Skip to main content

Advertisement

Log in

Value of 3D printing for the comprehension of surgical anatomy

  • Published:
Surgical Endoscopy Aims and scope Submit manuscript

Abstract

Background

In a preliminary experience, we claimed the potential value of 3D printing technology for pre-operative counseling and surgical planning. However, no objective analysis has ever assessed its additional benefit in transferring anatomical information from radiology to final users. We decided to validate the pre-operative use of 3D-printed anatomical models in patients with solid organs’ diseases as a new tool to deliver morphological information.

Methods

Fifteen patients scheduled for laparoscopic splenectomy, nephrectomy, or pancreatectomy were selected and, for each, a full-size 3D virtual anatomical object was reconstructed from a contrast-enhanced MDCT (Multiple Detector Computed Tomography) and then prototyped using a 3D printer. After having carefully evaluated—in a random sequence—conventional contrast MDCT scans, virtual 3D reconstructions on a flat monitor, and 3D-printed models of the same anatomy for each selected case, thirty subjects with different expertise in radiological imaging (10 medical students, 10 surgeons and 10 radiologists) were administered a multiple-item questionnaire. Crucial issues for the anatomical understanding and the pre-operative planning of the scheduled procedure were addressed.

Results

The visual and tactile inspection of 3D models allowed the best anatomical understanding, with faster and clearer comprehension of the surgical anatomy. As expected, less experienced medical students perceived the highest benefit (53.9% ± 4.14 of correct answers with 3D-printed models, compared to 53.4 % ± 4.6 with virtual models and 45.5% ± 4.6 with MDCT), followed by surgeons and radiologists. The average time spent by participants in 3D model assessing was shorter (60.67 ± 25.5 s) than the one of the corresponding virtual 3D reconstruction (70.8 ± 28.18 s) or conventional MDCT scan (127.04 ± 35.91 s).

Conclusions

3D-printed models help to transfer complex anatomical information to clinicians, resulting useful in the pre-operative planning, for intra-operative navigation and for surgical training purposes.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

References

  1. Calhoun PS, Kuszyk BS, Heath DG, Carley JC, Fishman EK (1999) Three dimensional volume rendering of spiral ct data: theory and method. Radiographics 19(3):745–764

    Article  CAS  PubMed  Google Scholar 

  2. Fleming RW, Holtmann-Rice D, Bulthoff HH (2011) Estimation of 3D shape from image orientation, Proc Natl Acad Sci USA 108:20438–20443

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Meijer F, Van der Lubbe RH (2011) Active exploration improves perceptual sensitivity for visual 3D objects in visual recognition tasks. Vision Res 51:2431–2439

    Article  PubMed  Google Scholar 

  4. Rasheed K, Mix D, Chandra A (2015) Numerous applications of 3d printing in vascular surgery. Ann Vasc Surg 29(4):643–644

    Article  Google Scholar 

  5. Vicknes W, Vairavan N, Ravindran K (2014) Utility of multimaterial 3D printers in creating models with pathological entities to enhance the training experience of neurosurgeons. J Neurosurg 120:489–492

    Article  Google Scholar 

  6. Jacobs S et al (2008) 3D-imaging of cardiac structures using 3D heart models for planning in heart surgery: a preliminary study. Interact Cardiovasc Thorac Surg 7(1)6–9

    Article  PubMed  Google Scholar 

  7. Tam MD, Laycock SD, Bell D, Chosjnowki A (2012) 3D printout of a DICOM file to aid surgical planning in a 6 year old patient with a large scapular osteochondroma complicating congenital diaphyseal aclasia. Radiol Case 6(1):31–37

    Article  Google Scholar 

  8. John NW, McCloy RF, Herrman S (2004) Interrogation of patient data delivered to the operating theatre during hepato-pancreatic surgery using high-performance computing. Comput Aided Surg 9:235–242

    Article  PubMed  Google Scholar 

  9. AlAli AB, Griffin MF, Butler PE, Three-Dimensional Printing Surgical Applications. Avaible on http://www.eplasty.com

  10. Malik HH, Darwood ARJ, Shaunak S, Kulatilake P, El-Hilly AA, Mulki O, Baskaradas A (2015) Three-dimensional printing in surgery: a review of current surgical applications. J Surg Res 199:512–522

    Article  PubMed  Google Scholar 

  11. Zein NN, Hanouneh A, Bishop PD, Samaan M, Eghtesad B, Quintini C, Miller C, Yerian L, Klatte R (2013) Three-dimensional print of a liver for preoperative planning in living donor liver transplantation. Liver Transpl 19:1304–1310

    Article  PubMed  Google Scholar 

  12. Takahashi K, Sasaki R, Ohkohchi N et al (2010) Preoperative 3D volumetric analysis for liver congestion applied in a patient with hilar cholangiocarcinoma. Langenbecks Arch Surg 395:761–765

    Article  PubMed  Google Scholar 

  13. Pietrabissa A, Marconi S, Peri A, Pugliese L, Auricchio F (2015) From ct scanning to 3-d printing technology for the pre-operative planning in laparoscopic splenectomy. Surg End 30(1), 366–371

    Google Scholar 

  14. Hnatkova E, Kratky P, Dvorak Z.(2014) Conversion of 2D medical scan data into 3D printed model. Advanced in Environmental Sciences, Development and Chemistry 315–318

  15. Gibson I, Rosen D, Stucker B (2010) Additive manufacturing technologies: 3D printing, rapid prototyping, and direct digital manufacturing. 2nd edn. Springer, Newyork

    Book  Google Scholar 

  16. Wunderlich H, Reichelt O, Schubert R, Zermann DH, Schubert J (2000) Preoperative simulation of partial nephrectomy with three-dimensional computed tomography. BJU Int 86(7):777–781

    Article  CAS  PubMed  Google Scholar 

  17. Rengier R, Mehndiratta A, von Tengg-Kobligk H, Zechmann CM, Unterhinninghofen R, Kauczor HU, Giesel FL (2010) 3D printing based on imaging data: review of medical applications. Int J Comput Assist Radiol Surg 5:335–341

    Article  CAS  PubMed  Google Scholar 

  18. Ferrari V, Cappelli C, Megali G, Pietrabissa A (2008) An anatomy driven approach for generation of 3D models from multi-phase ct images. In Proceedings of the International Congress and Exhibition, volume 3, Supplement 1, IJCARS

  19. Yushkevich PA, Piven J, Cody Hazlett H, Gimpel Smith R, Ho S, Gee JC, Gerig G (2006) User-guided 3D active contour segmentation of anatomical structures: significantly improved efficiency and reliability. Neuroimage 31:1116–1128

    Article  PubMed  Google Scholar 

  20. McMenamin PG, Quayle MR, McHenry CR, Adams JW (2014) The production of anatomical teaching resources using three-dimensional (3d) printing technology. Anat Sci Educ 7(6):479–486

    Article  PubMed  Google Scholar 

  21. Waran V, Narayanan V, Karuppiah R, Aziz TZ et al (2014) Injecting realism in surgical training—Initial simulation experience with custom 3D models. J Surg Educ, 71(2):193–197

    Article  PubMed  Google Scholar 

  22. Martelli N, Serrano C, Borget I et al (2015) Advantages and disadvantages of 3-dimensional printing insurgery: a systematic review. Surgery 159(6):1485–1500

    Article  Google Scholar 

  23. Kappers AM (2011) Human perception of shape from touch. Philos Trans R Soc Lond B Biol Sci 366:31063114

    Article  Google Scholar 

  24. Silberstein JL, Maddox MM, Dorsey P, Feibus A, Thomas R, Lee BR (2014) Physical models of renal malignancies using standard cross-sectional imaging and 3-dimensionalprinters: a pilot study. Urology 84:268–273

    Article  PubMed  Google Scholar 

Download references

Acknowledgements

The presented activity is inserted in the framework of 3D@UniPV Project (http://www.unipv.it/3d), one of the strategic research area of the University of Pavia.

Funding

No funding was received for this work by any of the following organizations: National Institutes of Health (NIH); Wellcome Trust; Howard Hughes Medical Institute (HHMI); or others.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Luigi Pugliese.

Ethics declarations

Disclosures

Stefania Marconi, Luigi Pugliese, Marta Botti, Andrea Peri, Emma Cavazzi, Saverio Latteri, Ferdinando Auricchio, and Andrea Pietrabissa have no conflicts of interest or financial ties to disclose.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Marconi, S., Pugliese, L., Botti, M. et al. Value of 3D printing for the comprehension of surgical anatomy. Surg Endosc 31, 4102–4110 (2017). https://doi.org/10.1007/s00464-017-5457-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00464-017-5457-5

Keywords

Navigation