Journal of Advances in Medicine and Medical Research

Background: The pituitary gland is a small bean-shaped gland situated at the base of the brain. Pituitary-based tumors are neuroendocrine tumours affecting the pituitary gland. Imaging of the pituitary gland involves the use of computed tomography and magnetic resonance imaging. 3D reconstruction of data from CT images can be converted into 3D and then made into a live anatomical model using a 3D printer.

The objective and aim of this study are to demonstrate that findings from CT scan images can be used to generate 3D printed specific models for patients and clinicians.

Methods: Patient-specific models for three clinical cases were segmented using a segmentation application to isolate the mass and the bone. The process involved image acquisition from a cross-sectional imaging to segmentation of the acquired DICOM image into a 3D model followed by file and model correction for final print, this is then followed on to slicing with the selection of 3D printing material as well as appropriate settings, this is then concluded with the actual print, print accuracy, and cost analysis.

Results: Segmentation of the region of interest took about 45 to 90 minutes with the majority of the time spent on segmentation of the pituitary. Printing of models was done into sections as the skull and mass were printed separately. The times required spanned from 20-40 minutes and 4-9 hours for the mass and skull base respectively. Print accuracy was less than 1.7mm with the total cost of printing a model was less than $50.

Conclusion: This study showed steps in 3D printing anatomical models from a computed tomogram of patients with brain lesions.