European Radiology

, Volume 28, Issue 11, pp 4818–4823 | Cite as

A radiopaque 3D printed, anthropomorphic phantom for simulation of CT-guided procedures

  • Paul JahnkeEmail author
  • Felix Benjamin Schwarz
  • Marco Ziegert
  • Tobias Almasi
  • Owais Abdelhadi
  • Maximilian Nunninger
  • Bernd Hamm
  • Michael Scheel



To develop an anthropomorphic phantom closely mimicking patient anatomy and to evaluate the phantom for the simulation of computed tomography (CT)-guided procedures.


Patient CT images were printed with aqueous potassium iodide solution (1 g/mL) on paper. The printed paper sheets were stacked in alternation with 1-mm thick polyethylene foam layers, cut to the patient shape and glued together to create an anthropomorphic abdomen phantom. Ten interventional radiologists performed periradicular infiltration on the phantom and rated the phantom procedure regarding different aspects of suitability for simulating CT-guided procedures.


Radiopaque printing in combination with polyethylene foam layers achieved a phantom with detailed patient anatomy that allowed needle placement. CT-guided periradicular infiltration on the phantom was rated highly realistic for simulation of anatomy, needle navigation and overall course of the procedure. Haptics were rated as intermediately realistic. Participants strongly agreed that the phantom was suitable for training and learning purposes.


A radiopaque 3D printed, anthropomorphic phantom provides a realistic platform for the simulation of CT-guided procedures. Future work will focus on application for training and procedure optimisation.

Key Points

Radiopaque 3D printing combined with polyethylene foam achieves patient phantoms for CT-guided procedures.

Radiopaque 3D printed, anthropomorphic phantoms allow realistic simulation of CT-guided procedures.

Realistic visual guidance is a key aspect in simulation of CT-guided procedures.

Three-dimensional printed phantoms provide a platform for training and optimisation of CT-guided procedures.


Printing, three-dimensional Phantoms, imaging Fluoroscopy Tomography, X-ray computed Simulation training 



We thank Christian Althoff, Torsten Diekhoff, Felix Doellinger, Ahi Sema Issever, Matthias Rief, Valentina Romano, Musaab Saleh, Regina Thiel and Elke Zimmermann of the Department of Radiology, Charité–Universitätsmedizin Berlin.


This study has received funding by the Bundesministerium für Wirtschaft und Energie (DE): 03EFHBE093.

Compliance with ethical standards


The scientific guarantor of this publication is Dr. Paul Jahnke.

Conflict of interest

The authors of this manuscript declare no relationships with any companies, whose products or services may be related to the subject matter of the article.


Patent applications for the 3D printing method were filed by Dr. Jahnke and PD Dr. Scheel: DE202015104282U1, EP000003135199A1, US020170042501A1.

Statistics and biometry

No complex statistical methods were necessary for this paper.

Informed consent

Written informed consent was waived by the Institutional Review Board.

Ethical approval

Institutional Review Board approval was obtained.


• prospective

• observational

• performed at one institution


  1. 1.
    Ahlberg G, Enochsson L, Gallagher AG et al (2007) Proficiency-based virtual reality training significantly reduces the error rate for residents during their first 10 laparoscopic cholecystectomies. Am J Surg 193:797–804CrossRefGoogle Scholar
  2. 2.
    MacDonald J, Ketchum J, Williams RG, Rogers LQ (2003) A lay person versus a trained endoscopist: can the preop endoscopy simulator detect a difference? Surg Endosc 17:896–898CrossRefGoogle Scholar
  3. 3.
    Waterman BR, Martin KD, Cameron KL, Owens BD, Belmont PJ (2016) Simulation training improves surgical proficiency and safety during diagnostic shoulder arthroscopy performed by residents. Orthopedics 39:e479–e485CrossRefGoogle Scholar
  4. 4.
    Thomsen AS, Bach-Holm D, Kjærbo H et al (2017) Operating room performance improves after proficiency-based virtual reality cataract surgery Training. Ophthalmology 124:524–531CrossRefGoogle Scholar
  5. 5.
    Draycott TJ, Crofts JF, Ash JP et al (2008) Improving neonatal outcome through practical shoulder dystocia training. Obstet Gynecol 112:14–20CrossRefGoogle Scholar
  6. 6.
    Pannell JS, Santiago-Dieppa DR, Wali AR et al (2016) Simulator-based angiography and endovascular neurosurgery curriculum: a longitudinal evaluation of performance following simulator-based angiography training. Cureus 8:e756PubMedPubMedCentralGoogle Scholar
  7. 7.
    Barsuk JH, McGaghie WC, Cohen ER, O'Leary KJ, Wayne DB (2009) Simulation-based mastery learning reduces complications during central venous catheter insertion in a medical intensive care unit. Crit Care Med 37:2697–2701PubMedGoogle Scholar
  8. 8.
    Zendejas B, Cook DA, Bingener J et al (2011) Simulation-based mastery learning improves patient outcomes in laparoscopic inguinal hernia repair: a randomized controlled trial. Ann Surg 254:502–509 discussion 509-511CrossRefGoogle Scholar
  9. 9.
    Cohen ER, Feinglass J, Barsuk JH et al (2010) Cost savings from reduced catheter-related bloodstream infection after simulation-based education for residents in a medical intensive care unit. Simul Healthc 5:98–102CrossRefGoogle Scholar
  10. 10.
    Gomoll AH, O'Toole RV, Czarnecki J, Warner JJ (2007) Surgical experience correlates with performance on a virtual reality simulator for shoulder arthroscopy. Am J Sports Med 35:883–888CrossRefGoogle Scholar
  11. 11.
    Dimmick S, Jones M, Challen J, Iedema J, Wattuhewa U, Coucher J (2007) CT-guided procedures: evaluation of a phantom system to teach accurate needle placement. Clin Radiol 62:166–171CrossRefGoogle Scholar
  12. 12.
    Mendiratta-Lala M, Williams TR, Mendiratta V, Ahmed H, Bonnett JW (2015) Simulation center training as a means to improve resident performance in percutaneous noncontinuous CT-guided fluoroscopic procedures with dose reduction. AJR Am J Roentgenol 204:W376–W383CrossRefGoogle Scholar
  13. 13.
    Gruber-Rouh T, Lee C, Bolck J et al (2015) Intervention planning using a laser navigation system for ct-guided interventions: a phantom and patient study. Korean J Radiol 16:729–735CrossRefGoogle Scholar
  14. 14.
    Jacobi V, Thalhammer A, Kirchner J (1999) Value of a laser guidance system for CT interventions: a phantom study. Eur Radiol 9:137–140CrossRefGoogle Scholar
  15. 15.
    Moser C, Becker J, Deli M, Busch M, Boehme M, Groenemeyer DH (2013) A novel Laser Navigation System reduces radiation exposure and improves accuracy and workflow of CT-guided spinal interventions: a prospective, randomized, controlled, clinical trial in comparison to conventional freehand puncture. Eur J Radiol 82:627–632CrossRefGoogle Scholar
  16. 16.
    Nitta N, Takahashi M, Tanaka T et al (2007) Laser-guided computed tomography puncture system: simulation experiments using artificial phantom lesions and preliminary clinical experience. Radiat Med 25:187–193CrossRefGoogle Scholar
  17. 17.
    Jahnke P, Limberg FR, Gerbl A et al (2017) Radiopaque three-dimensional printing: a method to create realistic CT phantoms. Radiology 282:569–575CrossRefGoogle Scholar
  18. 18.
    Feigl GC, Dreu M, Kastner M et al (2017) Thermocoagulation of the medial branch of the dorsal branch of the lumbal spinal nerve: flouroscopy versus CT. Pain Med 18:36–40CrossRefGoogle Scholar
  19. 19.
    Kroes MW, Busser WM, Fütterer JJ et al (2013) Assessment of needle guidance devices for their potential to reduce fluoroscopy time and operator hand dose during C-arm cone-beam computed tomography-guided needle interventions. J Vasc Interv Radiol 24:901–906CrossRefGoogle Scholar
  20. 20.
    Schulz B, Eichler K, Siebenhandl P et al (2013) Accuracy and speed of robotic assisted needle interventions using a modern cone beam computed tomography intervention suite: a phantom study. Eur Radiol 23:198–204CrossRefGoogle Scholar
  21. 21.
    Won HJ, Kim N, Kim GB, Seo JB, Kim H (2017) Validation of a CT-guided intervention robot for biopsy and radiofrequency ablation: experimental study with an abdominal phantom. Diagn Interv Radiol 23:233–237CrossRefGoogle Scholar

Copyright information

© European Society of Radiology 2018

Authors and Affiliations

  1. 1.Department of RadiologyCharité – Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of HealthBerlinGermany

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