Influence of CT parameters on STL model accuracy

M. van Eijnatten, F.H. Berger, P. de Graaf, J. Koivisto, T. Forouzanfar, J. Wolff

Research output: Contribution to journalArticleAcademicpeer-review

24 Citations (Scopus)


Purpose - Additive manufactured (AM) skull models are increasingly used to plan complex surgical cases and design custom implants. The accuracy of such constructs depends on the standard tessellation language (STL) model, which is commonly obtained from computed tomography (CT) data. The aims of this study were to assess the image quality and the accuracy of STL models acquired using different CT scanners and acquisition parameters. Design/methodology/approach - Images of three dry human skulls were acquired using two multi-detector row computed tomography (MDCT) scanners, a dual energy computed tomography (DECT) scanner and one cone beam computed tomography (CBCT) scanner. Different scanning protocols were used on each scanner. All images were ranked according to their image quality and converted into STL models. The STL models were compared to gold standard models. Findings - Image quality differed between the MDCT, DECT and CBCT scanners. Images acquired using low-dose MDCT protocols were preferred over images acquired using routine protocols. All CT-based STL models demonstrated non-uniform geometrical deviations of up to +0.9 mm. The largest deviations were observed in CBCT-derived STL models. Practical implications - While patient-specific AM constructs can be fabricated with great accuracy using AM technologies, their design is more challenging because it is dictated by the correctness of the STL model. Inaccurate STL models can lead to ill-fitting implants that can cause complications after surgery. Originality/value - This paper suggests that CT imaging technologies and their acquisition parameters affect the accuracy of medical AM constructs.
Original languageEnglish
Pages (from-to)678-685
Number of pages8
JournalRapid Prototyping Journal
Issue number4
Publication statusPublished - 2017


  • Accuracy
  • Additive manufacturing
  • Computed tomography
  • Medical
  • Modelling
  • STL

Cite this