전산화단층촬영의 발전 및 전망

  • 이승완 (건양대학교 방사선학과)
  • 발행 : 2017.03.25

초록

키워드

참고문헌

  1. 건강보험심사평가원, 국내 첨단의료진단기기 보유현황, 2014
  2. Wikipedia, https://en.wikipedia.org/wiki/CT_scan
  3. J. M. Norris, et al., Comparison of 640-slices multidetector computed tomography versus 32-slices MDCT for imaging of the osteo-odonto-keratoprosthesis lamina, Cornea 34, 888, 2015 https://doi.org/10.1097/ICO.0000000000000404
  4. G. L. Raff, et al., Diagnosis accuracy of noninvasive coronary angiography using 64-slice spiral computed tomography, J. Am. Coll. Cardiol. 46, 552, 2005 https://doi.org/10.1016/j.jacc.2005.05.056
  5. J. F. Bruzzi, et al., Detection of Richter's transformation of chronic lymphocytic leukemia by PET/CT, J. Nucl. Med. 47, 1267, 2006
  6. R. S. Jung et al., Hybrid SPECT/CT as a diagnostic modality in suspected urinoma with ambiguous planar Tc99m EC renal scintigraphy, Indian J. Nucl. Med. 28, 254, 2013 https://doi.org/10.4103/0972-3919.121986
  7. A. K. Buck, et al., SPECT/CT*, J. Nucl. Med. 49, 1305, 2008 https://doi.org/10.2967/jnumed.107.050195
  8. M. D. Chuong, et al., Adjuvant chemoradiation for pancreatic cancer: what does the evidence tell us?, J. Gastrointest. Oncol. 5, 166, 2014
  9. 강세식 외, 방사선치료학(청구문화사), 2014
  10. B. Schaffner, et al., The precision of proton range calculations in proton radiotherapy treatment planning: experimental verification of the relation between CT-HU and proton stopping power, Phys. Med. Biol. 43, 1579, 1998 https://doi.org/10.1088/0031-9155/43/6/016
  11. J. M. Verburg, et al., Proton range verification through prompt gamma-ray spectroscopy, Phys. Med. Biol. 59, 7089, 2014 https://doi.org/10.1088/0031-9155/59/23/7089
  12. Varian Medical System, https://www.varian.com/
  13. W. Lu, et al., Deformable registration for the planning image (kVCT) and the daily images (MVCT) for adaptive radiation therapy, Phys. Med. Biol. 51, 4357, 2006 https://doi.org/10.1088/0031-9155/51/17/015
  14. C. K. Glide-Hurst, et al., Improving radiotherapy planning, delivery accuracy, and normal tissue sparing using cutting edge technologies, J. Thorac. Dis. 6, 303, 2014
  15. M. J. Willemink, et al., Iterative reconstruction techniques for computed tomography Part 1: Technical principle, Eur. Radiol. 23, 1623, 2013 https://doi.org/10.1007/s00330-012-2765-y
  16. J. M. Hoxworth, et al., Radiation dose reduction in paranasal sinus CT using model-based iterative reconstruction, AJNR 35, 644, 2014 https://doi.org/10.3174/ajnr.A3749
  17. K. Bredies, et al., The Agile library for biomedical image reconstruction using GPU acceleration, Computing in Science & Engineering 15, 34, 2013
  18. J. G. Fletcher, et al., Dual-energy and dual-source CT: Is there a role in the abdomen and pelvis?, Radiol. Clin. N. Am. 47, 41, 2009 https://doi.org/10.1016/j.rcl.2008.10.003
  19. S. M. Ko, et al., 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, 26, 2011 https://doi.org/10.1007/s00330-010-1897-1
  20. T. R. C. Johnson, et al., Material differentiation by dual energy CT: initial experience, Eur. Radiol. 17, 1510, 2007 https://doi.org/10.1007/s00330-006-0517-6
  21. J. P. Schlomka, et al., Experimental feasibility of multi-energy photon-counting K-edge imaging in pre-clinical computed tomography, Phys. Med. Biol. 53, 4031, 2008 https://doi.org/10.1088/0031-9155/53/15/002