A robust, autonomous, volumetric quality assurance method for 3D printed porous scaffolds

Abstract

Bone tissue engineering strategies aimed at treating critical-sized craniofacial defects often utilize novel biomaterials and scaffolding. Rapid manufacturing of defect-matching geometries using 3D-printing strategies is a promising strategy to treat craniofacial bone loss to improve aesthetic and regenerative outcomes. To validate manufacturing quality, a robust, three-dimensional quality assurance pipeline is needed to provide an objective, quantitative metric of print quality if porous scaffolds are to be translated from laboratory to clinical settings. Previously published methods of assessing scaffold print quality utilized one- and two-dimensional measurements (e.g., strut widths, pore widths, and pore area) or, in some cases, the print quality of a single phantom is assumed to be representative of the quality of all subsequent prints. More robust volume correlation between anatomic shapes has been accomplished; however, it requires manual user correction in challenging cases such as porous objects like bone scaffolds. Here, we designed porous, anatomically-shaped scaffolds with homogenous or heterogenous porous structures. We 3D-printed the designs with acrylonitrile butadiene styrene (ABS) and used cone-beam computed tomography (CBCT) to obtain 3D image reconstructions. We applied the iterative closest point algorithm to superimpose the computational scaffold designs with the CBCT images to obtain a 3D volumetric overlap. In order to avoid false convergences while using an autonomous workflow for volumetric correlation, we developed an independent iterative closest point (I-ICP10) algorithm using MATLAB®, which applied ten initial conditions for the spatial orientation of the CBCT images relative to the original design. Following successful correlation, scaffold quality can be quantified and visualized on a sub-voxel scale for any part of the volume.

Geometric Mismatch Promotes Anatomic Repair in Periorbital Bony Defects in Skeletally Mature Yucatan Minipigs

“…First, post‐operative and pre‐operative CT scans of the porcine skulls were registered to segment out corresponding volume of interest (VOI) ( Figure A). Thereafter, the zygomatic bones were registered using independent iterative closest point (I‐ICP 10 ) [ 23 ] algorithm in MATLAB to provide finer registration in the vicinity of the defect site (Figure 4B). Post registration, areas of underfill (deficit areas where scaffold failed to recapitulate the native bone anatomy) were visualized and their respective magnitudes were quantified (Figure 4C).…”

“…B) The bitmaps were imported into MATLAB and converted into point cloud objects and re‐registered using I‐ICP 10 . [ 23 ] C) From the registered zygomatic point clouds, areas of anatomical mismatch (underfill) were visualized and their corresponding magnitudes were quantified at the defect site. Heatmap demonstrates magnitude of underfill at each point.…”

“…The bitmaps were imported into MATLAB and converted to point clouds. The point clouds of the post‐operative and pre‐operative zygomas were registered again using our previously developed autonomous I‐ICP 10 algorithm [ 23 ] to provide more accurate local registration at the defect site. Thereafter, the registered defect site and the corresponding pre‐operative point clouds were segmented out and areas of underfill were identified using knnsearch tool in MATLAB and their magnitude was determined by calculating the Euclidian distance between the closest neighboring points belonging to both the point clouds (Note: For post‐operative vs pre‐operative registration, underfill is defined as deficit of scaffold failing to recapitulate the native bone anatomy).…”

Artificial Intelligence-Empowered 3D and 4D Printing Technologies toward Smarter Biomedical Materials and Approaches

“…In recent years, additive manufacturing (AM), also called 3D printing, has emerged as a versatile technique and a valuable alternative to traditional manufacturing for the fabrication of complex materials through a layer-by-layer approach, thus resulting in new types of biomedical equipment, scaffolds, wearable devices, soft robotics, actuators, and flexible electronics [ 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 ].…”

“…In the recent years, additive manufacturing (AM), also called 3D-printing, has emerged as a versatile technique and a valuable alternative to traditional manufacturing for the fabrication of complex materials through layer-by-layer approach, thus resulting in new types of biomedical equipment, scaffolds, wearable devices, soft robotics, actuators, and flexible electronics [18][19][20][21][22][23][24][25][26][27]. In biomedical sector, 3D-printing played a crucial role as innovative technology for tissue engineering, organ fabrication, regenerative medicine, and drug delivery [28] (Figure 1).…”