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Innovations in Cardiac CTA

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Coronary Artery CTA

Abstract

Rapid technological advancements have enabled cardiac computed tomography angiography (cardiac CTA) to become the noninvasive modality of choice for the rule-out of coronary artery disease (CAD). Advances have been driven by progress in CT hardware technology, image reconstruction, and post-processing software. Diagnostic performance of cardiac CTA has been improved by the faster gantry rotational times and the corresponding improvement in temporal resolution. Another crucial contribution has been the development of large coverage detectors with more rows added, enabling to go from spiral to prospective axial acquisitions and reduce the dose exposure by as much as 80%. The development of iterative and other advanced reconstruction methods have facilitated this radiation dose reduction further in combination with improvements in low contrast and spatial resolution, making it also possible for functional information, such as myocardial perfusion and flow information to be extracted as well from cardiac CTA. Novel approaches have been developed to address functional motion analysis and motion artifact reduction for improved anatomic analysis. Cardiac dual-energy CT is among the latest developments which might enable further improvements in the diagnostic performance and robustness of this application, and give CT the potential to become the prime imaging modality in the field of cardiovascular medicine.

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References

  1. Sun Z. Coronary CT angiography with prospective ECG-triggering: an effective alternative to invasive coronary angiography. Cardiovasc Diagn Ther. 2012;2(1):28–37. https://doi.org/10.3978/j.issn.2223-3652.2012.02.04.

    PubMed  PubMed Central  Google Scholar 

  2. Mehta D, Thomson R, Morton T, Dhanantwari A, Shefer E. Iterative model reconstruction: simultaneously lowered computed tomography radiation dose and improved image quality. Med Phys Int. 2013;1:147–55.

    Google Scholar 

  3. Isola A, Ziegler A, Köhler T, Niessen W, Grass M. Motion-compensated iterative cone-beam CT image reconstruction with adapted blobs as basis functions. Phys Med Biol. 2008;53:6777–97.

    Article  CAS  PubMed  Google Scholar 

  4. Isola A, Grass M, Niessen W. Fully automatic nonrigid registration-based local motion estimation for motion-corrected iterative cardiac CT reconstruction. Med Phys. 2010;37(3):1093–109.

    Article  PubMed  Google Scholar 

  5. Isola A, Ziegler A, Schäfer D, Köhler T, Niessen W, Grass M. Motion compensated iterative reconstruction of a region of interest in cardiac cone-beam CT. Comput Med Imaging Graph. 2010;34:149–59.

    Article  CAS  PubMed  Google Scholar 

  6. Garcia MJ, Lessick J, Hoffmann MH. Accuracy of 16-row multidetector computed tomography for the assessment of coronary artery stenosis. JAMA. 2006;296:403–11.

    Article  CAS  PubMed  Google Scholar 

  7. 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.

    Article  PubMed  Google Scholar 

  8. Miller JM, Rochitte CE, Dewey M, Arbab-Zadeh A, Niinuma H, Gottlieb I, et al. Diagnostic performance of coronary angiography by 64-row CT. N Engl J Med. 2008;359(22):2324–36.

    Article  CAS  PubMed  Google Scholar 

  9. Meijboom WB, Van Mieghem CA, van Pelt N, Weustink A, Pugliese F, Mollet NR, et al. Comprehensive assessment of coronary artery stenoses: computed tomography coronary angiography versus conventional coronary angiography and correlation with fractional flow reserve in patients with stable angina. J Am Coll Cardiol. 2008;52(8):636–43.

    Article  PubMed  Google Scholar 

  10. Kak A, Slaney M. Principles of computerized tomographic imaging. New York: IEEE; 1988.

    Google Scholar 

  11. Kalender 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.

    Article  CAS  PubMed  Google Scholar 

  12. Crawford CR, King KF. Computed tomography scanning with simultaneous patient translation. Med Phys. 1990;17:967–82.

    Article  CAS  PubMed  Google Scholar 

  13. Ohnesorge B, Flohr T, Becker C, Kopp AF, Schoeph UJ, Baum U, et al. Cardiac imaging by means of electrocardiographically gated multi-section spiral CT: initial experience. Radiology. 2001;217:564–71.

    Article  Google Scholar 

  14. Feldkamp L, Davis L, Kress J. Practical cone-beam algorithm. J Opt Soc Am A. 1984;1(6):612–9.

    Article  Google Scholar 

  15. Grass M, Köhler T, Proksa R. 3D cone-beam CT reconstruction for circular trajectories. Phys Med Biol. 2000;45:329–47.

    Article  CAS  PubMed  Google Scholar 

  16. Proksa R, Koehler T, Grass M, Timmer J. The n-PI method for helical cone beam CT. IEEE Trans Med Imaging. 2001;19:848–63.

    Google Scholar 

  17. Koehler T, Bontus C, Brown K, Heuscher D, Grass M, Shechter G, et al. Evaluation of helical cone beam CT reconstruction algorithms. IEEE Nucl Sci Symp Conf Rec. 2002;2:1217–20.

    Google Scholar 

  18. Grass M, Manzke R, Nielsen T, Koken P, Proksa R, Natanzon M, et al. Helical cardiac cone beam reconstruction using retrospective ECG gating. Phys Med Biol. 2003;48:3069–84.

    Article  CAS  PubMed  Google Scholar 

  19. Shechter G, Naveh G, Altman A, Proksa R, Grass M. Cardiac image reconstruction on a 16-slice CT scanner using a retrospectively ECG-gated, multi-cycle 3D back projection algorithm. Proc SPIE Med Imaging. 2003;5032:1820–8.

    Article  Google Scholar 

  20. Muenzel D, Noël PB, Dorn F, Dobritz M, Rummeny EJ, Huber A. Coronary CT angiography in step-and-shoot technique with 256-slice CT: impact of the field of view on image quality, craniocaudal coverage, and radiation exposure. Eur J Radiol. 2012;81(7):1562–8.

    Article  PubMed  Google Scholar 

  21. Vlassenbroek A. The use of isotropic imaging and computed tomography reconstructions. In: Coche EE, Ghaye B, de Mey J, Duyck P, editors. Comparative interpretation of CT and standard radiography of the chest, Medical radiology. Berlin/Heidelberg: Springer; 2011a. https://doi.org/10.1007/978-3-540-79942-9_3.

  22. Flohr TG, McCollough CH, Bruder H, Petersilka M, Gruber K, Süss C, et al. First performance evaluation of a dual-source CT (DSCT) system. Eur Radiol. 2006;16:256–68.

    Article  PubMed  Google Scholar 

  23. Achenbach S, Ropers D, Kuettner A, Flohr T, Ohnesorge B, Bruder H, et al. Contrast-enhanced coronary artery visualization by dual-source computed tomography—initial experience. Eur J Radiol. 2006;57:331–5.

    Article  PubMed  Google Scholar 

  24. McCollough CH, Schmidt B, Yu L, Primak A, Ulzheimer S, Bruder H, et al. Measurement of temporal resolution in dual source CT. Med Phys. 2008;35(2).

    Google Scholar 

  25. Potel MJ, Rubin JM, MacKay SA, Aisen AM, Al-Sadir J, Sayre RE. Methods for evaluating cardiac wall motion in three dimensions using bifurcation points of the coronary arterial tree. Investig Radiol. 1983;18:47–57.

    Article  CAS  Google Scholar 

  26. Wang Y, Vidan E, Bergman GW. Cardiac motion of coronary arteries: variability in the rest period and implications for coronary MR angiography. Radiology. 1999;213:751–8.

    Article  CAS  PubMed  Google Scholar 

  27. Achenbach S, Ropers D, Holle J, Muschiol G, Daniel WG, Moshage W. In-plane coronary arterial motion velocity: measurement with electron-beam CT. Radiology. 2000;216:457–63.

    Article  CAS  PubMed  Google Scholar 

  28. Vembar M, Garcia MJ, Heuscher DJ, Haberl R, Matthews D, Boehme GE, et al. A dynamic approach to identifying desired physiological phases for cardiac imaging using multislice spiral CT. Med Phys. 2003;30:1683–93.

    Article  CAS  PubMed  Google Scholar 

  29. Vembar M, Walker MJ, Johnson PC. Cardiac imaging using multislice computed tomography scanners: technical considerations. Coron Artery Dis. 2006;17:115–23.

    Article  PubMed  Google Scholar 

  30. Gurudevan SV. Postprocessing and reconstruction techniques for the coronary arteries. In:Cardiac CT imaging: diagnosis of cardiovascular disease. London: Springer; 2010. https://doi.org/10.1007/978-1-84882-650-2.

    Google Scholar 

  31. Chandra S, Heuscher DJ, Vembar M, Shreter U, Garcia M. Algorithm for acquiring/reconstructing any phase of the heart cycle in multi-slice cardiac CT. First Annual Cardiac CT Conference; 2000 Sept; Heidelberg.

    Google Scholar 

  32. Heuscher DJ, Chandra S. Multi-phase cardiac imager. United States Patent 6,510,337. 2003.

    Google Scholar 

  33. Manzke R, Köhler T, Nielsen T, Hawkes D, Grass M. Automatic phase determination for retrospectively gated cardiac CT. Med Phys. 2004a;31(12):3345–62.

    Article  CAS  PubMed  Google Scholar 

  34. Hoffmann MH, Lessick J, Manzke R, Schmid FT, Gershin E, Boll DT, et al. Automatic determination of minimal cardiac motion phases for computed tomography imaging: initial experience. Eur Radiol. 2006;16:365–73.

    Article  PubMed  Google Scholar 

  35. Stanford W, Rumberger J. Ultrafast computed tomography in cardiac imaging: principles and practice. New York: Futura; 1992.

    Google Scholar 

  36. Halpern EJ. Technique, protocols, instrumentation, and radiation dose. In:Clinical cardiac CT, anatomy and function. 2nd ed. Stuttgart, Germany: Thieme Medical Publishers; 2011.

    Google Scholar 

  37. Dewey M, Laule M, Krug L, Schnapauff D, Rogalla P, Rutsch W, et al. Multisegment and halfscan reconstruction of 16-slice computed tomography for detection of coronary artery stenosis. Investig Radiol. 2004;39:223–9.

    Article  Google Scholar 

  38. Manzke R, Grass M, Nielsen T, Shechter G, Hawkes D. Adaptive temporal resolution optimization in helical cardiac cone beam CT reconstruction. Med Phys. 2003;30:3072–80.

    Article  CAS  PubMed  Google Scholar 

  39. Hoffmann MH, Heshui S, Manzke R, Schmid FT, De Vries L, Grass M, et al. Noninvasive coronary angiography with 16-detector row CT: effect of heart rate. Radiology. 2005;234:86–97.

    Article  PubMed  Google Scholar 

  40. van Stevendaal U, Koken P, Begemann PG, Koester R, Adam G, Grass M. ECG gated continuous circular cone-beam multi-cycle reconstruction for in-stent coronary artery imaging: a phantom study. Proc SPIE. 2006;6142:61420L. https://doi.org/10.1117/12.652011.

    Article  Google Scholar 

  41. Klass O, Jeltsch M, Feuerlein S, Brunner H, Nagel H-D, Walker MJ, et al. Prospectively gated axial CT coronary angiography: preliminary experiences with a novel low-dose technique. Eur Radiol. 2009;19(4):829–36. https://doi.org/10.1007/s00330-008-1222-4. Epub 2008 Nov 15.

    Article  PubMed  Google Scholar 

  42. Whiting BR, Massoumzadeh P, Earl OA, O’Sullivan JA, Snyder DL, Williamson JF. Properties of preprocessed sinogram data in X-ray computed tomography. Med Phys. 2006;33(9):3290–303.

    Article  PubMed  Google Scholar 

  43. Brown K, Zabic S, Koehler T. Acceleration of ML iterative algorithms for CT by the use of fast start images. Proc. SPIE. 2012;8313:831339. https://doi.org/10.1117/12.911412.

    Article  Google Scholar 

  44. Oda S, Weismann G, Vembar M, Weigold WG. Iterative model reconstruction: improved image quality of low-tube-voltage prospective ECG-gated coronary CT angiographyimages at 256-slice CT. Eur J Radiol. 2014. https://doi.org/10.1016/j.ejrad.2014.04.027.

  45. Higgins WE, Chung N, Ritman EL. Extraction of left-ventricular chamber from 3-D CT images of the heart. IEEE Trans. Med. Imaging. 1990;9(4):384–94.

    Article  CAS  PubMed  Google Scholar 

  46. Redwood AB, Camp JJ, Robb RA. Semiautomatic segmentation of the heart from CT images based on intensity and morphological features. Proc SPIE Med Imaging. 2005;5747:1713–9.

    Article  Google Scholar 

  47. Ecabert O, Peters J, Schramm H, Lorenz C, von Berg J, Walker M, et al. Automatic model-based segmentation of the heart in CT images. IEEE Trans Med Imaging. 2008;27(9):1189–201.

    Article  PubMed  Google Scholar 

  48. Ecabert O, Peters J, Walker M, Ivanc T, Lorenz C, von Berg J, et al. Segmentation of the heart and great vessels in CT images using a model-based adaptation framework. Med Image Anal. 2011;15:863–76.

    Article  PubMed  Google Scholar 

  49. Rhode KS, Sermesant M, Brogan D, Hegde S, Hipwell J, Lambiase P, et al. A system for real-time XMR guided cardiovascular intervention. IEEE Trans Med Imaging. 2005;24:1428–40.

    Article  PubMed  Google Scholar 

  50. Knecht S, Skali H, O’Neill MD, Wright M, Matsuo S, Chaudhry GM, et al. Computed tomography-fluoroscopy overlay evaluation during catheter ablation of left atrial arrhythmia. Europace. 2008;10:931–8.

    Article  PubMed  Google Scholar 

  51. Gutiérrez LF, de Silva R, Ozturk C, Sonmez M, Stine AM, Raval AN, et al. Technology preview: X-ray fused with magnetic resonance during invasive cardiovascular procedures. Catheter Cardiovasc Interv. 2007;70:773–82.

    Article  PubMed  PubMed Central  Google Scholar 

  52. Lehmann H, Kneser R, Neizel M, Peters J, Ecabert O, Kühl H, et al. Integrating viability information into a cardiac model for interventional guidance. In:Functional imaging and modeling of the heart, FIMH 2009. LNCS, vol. 5528. Berlin/Heidelberg: Springer; 2009. p. 312–20.

    Chapter  Google Scholar 

  53. Hachamovitch R, Berman DS, Shaw LJ, Kiat H, Cohen I, Cabico JA, et al. Incremental prognostic value of myocardial perfusion single photon emission computed tomography for the prediction of cardiac death: differential stratification for risk of cardiac death and myocardial infarction. Circulation. 1998;97(6):535–43.

    Article  CAS  PubMed  Google Scholar 

  54. Gerber BL, Belge B, Legros GJ, Lim P, Poncelet A, Pasquet A, et al. Characterization of acute and chronic myocardial infarcts by multidetector computed tomography: comparison with contrast-enhanced magnetic resonance. Circulation. 2006;113(6):823–33.

    Article  PubMed  Google Scholar 

  55. Gerber BL, Rochitte CE, Melin JA, McVeigh ER, Bluemke DA, Wu KC, et al. Microvascular obstruction and left ventricular remodeling early after acute myocardial infarction. Circulation. 2000;101:2734–41.

    Article  CAS  PubMed  Google Scholar 

  56. Gerber BL, Garot J, Bluemke DA, Wu KC, Lima JA. Accuracy of contrast-enhanced magnetic resonance imaging in predicting improvement of regional myocardial function in patients after acute myocardial infarction. Circulation. 2002;106:1083–9.

    Article  PubMed  Google Scholar 

  57. Blankstein R, Okada DR, Rocha-Filho JA, Rybicki FJ, Brady TJ, Cury RC. Cardiac myocardial perfusion imaging using dual-source computed tomography. Int J Cardiovasc Imaging. 2009. https://doi.org/10.1007/s10554-009-9438-1.

  58. Blankstein R, Shturman LD, Rogers IS, Rocha-Filho JA, Okada DR, Sarwar A, et al. Adenosine-induced stress myocardial perfusion imaging using dual-source cardiac computed tomography. J Am Coll Cardiol. 2009;54(12):1072–84. https://doi.org/10.1016/j.jacc.2009.06.014.

    Article  PubMed  Google Scholar 

  59. George RT, Arbab-Zadeh A, Miller JM, Kitagawa K, Chang HJ, Bluemke DA, et al. 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. 2009;2:174–82. https://doi.org/10.1161/circimaging.108.813766.

    Article  PubMed  PubMed Central  Google Scholar 

  60. Lessick J, Dragu R, Mutlak D, Rispler S, Beyar R, Litmanovich D, et al. Is functional improvement after myocardial infarction predicted with myocardial enhancement patterns at multidetector CT? Radiology. 2007;244(3):736–44. https://doi.org/10.1148/radiol.2443061397. Epub 2007 Aug 9.

    Article  PubMed  Google Scholar 

  61. Mahnken AH, Klotz E, Pietsch H, Schmidt B, Allmendinger T, Haberland U, et al. Quantitative whole heart stress perfusion CT imaging as noninvasive assessment of hemodynamics in coronary artery stenosis: preliminary animal experience. Investig Radiol. 2010;45(6):298–305. https://doi.org/10.1097/RLI.0b013e3181dfa3cf.

    Google Scholar 

  62. Bamberg F, Klotz E, Flohr T, Becker A, Becker CR, Schmidt B, et al. Dynamic myocardial stress perfusion imaging using fast dual-source CT with alternating table positions: initial experience. Eur Radiol. 2010;20(5):1168–73. https://doi.org/10.1007/s00330-010-1715-9. Epub 2010 Mar 24.

    Article  PubMed  Google Scholar 

  63. Bamberg F, Becker A, Schwarz F, Marcus RP, Greif M, von Ziegler F, et al. Detection of hemodynamically significant coronary artery stenosis: incremental diagnostic value of dynamic CT-based myocardial perfusion imaging. Radiology. 2011;260(3):689–98. https://doi.org/10.1148/radiol.11110638.

    Article  PubMed  Google Scholar 

  64. Gramer BM, Muenzel D, Leber V, von Thaden AK, Feussner H, Schneider A, et al. Impact of iterative reconstruction on CNR and SNR in dynamic myocardial perfusion imaging in an animal model. Eur Radiol. 2012;22(12):2654–61. https://doi.org/10.1007/s00330-012-2525-z. Epub 2012 Jul 3.

    Article  CAS  PubMed  Google Scholar 

  65. Kurata A, Kawaguchi N, Kido T, Inoue K, Suzuki J, Ogimoto A, et al. Qualitative and quantitative assessment of adenosine triphosphate stress whole-heart dynamic myocardial perfusion imaging using 256-slice computed tomography. PLoS One. 2013;8(12):e83950. https://doi.org/10.1371/journal.pone.0083950. eCollection 2013.

    Article  PubMed  PubMed Central  Google Scholar 

  66. Huber AM, Leber V, Gramer BM, Muenzel D, Leber A, Rieber J, et al. Myocardium: dynamic versus single-shot CT perfusion imaging. Radiology. 2013;269(2):378–86. https://doi.org/10.1148/radiol.13121441. Epub 2013 Jun 20.

    Article  PubMed  Google Scholar 

  67. Muenzel D, Kabus S, Gramer B, Leber V, Vembar M, Schmitt H, et al. Dynamic CT perfusion imaging of the myocardium: a technical note on improvement of image quality. PLoS One. 2013;8(10):e75263. https://doi.org/10.1371/journal.pone.0075263. eCollection 2013.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  68. Muenzel D, Noël PB, Gramer BM, Leber V, Schneider A, Leber A, et al. Dynamic CT perfusion imaging of the myocardium using a wide-detector scanner: a semiquantitative analysis in an animal model. Clin Imaging. 2014;38(5):675–80. https://doi.org/10.1016/j.clinimag.2014.05.011. Epub 2014 Jun 2.

    Article  PubMed  Google Scholar 

  69. Isola AA, Schmitt H, van Stevendaal U, Begemann PG, Coulon P, Boussel L, et al. Image registration and analysis for quantitative myocardial perfusion: application to dynamic circular cardiac CT. Phys Med Biol. 2011;56(18):5925–47. https://doi.org/10.1088/0031-9155/56/18/010. Epub 2011 Aug 22.

    Article  CAS  PubMed  Google Scholar 

  70. Eck B, Fahmi R, Wen G, Fuqua C, Vembar M, Dhanantwari A, et al. Low dose dynamic myocardial CT perfusion using advanced iterative reconstruction. Proc. SPIE. 2014;9417:94170Z. https://doi.org/10.1117/12.2081418.

    Google Scholar 

  71. Fahmi R, Eck BL, Vembar M, Bezerra HG, Wilson DL. Dose reduction assessment in dynamic CT myocardial perfusion imaging in a porcine balloon-induced-ischemia model. Proc. SPIE. 2014;9033:903305. https://doi.org/10.1117/12.2043748.

    Article  Google Scholar 

  72. Koo BK, Erglis A, Doh JH, Daniels DV, Jegere S, Kim HS, et al. Diagnosis of ischemia-causing coronary stenoses by noninvasive fractional flow reserve computed from coronary computed tomographic angiograms. Results from the prospective multicenter DISCOVER-FLOW (Diagnosis of Ischemia-Causing Stenoses Obtained Via Noninvasive Fractional Flow Reserve) study. J Am Coll Cardiol. 2011;58(19):1989–97. https://doi.org/10.1016/j.jacc.2011.06.066.

    Article  PubMed  Google Scholar 

  73. Min JK, Leipsic J, Pencina MJ, Berman DS, Koo BK, van Mieghem C, et al. Diagnostic accuracy of fractional flow reserve from anatomic CT angiography. JAMA. 2012;308(12):1237–45.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  74. Nørgaard BL, Leipsic J, Gaur S, Seneviratne S, Ko BS, Ito H, et al. NXT Trial Study Group. Diagnostic performance of noninvasive fractional flow reserve derived from coronary computed tomography angiography in suspected coronary artery disease: the NXT trial (Analysis of Coronary Blood Flow Using CT Angiography: Next Steps). J Am Coll Cardiol. 2014;63(12):1145–55. https://doi.org/10.1016/j.jacc.2013.11.043. Epub 2014 Jan 30.

    Article  PubMed  Google Scholar 

  75. Manzke R, Grass M, Hawkes D. Artifact analysis and reconstruction improvement in helical cardiac cone beam CT. IEEE Trans. Med. Imaging. 2004b;23(9):1150–64.

    Article  PubMed  Google Scholar 

  76. von Berg J, Barschdorf H, Blaffert T, Kabus S, Lorenz C. Surface based cardiac and respiratory motion extraction for pulmonary structures from multi-phase CT. Proc SPIE Med Imaging Conf. 2007;6511:65110Y-1–65110Y-11.

    Google Scholar 

  77. Peters J, Ecabert O, Schmitt H, Grass M, Weese J. Local cardiac wall motion estimation from retrospectively gated CT images. In: Ayache N, Delingette H, Sermesant M, editors. FIMH 2009, LNCS 5528; 2009; pp. 191–200.

    Google Scholar 

  78. Hansis E, Schomberg H, Erhard K, Dössel O, Grass M. Four-dimensional cardiac reconstruction from rotational x-ray sequences: first results for 4D coronary angiography. In: Samei E, Hsieh J, editors. Medical imaging 2009: physics of medical imaging, 72580B; 2009.

    Google Scholar 

  79. Rohkohl C, Lauritsch G, Biller L, Prümmer M, Boese J, Hornegger J. Interventional 4D motion estimation and reconstruction of cardiac vasculature without motion periodicity assumption. Med Image Anal. 2010;14:687–94.

    Article  CAS  PubMed  Google Scholar 

  80. Forthmann P, van Stevendaal U, Grass M, Köhler T. Vector field interpolation for cardiac motion compensated reconstruction. Proceeding of the IEEE NSS-MIC Conference; 2008.

    Google Scholar 

  81. van Stevendaal U, von Berg J, Lorenz M, Grass M. A motion-compensated scheme for helical cone-beam reconstruction in cardiac CT angiography. Med Phys. 2008;35(7):3239–51.

    Article  PubMed  Google Scholar 

  82. Schäfer D, Borgert J, Rasche V, Grass M. Motion-compensated and gated cone beam filtered back-projection for 3-D rotational X-ray angiography. IEEE Trans Med Imaging. 2006;25(7):898–906.

    Article  PubMed  Google Scholar 

  83. Schirra C, Bontus C, van Stevendaal U, Dössel O, Grass M. Improvement of cardiac CT reconstruction using local motion vector fields. Comput Med Imaging Graph. 2009;33:122–30.

    Article  PubMed  Google Scholar 

  84. Fornaro J, Leschka S, Hibbeln D, Butler A, Anderson N, Pache G, et al. Dual- and multi-energy CT: approach to functional imaging. Insights Imaging. 2011;2:149–59.

    Article  PubMed  PubMed Central  Google Scholar 

  85. Vlassenbroek A, Dual Layer CT. Dual energy CT in clinical practice, Medical radiology. Berlin/Heidelberg: Springer; 2011b. https://doi.org/10.1007/978-3-642-01740-7.

    Google Scholar 

  86. Maass N, Baer M and Kachelriess M. Image-based dual energy CT using optimized precorrection functions: a practical new approach of material decomposition in image domain. Med Phys. 2009;36(8).

    Google Scholar 

  87. Fahmi R, Eck BL, Fares A, Levi J, Wu H, Vembar M, et al. Dynamic myocardial perfusion in a porcine balloon-induced ischemia model using a prototype spectral detector CT. Proc. SPIE. 2015;9417:94170Y-8.

    Article  Google Scholar 

  88. Engel KJ, Herrmann C, Zeitler G. X-ray scattering in single and dual-source CT. Med Phys. 2008;35(1):318–32.

    Article  PubMed  Google Scholar 

  89. Alvarez RE, Macovski A. Energy-selective reconstructions in X-ray computerized tomography. Phys Med Biol. 1976;21(5):733–44.

    Article  CAS  PubMed  Google Scholar 

  90. Goodsitt MM, Christodoulou EG, Larson SC. Accuracies of the synthesized monochromatic CT numbers and effective atomic numbers obtained with a rapid kVp switching dual energy CT scanner. Med Phys. 2011;38:2222–32.

    Article  PubMed  Google Scholar 

  91. Yu L, Leng S, McCollough CH. Dual energy CT-based monochromatic imaging. AJR Am J Roentgenol. 2012;199(5 Suppl):S9–S15.

    Article  PubMed  Google Scholar 

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Vlassenbroek, A., Vembar, M., Grass, M. (2018). Innovations in Cardiac CTA. In: Smuclovisky, C. (eds) Coronary Artery CTA. Springer, Cham. https://doi.org/10.1007/978-3-319-66988-5_2

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