CT-Guided Interventions: Current Practice and Future Directions

  • Rajiv GuptaEmail author
  • Conor Walsh
  • Irene S. Wang
  • Marc Kachelrieß
  • Jan Kuntz
  • Sönke Bartling


Computed tomography (CT) plays an important role in interventional procedures such as biopsy, abscess drainage, tumor ablation, catheter placement, and orthopedic instrumentation. All these procedures involve precise incremental advancement of a needle or a probe. This chapter reviews the current state of the art and advanced applications of CT in interventional procedures, including the use of C-arm CT, multi-detector CT, and ultrahigh-resolution flat-panel CT. Interventional capabilities of C-arm CT, which combines the advantages of a digital flat-panel detector with the versatility of a C-arm, are described. Ultrahigh-resolution flat-panel CT, another technology-based flat-panel detector, is also described. Recent development of portable CT not only provides on-site imaging for critically ill patients; it also enables faster response to imaging requests and increased productivity of the care team. The new advancements covered by this chapter introduce robot-assisted image-guided interventions. The current CT-guided intervention only provides 3D data in a discontinuous, manipulate, and rescan fashion. A new paradigm for real-time 4D imaging, which could play an important role in intervention guidance in the near future, is described and illustrated with the help of examples.


Compute Tomography Guidance Entrance Skin Dose Stapes Prosthesis Dynamic Compute Tomography Scanning Portable Compute Tomography 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 1.
    Saint-Félix D, Trousset Y, Picard C, Ponchut C, Roméas R, Rougée A. In vivo evalution of a new system for 3Dcomputer angiography. Phys Med Biol. 1994;39(3):583–95.PubMedCrossRefGoogle Scholar
  2. 2.
    Haaga JR, Alfidi RJ. Precise biopsy localization by computer tomography. Radiology. 1976;118:603–7.PubMedGoogle Scholar
  3. 3.
    Manhire A, Charig M, Clelland C, et al. Guidelines for radiologically guided lung biopsy. Thorax. 2003;58:920–36.PubMedCrossRefGoogle Scholar
  4. 4.
    The International Early Lung Cancer Action Program I. Survival of patients with stage I lung cancer detected on CT screening. N Engl J Med. 2006;355:1763–71.CrossRefGoogle Scholar
  5. 5.
    Walsh C, Sapkota B, Kalra M, Hanumara N, Liu B, Shepard J, Gupta R. Smaller and deeper lesions increase the number of acquired scan series in CT-guided lung biopsy. J Thorac Imaging. 2011;26(3):196–203.PubMedCrossRefGoogle Scholar
  6. 6.
    Geleijns J, Wondergem J. X-ray imaging and the skin: radiation biology, patient dosimetry and observed effects. Radiat Prot Dosimetry. 2005;114:121–5.PubMedCrossRefGoogle Scholar
  7. 7.
    Valentin J. Abstract – avoidance of radiation injuries from medical interventional procedures, ICRP Publication 85. Ann ICRP. 2000;30:7–67.Google Scholar
  8. 8.
    Tsalafoutas IA, Tsapaki V, Triantopoulou C, Gorantonaki A, Papailiou J. CT-guided interventional procedures without CT fluoroscopy assistance: patient effective dose and absorbed dose considerations. Am J Roentgenol. 2007;188:1479–84.CrossRefGoogle Scholar
  9. 9.
    Heran NS, Song JK, Namba K, Smith W, Niimi Y, Berenstein A. The utility of DynaCT in neuroendovascular procedures. AJNR Am J Neuroradiol. 2006;27(2):330–2.PubMedGoogle Scholar
  10. 10.
    Roos G, et al. Multiple gain ranging readout method to extend the dynamic range of amorphous silicon flat panel imagers. Proc SPIE. 2004;5368:139–49, San Diego.CrossRefGoogle Scholar
  11. 11.
    Wiegert J, et al. Performance of standard fluoroscopy anti-scatter grids in flat detector based cone beam CT. Proc SPIE. 2004;5368:67–78.CrossRefGoogle Scholar
  12. 12.
    Popescu S, et al. Design and evaluation of a prototype volume CT scanner. Submitted to proceedings of SPIE. San Diego; 2005.Google Scholar
  13. 13.
    Gupta R, Bartling S, Basu S, Ross WR, Pfoh A, Becker HJ, Brady T, Curtin HG. Experimental flat-panel high-spatial-resolution volume CT of the temporal bone. AJNR Am J Neuroradiol. 2004;25:1417–24.PubMedGoogle Scholar
  14. 14.
    Ramachandran GN, Lakshminarayanan AV. Three dimensional reconstructions from radiographs and electron micrographs: application of convolution instead of Fourier transform. Proc Natl Acad Sci. 1971;68:2236–40.PubMedCrossRefGoogle Scholar
  15. 15.
    Rodt T, Bartling S, Gupta R, Pfoh A, Weber BP, Becker H. Optimization of temporal bone CT and 3D-imaging: an experimental approach. Comput Aided Surg. 2002;7(2):107–26.Google Scholar
  16. 16.
    Bartling S, Shukla V, Hayman A, Becker H, Brady T, Gupta R. Ultra-high resolution axial and coronal anatomy of temporal bone. J Comput Aided Tomogr (in press).Google Scholar
  17. 17.
    Walsh C, Phan C, Misra M, Bredella M, Miller K, Fazeli P, Bayraktar H, Kilbanski A, Gupta R. Women with anorexia nervosa: finite element and trabecular structure analysis by using flat-panel volume CT. Radiology. 2010;257(1):167–74.PubMedCrossRefGoogle Scholar
  18. 18.
    Brabrand K, Aaløkken TM, et al. Multicenter evaluation of a new laser guidance system for computed tomography intervention. Acta Radiol. 2004;45:308–12.PubMedCrossRefGoogle Scholar
  19. 19.
    Kwoh YS, Hou J, Jonckheere EA, Hayati S. A robot with improved absolute positioning accuracy for CT guided stereotactic brain surgery. IEEE Trans Biomed Eng. 1988;35:153–60.PubMedCrossRefGoogle Scholar
  20. 20.
    Melzer A, Gutmann B, Remmele T, et al. INNOMOTION for percutaneous image-guided interventions. IEEE Eng Med Biol Mag. 2008;27:66–73.PubMedCrossRefGoogle Scholar
  21. 21.
    Stoianovici D, Cleary K, Patriciu A, et al. AcuBot: a robot for radiological interventions. IEEE Trans Robot Automat. 2003;19:927–30.CrossRefGoogle Scholar
  22. 22.
    Maurin B, Bayle B, Gangloff J, Zanne P, de Mathelin M, Piccin O. A robotized positioning platform guided by computed tomography: practical issues and evaluation. In: Robotics and automation, 2006. ICRA 2006. Proceedings 2006 IEEE international conference on 2006. p. 251–6.Google Scholar
  23. 23.
    Taillant E, Avila-Vilchis J-C, Allegrini C, Bricault I, Cinquin P. CT and MR compatible light puncture robot: architectural design and first experiments. In: Medical image computing and computer-assisted intervention – MICCAI 2004; 2004. p. 145–52.Google Scholar
  24. 24.
    Bricault I, Zemiti N, Jouniaux E, et al. Light puncture robot for CT and MRI interventions. IEEE Eng Med Biol Mag. 2008;27:42–50.PubMedCrossRefGoogle Scholar
  25. 25.
    Walsh CJ, Hanumara NC, Slocum AH, Shepard J-A, Gupta RA. Patient-mounted. Telerobotic tool for CT-guided percutaneous interventions. J Med Devices. 2008;2:011007–10.CrossRefGoogle Scholar
  26. 26.
    Walsh C, Hanumara N, Slocum A, Shepard J, Gupta R. A patient-mounted telerobotic tool for CT-guided percutaneous interventions. ASME J Med Devices. 2008;2(1):011007.Google Scholar
  27. 27.
    Seitel A, Walsh C, Hanumara N, Shepard J, Slocum A, Meinzer H-P, Gupta R, Maier-Hein L. Development and evaluation of a new image-based user interface for robot-assisted needle placements with the Robopsy system (oral presentation and paper). Proc SPIE. 2009;7261:72610X–72610X-9.Google Scholar
  28. 28.
    Walsh C, Franklin J, Slocum A, Gupta R. Design and evaluation of a robot for percutaneous instrument distal tip repositioning. Proceedings of the IEEE engineering in medicine and biology conference. Boston; 2011.Google Scholar
  29. 29.
    Kroeze SGC, Huisman M, Verkooijen HM, van Diest PJ, Ruud Bosch JLH, van den Bosch MAAJ. Real-time 3D fluoroscopy-guided large core needle biopsy of renal masses: a critical early evaluation according to the IDEAL recommendations. Cardiovasc Intervent Radiol. 2012;35:680–5.PubMedCrossRefGoogle Scholar
  30. 30.
    CardiovascInterventRadiol. 6 Aug 2011.
  31. 31.
    Sidky EY, Pan X. Image reconstruction in circular cone-beam computed tomography by constrained, total-variation minimization. Phys Med Biol. 2008;53(17):4777–807.PubMedCentralPubMedCrossRefGoogle Scholar
  32. 32.
    Candés EJ, Romberg J, Tao T. Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information. IEEE Trans IT. 2006;52(2):489–509.CrossRefGoogle Scholar
  33. 33.
    Chen G-H, Tang J, Leng S. Prior image constrained compressed sensing (PICCS): a method to accurately reconstruct dynamic CT images from highly undersampled projection data sets. Med Phys. 2008;35(2):660–3.PubMedCentralPubMedCrossRefGoogle Scholar
  34. 34.
    Gupta R, Grasruck M, Suess C, Bartling SH, Schmidt B, Stierstorfer K, et al. Ultra-high resolution flat-panel volume CT: fundamental principles, design architecture, and system characterization. Eur Radiol. 2006;16(6):1191–205.PubMedCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  • Rajiv Gupta
    • 1
    Email author
  • Conor Walsh
    • 2
  • Irene S. Wang
    • 1
  • Marc Kachelrieß
    • 3
  • Jan Kuntz
    • 3
  • Sönke Bartling
    • 3
  1. 1.Department of RadiologyMassachusetts General Hospital and Harvard Medical SchoolBostonUSA
  2. 2.School of Engineering and Applied Sciences, Wyss Institute for Biologically Inspired EngineeringHarvard UniversityBostonUSA
  3. 3.Department of Medical Physics in RadiologyGerman Cancer Research CenterHeidelbergGermany

Personalised recommendations