European Radiology

, Volume 23, Issue 6, pp 1669–1677 | Cite as

Real-time X-ray-based 4D image guidance of minimally invasive interventions

  • Jan Kuntz
  • Rajiv Gupta
  • Stefan O. Schönberg
  • Wolfhard Semmler
  • Marc Kachelrieß
  • Sönke Bartling



A new technology is introduced that enables real-time 4D (three spatial dimensions plus time) X-ray guidance for vascular catheter interventions with acceptable levels of ionising radiation.


The enabling technology is a combination of low-dose tomographic data acquisition with novel compressed sensing reconstruction and use of prior image information. It was implemented in a prototype set-up consisting of a gantry-based flat detector system. In pigs (n = 5) angiographic interventions were simulated. Radiation dosage on a per time base was compared with the “gold standard” of X-ray projection imaging.


Contrary to current image guidance methods that lack permanent 4D updates, the spatial position of interventional instruments could be resolved in continuous, spatial 4D guidance; the movement of the guide wire as well as the expansion of stents could be precisely tracked in 3D angiographic road maps. Dose rate was 23.8 μGy/s, similar to biplane standard angiographic fluoroscopy, which has a dose rate of 20.6 μGy/s.


Real-time 4D X-ray image-guidance with acceptable levels of radiation has great potential to significantly influence the field of minimally invasive medicine by allowing faster and safer interventions and by enabling novel, much more complex procedures for vascular and oncological minimally invasive therapy.

Key Points

• Real-time 4D (three spatial dimensions plus time) angiographic intervention guidance is realistic.

• Low-dose tomographic data acquisition with special compressed sensing-based algorithms is enabled.

• Compared with 4D CT fluoroscopy, this method reduces radiation to acceptable levels.

• Once implemented, vascular interventions may become safer and faster.

• More complex intervention approaches may be developed.


Intervention guidance 4D imaging Compressed sensing Catheter lab Minimally invasive 



The research is funded by DFG (German Research Foundation) grant (KA 1678/6-1 and BA 3546/2-1) and Siemens Healthcare. We acknowledge Stefan Sawall’s support in the creation of the reconstruction algorithm and his contribution to the discussion of the methodology. We thank Dr. Michaela Socher and Roland Galmbacher for animal handling. Furthermore, we would like to acknowledge Barbara Flach and Rolf Kueres for help during experimental set-ups and discussion of future developments. We would like to thank Dr. Michael Grasruck, Dr. Andreas Maier and Dr. Yiannis Kyriakou for extensive help with the experimental set-up, as well as discussion of applications, clinical implementation and future developments.

Supplementary material

330_2012_2761_MOESM1_ESM.mpeg (880 kb)
Supplementary video 1 Example of 4D intervention guidance of a guide wire in the right carotid artery of a full sized pig using colour-coded volume rendering. First the bent guide wire is visible in the external carotid artery, from which it is retracted into the common carotid artery. Here the guide wire tip is placed in front of the ostium of the ascending pharyngeal artery, into which it is then advanced. At all times the spatial position of the guide wire as well as its tip was clear (MPEG 880 kb)
330_2012_2761_MOESM2_ESM.mpeg (152 kb)
Supplementary video 2 A movie of an unfolding stent protruding from the common carotid artery of a pig into the external carotid artery is shown. A volume rendering technique is used (MPEG 152 kb)


  1. 1.
    Rodés-Cabau J (2011) Transcatheter aortic valve implantation: current and future approaches. Nat Rev Cardiol 9:15–29PubMedCrossRefGoogle Scholar
  2. 2.
    Bock M, Wacker FK (2008) MR-guided intravascular interventions: techniques and applications. J Magn Reson Imaging 27:326–338PubMedCrossRefGoogle Scholar
  3. 3.
    Schirra CO, Weiss S, Krueger S, Pedersen SF, Razavi R, Schaeffter T, Kozerke S (2009) Toward true 3D visualization of active catheters using compressed sensing. Magn Reson Med 62:341–347PubMedCrossRefGoogle Scholar
  4. 4.
    Tang J, Hsieh J, Chen G-H (2010) Temporal resolution improvement in cardiac CT using PICCS (TRI-PICCS): performance studies. Med Phys 37:4377–4388PubMedCrossRefGoogle Scholar
  5. 5.
    Carlson SK, Bender CE, Classic KL, Zink FE, Quam JP, Ward EM, Oberg AL (2001) Benefits and safety of CT fluoroscopy in interventional radiologic procedures. Radiology 219:515–520PubMedGoogle Scholar
  6. 6.
    Racadio JM, Babic D, Homan R, Rampton JW, Patel MN, Racadio JM, Johnson ND (2007) Live 3D guidance in the interventional radiology suite. AJR Am J Roentgenol 189:W357–W364PubMedCrossRefGoogle Scholar
  7. 7.
    Schulz B, Eichler K, Siebenhandl P, Gruber-Rouh T, Czerny C, Vogl TJ, Zangos S (2012) Accuracy and speed of robotic assisted needle interventions using a modern cone beam computed tomography intervention suite: a phantom study. Eur Radiol 23:198-204Google Scholar
  8. 8.
    Kroeze SGC, Huisman M, Verkooijen HM, van Diest PJ, Ruud Bosch JLH, van den Bosch MAAJ (2012) Real-time 3D fluoroscopy-guided large core needle biopsy of renal masses: a critical early evaluation according to the IDEAL recommendations. Cardiovasc Intervent Radiol 35:680–685PubMedCrossRefGoogle Scholar
  9. 9.
    Mistretta CA (2011) Sub-nyquist acquisition and constrained reconstruction in time resolved angiography. Med Phys 38:2975–2985PubMedCrossRefGoogle Scholar
  10. 10.
    Neeman Z, Dromi SA, Sarin S, Wood BJ (2006) CT fluoroscopy shielding: decreases in scattered radiation for the patient and operator. J Vasc Interv Radiol 17:1999–2004PubMedCrossRefGoogle Scholar
  11. 11.
    Donoho DL (2006) Compressed sensing. IEEE Trans Inf Theory 52:1289–1306CrossRefGoogle Scholar
  12. 12.
    Chen G-H, Tang J, Leng S (2008) Prior image constrained compressed sensing (PICCS): a method to accurately reconstruct dynamic CT images from highly undersampled projection data sets. Med Phys 35:660–663PubMedCrossRefGoogle Scholar
  13. 13.
    Tang J, Nett BE, Chen G-H (2009) Performance comparison between total variation (TV)-based compressed sensing and statistical iterative reconstruction algorithms. Phys Med Biol 54:5781–5804PubMedCrossRefGoogle Scholar
  14. 14.
    Pan X, Sidky EY, Vannier M (2009) Why do commercial CT scanners still employ traditional, filtered back-projection for image reconstruction? Inverse Probl 25:1230009PubMedCrossRefGoogle Scholar
  15. 15.
    Ritschl L, Bergner F, Fleischmann C, Kachelriess M (2011) Improved total variation-based CT image reconstruction applied to clinical data. Phys Med Biol 56:1545–1561PubMedCrossRefGoogle Scholar
  16. 16.
    Chen G-H, Tang J, Nett B, Qi Z, Leng S, Szczykutowicz T (2010) Prior image constrained compressed sensing (PICCS) and applications in X-ray computed tomography. Curr Med Imaging Rev 2:119–134Google Scholar
  17. 17.
    Chen GH, Tang J, Leng S (2008) Prior Image Constrained Compressed SEnsing (PICCS). Proc Soc Photo Opt Instrum Eng 6856:685618PubMedGoogle Scholar
  18. 18.
    Feldkamp LA, Davis LC, Kress JW (1984) Practical cone-beam algorithm. J Opt Soc Am 1:612–619CrossRefGoogle Scholar
  19. 19.
    McKinnon GC, Bates RH (1981) Towards imaging the beating heart usefully with a conventional CT scanner. IEEE Trans Biomed Eng 28:123–127CrossRefGoogle Scholar
  20. 20.
    Sidky EY, Pan X, Reiser IS, Nishikawa RM, Moore RH, Kopans DB (2009) Enhanced imaging of microcalcifications in digital breast tomosynthesis through improved image-reconstruction algorithms. Med Phys 36:4920–4932PubMedCrossRefGoogle Scholar
  21. 21.
    Candes EJ, Plan Y (2011) A probabilistic and RIPless theory of compressed sensing. IEEE Trans Inf Theory 57:7235–7254CrossRefGoogle Scholar
  22. 22.
    Gupta R, Grasruck M, Suess C, Bartling SH, Schmidt B, Stierstorfer K, Popescu S, Brady T, Flohr T (2006) Ultra-high resolution flat-panel volume CT: fundamental principles, design architecture, and system characterization. Eur Radiol 16:1191–1205PubMedCrossRefGoogle Scholar
  23. 23.
    Grasruck M, Suess C, Stierstorfer K, Popescu S, Flohr T (2005) Evaluation of image quality and dose on a flat-panel CT-scanner. Proc SPIE 5745:179–188CrossRefGoogle Scholar
  24. 24.
    McNitt-Gray MF (2002) AAPM/RSNA physics tutorial for residents: topics in CT. Radiation dose in CT. Radiographics 22:1541–1553PubMedCrossRefGoogle Scholar
  25. 25.
    Kachelriess M, Knaup M, Bockenbach O (2007) Hyperfast parallel-beam and cone-beam backprojection using the cell general purpose hardware. Med Phys 34:1474–1486PubMedCrossRefGoogle Scholar

Copyright information

© European Society of Radiology 2013

Authors and Affiliations

  • Jan Kuntz
    • 1
  • Rajiv Gupta
    • 2
  • Stefan O. Schönberg
    • 3
  • Wolfhard Semmler
    • 1
  • Marc Kachelrieß
    • 1
  • Sönke Bartling
    • 1
    • 3
  1. 1.Department of Medical Physics in RadiologyGerman Cancer Research Center—DKFZHeidelbergGermany
  2. 2.Department of Radiology, Harvard Medical SchoolMassachusetts General HospitalBostonUSA
  3. 3.Institute of Clinical Radiology and Nuclear Medicine, Medical Faculty MannheimHeidelberg UniversityMannheimGermany

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