlieb, I,Diagnostic performance of coronary angiography by 64-row CT, N Engl J Med, 359, 1, 2324-2336(2008) https://doi.org/10.1056/NEJMoa0806576
  2. Lee, HJ, Kim, JS, Kim, YJ, Hur, J, Yoo, KJ, Choe, KO,Diagnostic accuracy of 64-slice multidetector computed tomography for selecting coronary artery bypass graft surgery candidates, J Thorac Cardiovasc Surg, 141, 2, 571-577(2011) https://doi.org/10.1016/j.jtcvs.2010.03.020
  3. Moon, JH, Park, EA, Lee, W, Yin, YH, Chung, JW, Park, JH,practice: a single institution's experience, Korean J Radiol, 12, 3, 308-318(2011) https://doi.org/10.3348/kjr.2011.12.3.308
  4. Dewey, M, Zimmermann, E, Deissenrieder, F, Laule, M, Dübel, HP, Schlattmann, P,Noninvasive coronary angiography by 320-row computed tomography with lower radiation exposure and maintained diagnostic accuracy: comparison of results with cardiac catheterization in a head-to-head pilot investigation, Circulation, 120, 4, 867-875(2009) https://doi.org/10.1161/CIRCULATIONAHA.109.859280
  5. So, A, Wisenberg, G, Islam, A, Amann, J, Romano, W, Brown, J,Non-invasive assessment of functionally relevant coronary artery stenoses with quantitative CT perfusion: preliminary clinical experiences, Eur Radiol, 22, 5, 39-50(2012) https://doi.org/10.1007/s00330-011-2260-x
  6. Bastarrika, G, Ramos-Duran, L, Schoepf, UJ, Rosenblum, MA, Abro, JA, Brothers, RL,Adenosine-stress dynamic myocardial volume perfusion imaging with second generation dual-source computed tomography: Concepts and first experiences, J Cardiovasc Comput Tomogr, 4, 6, 127-135(2010) https://doi.org/10.1016/j.jcct.2010.01.015
  7. George, RT, Arbab-Zadeh, A, Cerci, RJ, Vavere, AL, Kitagawa, K, Dewey, M,Diagnostic performance of combined noninvasive coronary angiography and myocardial perfusion imaging using 320-MDCT: the CT angiography and perfusion methods of the CORE320 multicenter multinational diagnostic study, AJR Am J Roentgenol, 197, 7, 829-837(2011) https://doi.org/10.2214/AJR.10.5689
  8. Ko, SM, Choi, JW, Song, MG, Shin, JK, Chee, HK, Chung, HW,Myocardial perfusion imaging using adenosine-induced stress dual-energy computed tomography of the heart: comparison with cardiac magnetic resonance imaging and conventional coronary angiography, Eur Radiol, 21, 8, 26-35(2011) https://doi.org/10.1007/s00330-010-1897-1
  9. Cerqueira, MD, Verani, MS, Schwaiger, M, Heo, J, Iskandrian, AS,Safety profile of adenosine stress perfusion imaging: results from the Adenoscan Multicenter Trial Registry, J Am Coll Cardiol, 23, 9, 384-389(1994) https://doi.org/10.1016/0735-1097(94)90424-3
  10. Voigtlander, T, Schmermund, A, Bramlage, P, Elsasser, A, Magedanz, A, Kauczor, HU,The adverse events and hemodynamic effects of adenosine-based cardiac MRI, Korean J Radiol, 12, 10, 424-430(2011) https://doi.org/10.3348/kjr.2011.12.4.424
  11. Ko, BS, Cameron, JD, Meredith, IT, Leung, M, Antonis, PR, Nasis, A,Computed tomography stress myocardial perfusion imaging in patients considered for revascularization: a comparison with fractional flow reserve, Eur Heart J, 33, 11, 67-77(2012) https://doi.org/10.1093/eurheartj/ehr268
  12. Choi, JH, Min, JK, Labounty, TM, Lin, FY, Mendoza, DD, Shin, DH,Intracoronary transluminal attenuation gradient in coronary CT angiography for determining coronary artery stenosis, JACC Cardiovasc Imaging, 4, 12, 1149-1157(2011) https://doi.org/10.1016/j.jcmg.2011.09.006
  13. Reimann, AJ, Tsiflikas, I, Brodoefel, H, Scheuering, M, Rinck, D, Kopp, AF,Efficacy of computer aided analysis in detection of significant coronary artery stenosis in cardiac using dual source computed tomography, Int J Cardiovasc Imaging, 25, 13, 195-203(2009) https://doi.org/10.1007/s10554-008-9372-7
  14. Dewey, M, Schnapauff, D, Laule, M, Lembcke, A, Borges, AC, Rutsch, W,Multislice CT coronary angiography: evaluation of an automatic vessel detection tool, Rofo, 176, 14, 478-483(2004) https://doi.org/10.1055/s-2004-812991
  15. Okada, DR, Ghoshhajra, BB, Blankstein, R, Rocha-Filho, JA, Shturman, LD, Rogers, IS,Direct comparison of rest and adenosine stress myocardial perfusion CT with rest and stress SPECT, J Nucl Cardiol, 17, 15, 27-37(2010) https://doi.org/10.1007/s12350-009-9156-z
  16. Tamarappoo, BK, Dey, D, Nakazato, R, Shmilovich, H, Smith, T, Cheng, VY,Comparison of the extent and severity of myocardial perfusion defects measured by CT coronary angiography and SPECT myocardial perfusion imaging, JACC Cardiovasc Imaging, 3, 16, 1010-1019(2010) https://doi.org/10.1016/j.jcmg.2010.07.011
  17. Halliburton, SS, Abbara, S, Chen, MY, Gentry, R, Mahesh, M, Raff, GL,SCCT guidelines on radiation dose and dose-optimization strategies in cardiovascular CT, J Cardiovasc Comput Tomogr, 5, 17, 198-224(2011) https://doi.org/10.1016/j.jcct.2011.06.001
  18. Hoffmann, MH, Lessick, J, Manzke, R, Schmid, FT, Gershin, E, Boll, DT,Automatic determination of minimal cardiac motion phases for computed tomography imaging: initial experience, Eur Radiol, 16, 18, 365-373(2006) https://doi.org/10.1007/s00330-005-2849-z
  19. Cootes, TF, Taylor, CJ, Cooper, DH, Graham, J,Active shape modelstheir training and application, Comput Vis Image Underst, 61, 19, 38-59(1995) https://doi.org/10.1006/cviu.1995.1004
  20. Rogers, IS, Cury, RC, Blankstein, R, Shapiro, MD, Nieman, K, Hoffmann, U,Comparison of postprocessing techniques for the detection of perfusion defects by cardiac computed tomography in patients presenting with acute ST-segment elevation myocardial infarction, J Cardiovasc Comput Tomogr, 4, 20, 258-266(2010) https://doi.org/10.1016/j.jcct.2010.04.003
  21. Valdiviezo, C, Ambrose, M, Mehra, V, Lardo, AC, Lima, JA, George, RT,Quantitative and qualitative analysis and interpretation of CT perfusion imaging, J Nucl Cardiol, 17, 21, 1091-1100(2010) https://doi.org/10.1007/s12350-010-9291-6
  22. Cerqueira, MD, Weissman, NJ, Dilsizian, V, Jacobs, AK, Kaul, S, Laskey, WK,Standardized myocardial segmentation and nomenclature for tomographic imaging of the heart. A statement for healthcare professionals from the Cardiac Imaging Committee of the Council on Clinical Cardiology of the American Heart Association, Int J Cardiovasc Imaging, 18, 22, 539-542(2002)
  23. Shrout, PE, Fleiss, JL,Intraclass correlations: uses in assessing rater reliability, Psychol Bull, 86, 23, 420-428(1979) https://doi.org/10.1037/0033-2909.86.2.420
  24. Dey, D, Schepis, T, Marwan, M, Slomka, PJ, Berman, DS, Achenbach, S,Automated three-dimensional quantification of noncalcified coronary plaque from coronary CT angiography: comparison with intravascular US, Radiology, 257, 24, 516-522(2010) https://doi.org/10.1148/radiol.10100681
  25. Dey, D, Cheng, VY, Slomka, PJ, Nakazato, R, Ramesh, A, Gurudevan, S,Automated 3-dimensional quantification of noncalcified and calcified coronary plaque from coronary CT angiography, J Cardiovasc Comput Tomogr, 3, 25, 372-382(2009) https://doi.org/10.1016/j.jcct.2009.09.004
  26. Sugiura, T, Takeguchi, T, Sakata, Y, Nitta, S, Okazaki, T, Matsumoto, N,Automatic model-based contour detection of left ventricle myocardium from cardiac CT images, Int J Comput Assist Radiol Surg, , 26, (2012)
  27. Muller, M, Teige, F, Schnapauff, D, Hamm, B, Dewey, M,Evaluation of right ventricular function with multidetector computed tomography: comparison with magnetic resonance imaging and analysis of inter- and intraobserver variability, Eur Radiol, 19, 27, 278-289(2009) https://doi.org/10.1007/s00330-008-1146-z
  28. Dewey, M, Müller, M, Eddicks, S, Schnapauff, D, Teige, F, Rutsch, W,Evaluation of global and regional left ventricular function with 16-slice computed tomography, biplane cineventriculography, and two-dimensional transthoracic echocardiography: comparison with magnetic resonance imaging, J Am Coll Cardiol, 48, 28, 2034-2044(2006) https://doi.org/10.1016/j.jacc.2006.04.104
  29. Mo, YH, Jaw, FS, Wang, YC, Jeng, CM, Peng, SF,Effects of propranolol on the left ventricular volume of normal subjects during CT coronary angiography, Korean J Radiol, 12, 29, 319-326(2011) https://doi.org/10.3348/kjr.2011.12.3.319
  30. Boehm, T, Alkadhi, H, Roffi, M, Willmann, JK, Desbiolles, LM, Marincek, B,Time-effectiveness, observer-dependence, and accuracy of measurements of left ventricular ejection fraction using 4-channel MDCT, Rofo, 176, 30, 529-537(2004) https://doi.org/10.1055/s-2004-813012
  31. Schlosser, T, Pagonidis, K, Herborn, CU, Hunold, P, Waltering, KU, Lauenstein, TC,Assessment of left ventricular parameters using 16-MDCT and new software for endocardial and epicardial border delineation, AJR Am J Roentgenol, 184, 31, 765-773(2005) https://doi.org/10.2214/ajr.184.3.01840765
  32. Plumhans, C, Keil, S, Ocklenburg, C, Mühlenbruch, G, Behrendt, FF, Günther, RW,Comparison of manual, semi- and fully automated heart segmentation for assessing global left ventricular function in multidetector computed tomography, Invest Radiol, 44, 32, 476-482(2009) https://doi.org/10.1097/RLI.0b013e3181aaf4e1
  33. Dewey, M, Müller, M, Teige, F, Hamm, B,Evaluation of a semiautomatic software tool for left ventricular function analysis with 16-slice computed tomography, Eur Radiol, 16, 33, 25-31(2006) https://doi.org/10.1007/s00330-005-2817-7
  34. Ko, SM, Kim, YW, Han, SW, Seo, JB,Early and delayed myocardial enhancement in myocardial infarction using two-phase contrast-enhanced multidetector-row CT, Korean J Radiol, 8, 34, 94-102(2007) https://doi.org/10.3348/kjr.2007.8.2.94
  35. Kachenoura, N, Veronesi, F, Lodato, JA, Corsi, C, Mehta, R, Newby, B,Volumetric quantification of myocardial perfusion using analysis of multi-detector computed tomography 3D datasets: comparison with nuclear perfusion imaging, Eur Radiol, 20, 35, 337-347(2010) https://doi.org/10.1007/s00330-009-1552-x
  36. Patel, AR, Lodato, JA, Chandra, S, Kachenoura, N, Ahmad, H, Freed, BH,Detection of myocardial perfusion abnormalities using ultra-low radiation dose regadenoson stress multidetector computed tomography, J Cardiovasc Comput Tomogr, 5, 36, 247-254(2011) https://doi.org/10.1016/j.jcct.2011.06.004
  37. George, RT, Arbab-Zadeh, A, Miller, JM, Kitagawa, K, Chang, HJ, Bluemke, DA,Adenosine stress 64- and 256-row detector computed tomography angiography and perfusion imaging: a pilot study evaluating the transmural extent of perfusion abnormalities to predict atherosclerosis causing myocardial ischemia, Circ Cardiovasc Imaging, 2, 37, 174-182(2009) https://doi.org/10.1161/CIRCIMAGING.108.813766
  38. Lubbers DD, Kuijpers D, Bodewes R, Kappert P, Kerkhof M, van Ooijen PM, et al. Inter-observer variability of visual analysis of "stress"-only adenosine first-pass myocardial perfusion imaging in relation to clinical experience and reading criteria. Int J Cardiovasc Imaging 2011,27,557-562 https://doi.org/10.1007/s10554-010-9703-3
  39. Park, JM, Choe, YH, Chang, S, Sung, YM, Kang, SS, Kim, MJ,Usefulness of multidetector-row CT in the evaluation of reperfused myocardial infarction in a rabbit model, Korean J Radiol, 5, 39, 19-24(2004) https://doi.org/10.3348/kjr.2004.5.1.19

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