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Dental crowns are used to restore decayed or chipped teeth, where their surfaces play a key role in this restoration process, as they affect the fitting and stable bonding of the prostheses. The surface texture of crowns can interfere with this restoration process, therefore the measurement of their inner surface roughness is very important but difficult to achieve using conventional imaging methods. In this study, the inner surfaces of dental crowns were three-dimensionally (3D) visualized using swept-source optical coherence tomography (SS-OCT) system. Nine crowns were fabricated with a commercial 3D printer using three different hatching methods (one-way, cross, and 30&#x00B0; angle counter-clockwise) and three different build direction angles (0&#x00B0;, 45&#x00B0;, and 90&#x00B0;). In addition, an image processing algorithm was developed, which uses morphological filtering, boundary detection, and a high-pass frequency filtering technique, to quantitatively evaluate the inner surface roughness of the dental crowns cross-sections with the depth-of-focus set to match two different regions. The averaged smoothness of fabricated crown was effectively produced using the cross-hatching and the build direction angle of 90&#x00B0; by the respective process. Thus, the results confirm the potential use of this methodology to determine the best parameters to use in 3D fabrication for improving the effectiveness and stability of dental prostheses.

Assessment of the Inner Surface Roughness of 3D Printed Dental Crowns via Optical Coherence Tomography Using a Roughness Quantification Algorithm