With the rapid development and popularization of additive manufacturing, different technologies, including, but not limited to, extrusion-, droplet-, and vat-photopolymerization-based fabrication techniques, have emerged that have allowed tremendous progress in three-dimensional (3D) printing in the past decades. Bioprinting, typically using living cells and/or biomaterials conformed by different printing modalities, has produced functional tissues. As a subclass of vat-photopolymerization bioprinting, digital light processing (DLP) uses digitally controlled photomasks to selectively solidify liquid photocurable bioinks to construct complex physical objects in a layer-by-layer manner. DLP bioprinting presents unique advantages, including short printing times, relatively low manufacturing costs, and decently high resolutions, allowing users to achieve significant progress in the bioprinting of tissue-like complex structures. Nevertheless, the need to accommodate different materials while bioprinting and improve the printing performance has driven the rapid progress in DLP bioprinters, which requires multiple pieces of knowledge ranging from optics, electronics, software, and materials beyond the biological aspects. This raises the need for a comprehensive review to recapitulate the most important considerations in the design and assembly of DLP bioprinters. This review begins with analyzing unique considerations and specific examples in the hardware, including the resin vat, optical system, and electronics. In the software, the workflow is analyzed, including the parameters to be considered for the control of the bioprinter and the voxelizing/slicing algorithm. In addition, we briefly discuss the material requirements for DLP bioprinting. Then, we provide a section with best practices and maintenance of a do-it-yourself DLP bioprinter. Finally, we highlight the future outlooks of the DLP technology and their critical role in directing the future of bioprinting. The state-of-the-art progress in DLP bioprinter in this review will provide a set of knowledge for innovative DLP bioprinter designs.
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Compensating the cell-induced light scattering effect in light-based bioprinting using deep learning
Abstract Digital light processing (DLP)-based three-dimensional (3D) printing technology has the advantages of speed and precision comparing with other 3D printing technologies like extrusion-based 3D printing. Therefore, it is a promising biomaterial fabrication technique for tissue engineering and regenerative medicine. When printing cell-laden biomaterials, one challenge of DLP-based bioprinting is the light scattering effect of the cells in the bioink, and therefore induce unpredictable effects on the photopolymerization process. In consequence, the DLP-based bioprinting requires extra trial-and-error efforts for parameters optimization for each specific printable structure to compensate the scattering effects induced by cells, which is often difficult and time-consuming for a machine operator. Such trial-and-error style optimization for each different structure is also very wasteful for those expensive biomaterials and cell lines. Here, we use machine learning to learn from a few trial sample printings and automatically provide printer the optimal parameters to compensate the cell-induced scattering effects. We employ a deep learning method with a learning-based data augmentation which only requires a small amount of training data. After learning from the data, the algorithm can automatically generate the printer parameters to compensate the scattering effects. Our method shows strong improvement in the intra-layer printing resolution for bioprinting, which can be further extended to solve the light scattering problems in multilayer 3D bioprinting processes.
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- Award ID(s):
- 1907434
- NSF-PAR ID:
- 10333666
- Date Published:
- Journal Name:
- Biofabrication
- Volume:
- 14
- Issue:
- 1
- ISSN:
- 1758-5082
- Page Range / eLocation ID:
- 015011
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1088/1758-5090/ac3b92
