Evaluation of geometric deviations in rapid prototyped three-dimensional models created from computed tomography data

BACKGROUND: Computer-aided design and three-dimensional printing have been used in various clinical and fundamental medicine fields, especially in surgery. For example, in the preoperative period, the correspondence of printed products to the anatomy can play an important role in evaluating pathological changes and correction methods. However, determining dimensional deviations of printed models involves ethical and technical difficulties associated with defining a reference and taking many measurements, respectively. Therefore, we propose to use a geometric object with known dimensions as a reference and estimate linear deviations using the Iterative Closest Point algorithm for each of the vertices of the prototyped polygonal mesh.

AIMS: To evaluate the geometric deviations associated with creation of bone-like physical objects from computed tomography data using computer-aided design and additive manufacturing.

MATERIALS AND METHODS: The source object was created using the FreeCAD application; Blender and Meshmixer software was used for polygon meshes correction and transformation. The 3D printing was carried out on an Ender-3 printer with copper-impregnated polylactide plastic BFCopper. Scanning was performed using a 128-slice tomograph Philips Ingenuity CT. A series of tomographic images were processed in 3DSlicer software to create virtual models by semiautomatic segmentation with threshold values of 500 HU, 0 HU, −500 HU, −750 HU, and manual segmentation. Reproduced and reference polygon meshes were compared using the Iterative Closest Point algorithm in CloudCompare software.

RESULTS: The volume of reproduced models exceeded the volume of respective reference models by 1%–27%. The average point cloud linear deviation values of reproduced models from the reference ones were 0.03–0.41 mm. A significant correlation between integral sums of linear deviations and changes in the volume of reproduced models was shown using Spearman's rank correlation coefficient (ρ = 0.83; t_emp = 5.27, p=0.05).

CONCLUSION: The geometry of the reproduced object changes inevitably, while the linear deviations depend more on the chosen segmentation method than on the overall size of the model or its structures. The manual segmentation method can lead to greater linear deviations, though it saves all the necessary anatomical structures.