Error Measurement Between Anatomical Porcine Spine, CT Images, and 3D Printing

Abstract

Rationale and objectives: 3D printers are increasingly used in medical applications such as surgical planning, creation of implants and prostheses, and medical education. For the creation of reliable 3D printed models of the vertebral column, processing must be performed on CT images. This processing must be assessed and validated so that any error of the printed model can be recognized and minimized.

Material and methods: In order to perform this validation, 10 CT scans of porcine lumbar spinal vertebra were used, which were then dissected and scanned again. CT image processing was performed to obtain a mesh and perform 3D printing.

Results: There was no statistical difference among the four different levels of vertebrae measurements (first CT images, second CT images, anatomical piece of porcine bone and 3D printing of porcine bone; One Way repeated measure ANOVA, F < F_crit, p value > α = 0.05). The Intraclass Correlation also revealed a mean intraclass correlation coefficient (3,1) = 0.9553, which describes the reliability of all four levels in addition to the reliability of the data between porcine samples subjected to different levels of measurement. This shows that the average error is less than 1 mm.

Conclusions: The measurements of models created with 3D printers using the pipeline described in this paper have an average error of 0.60 mm with CT images and 0.73 mm with anatomical piece. Thus, 3D printed models accurately reflect in vivo bones and provide accurate 3D impressions to assist in surgical planning.

Keywords: 3D print; CT; Medical image segmentation; Porcine; Spine.