AccScience Publishing / IJB / Volume 10 / Issue 3 / DOI: 10.36922/ijb.2021
Cite this article
134
Download
1513
Views
Journal Browser
Volume | Year
Issue
Search
News and Announcements
View All
RESEARCH ARTICLE

A new solution for in situ monitoring of shape fidelity in extrusion-based bioprinting via thermal imaging

Simone Giovanni Gugliandolo1,2 Egon Prioglio1 Davide Moscatelli2 Bianca Maria Colosimo1*
Show Less
1 Department of Mechanical Engineering, Politecnico di Milano, Via La Masa, 1, 20156, Milano, Italy
2 Department of Chemistry, Materials and Chemical Engineering “Giulio Natta”, Politecnico di Milano, Piazza Leonardo da Vinci, 32, 20133, Milano, Italy
IJB 2024, 10(3), 2021 https://doi.org/10.36922/ijb.2021
Submitted: 12 October 2023 | Accepted: 28 December 2023 | Published: 22 March 2024
© 2024 by the Author(s). This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution 4.0 International License ( https://creativecommons.org/licenses/by/4.0/ )
Abstract

Bioprinting is an interdisciplinary study field, where additive manufacturing is combined with tissue engineering and material sciences. The ever-increasing need for personalized medicine fueled interest in the possibility of using this technique to reproduce biological tissues, allowing bioprinting to establish itself as one of the most promising approach in biomedical research. Producing bioconstructs that resemble living tissues is a very complex and multi-step procedure. Given the complexity of the processes involved, the literature still lacks robust solutions for monitoring the bioprinted construct quality, especially in situ and in-line. Here, a novel non-destructive approach for monitoring the geometries of bioprinted constructs based on infrared (IR) imaging is proposed. Besides the intuitive use of IR information to gain insight on the temperature signature, we propose IR video imaging as a viable solution to overcome traditional problems of visible-range imaging for geometry reconstruction with transparent bioinks, especially when precise information on the last printed layer only is required. The results obtained show a significant new direction for in-line monitoring of bioprinting processes.

Keywords
Additive manufacturing
3D bioprinting
Thermal imaging
Monitoring
Funding
This research was partially funded by the European Commission under the “HORIZON-CL4-2021-DIGITAL-EMERGING-01 project BioProS - Biointelligent Production Sensor to Measure Viral Activity” (grant agreement no. 101070120, 2022–2026).
Conflict of interest
The authors declare no conflicts of interest.
References
  1. Moroni L, Boland T, Burdick JA, et al. Biofabrication: a guide to technology and terminology. Trends Biotechnol. 2018;36(4):384-402. doi: 10.1016/j.tibtech.2017.10.015
  2. Ng WL, Chua CK, Shen YF. Print me an organ! Why we are not there yet. Prog Polym Sci. 2019;97:101145. doi: 10.1016/j.progpolymsci.2019.101145
  3. Santoni S, Gugliandolo SG, Sponchioni M, Moscatelli D, Colosimo BM. 3D bioprinting: current status and trends—a guide to the literature and industrial practice. Bio-Des Manuf. 2021;5(1):14-42. doi: 10.1007/S42242-021-00165-0
  4. Pereira FDAS, Parfenov V, Khesuani YD, Ovsianikov A, Mironov V. Commercial 3D bioprinters. In: Ovsianikov A, Yoo J, Mironov V, eds. 3D Printing and Biofabrication. Cham: Springer; 2018: 535-549. doi: 10.1007/978-3-319-45444-3_12
  5. Jang J, Park JY, Gao G, Cho D-W. Biomaterials-based 3D cell printing for next-generation therapeutics and diagnostics. Biomaterials. 2018;156:88-106. doi: 10.1016/j.biomaterials.2017.11.030
  6. Constante G, Apsite I, Alkhamis H, et al. 4D biofabrication using a combination of 3D printing and melt-electrowriting of shape-morphing polymers. ACS Appl Mater Interfaces. 2021;13(11):12767-12776. doi: 10.1021/acsami.0c18608
  7. Caleffi JT, Aal MCE, Gallindo H de OM, et al. Magnetic 3D cell culture: state of the art and current advances. Life Sci. 2021;286:120028. doi: 10.1016/j.lfs.2021.120028
  8. Hu X, Zheng J, Hu Q, et al. Smart acoustic 3D cell construct assembly with high-resolution. Biofabrication. 2022;14(4). doi: 10.1088/1758-5090/ac7c90
  9. Moldovan NI, Hibino N, Nakayama K. Principles of the kenzan method for robotic cell spheroid-based three-dimensional bioprinting. Tissue Eng Part B Rev. 2017;23(3):237-244. doi: 10.1089/ten.teb.2016.0322
  10. Zhou X, Wu H, Wen H, Zheng B. Advances in single-cell printing. Micromachines. 2022;13(1):80. doi: 10.3390/mi13010080
  11. Ramesh S, Harrysson OLA, Rao PK, et al. Extrusion bioprinting: recent progress, challenges, and future opportunities. Bioprinting. 2021;21:e00116. doi: 10.1016/j.bprint.2020.e00116
  12. Boularaoui S, Al Hussein G, Khan KA, Christoforou N, Stefanini C. An overview of extrusion-based bioprinting with a focus on induced shear stress and its effect on cell viability. Bioprinting. 2020;20:e00093. doi: 10.1016/J.BPRINT.2020.E00093
  13. Saunders RE, Derby B. Inkjet printing biomaterials for tissue engineering: bioprinting. Int Mater Rev. 2014;59(8):430-448. doi: 10.1179/1743280414Y.0000000040
  14. Ng WL, Huang X, Shkolnikov V, Suntornnond R, Yeong WY. Polyvinylpyrrolidone-based bioink: influence of bioink properties on printing performance and cell proliferation during inkjet-based bioprinting. Bio-Des Manuf. 2023;6(6):676-690. doi: 10.1007/S42242-023-00245-3/FIGURES/5
  15. Levato R, Dudaryeva O, Garciamendez-Mijares CE, et al. Light-based vat-polymerization bioprinting. Nat Rev Methods Primers. 2023;3(1):1-19. doi: 10.1038/s43586-023-00231-0
  16. Gao G, Kim BS, Jang J, Cho D-W. Recent strategies in extrusion-based three-dimensional cell printing toward organ biofabrication. ACS Biomater Sci Eng. 2019;5(3):1150-1169. doi: 10.1021/acsbiomaterials.8b00691
  17. Antoshin AA, Churbanov SN, Minaev NV, et al. LIFT-bioprinting, is it worth it? Bioprinting. 2019;15(May):e00052. doi: 10.1016/j.bprint.2019.e00052
  18. Nuñez Bernal P, Delrot P, Loterie D, et al. Volumetric bioprinting of complex living-tissue constructs within seconds. Adv Mater. 2019;31(42):1904209. doi: 10.1002/ADMA.201904209 
  19. Kumar H, Kim K. Stereolithography 3D bioprinting. Methods Mol Biol. 2020;2140:93-108. doi: 10.1007/978-1-0716-0520-2_6
  20. Sheth R, Balesh ER, Zhang YS, Hirsch JA, Khademhosseini A, Oklu R. Three-dimensional printing: an enabling technology for IR. J Vasc Interv Radiol. 2016;27(6):859-865. doi: 10.1016/j.jvir.2016.02.029
  21. Bouguéon G, Kauss T, Dessane B et al., 2019, Micro-and nano-formulations for bioprinting and additive manufacturing. Drug Discov Today 24 (1): 163–178. doi: 10.1016/j.drudis.2018.10.013
  22. Caltanissetta F, Grasso M, Petrò S, Colosimo BM. Characterization of in-situ measurements based on layerwise imaging in laser powder bed fusion. Addit Manuf. 2018;24:183-199. doi: 10.1016/J.ADDMA.2018.09.017
  23. Grasso M, Remani A, Dickins A, Colosimo BM, Leach RK. In-situ measurement and monitoring methods for metal powder bed fusion: an updated review. Meas Sci Technol. 2021;32(11):112001. doi: 10.1088/1361-6501/AC0B6B
  24. Grasso M, Colosimo BM. Process defects and in situ monitoring methods in metal powder bed fusion: a review. Meas Sci Technol. 2017;28(4):044005. doi: 10.1088/1361-6501/AA5C4F
  25. Colosimo BM, Huang Q, Dasgupta T, Tsung F. Opportunities and challenges of quality engineering for additive manufacturing. J Qual Technol. 2018;50(3):233-252. doi: 10.1080/00224065.2018.1487726
  26. AbouelNour Y, Gupta N. Assisted defect detection by in-process monitoring of additive manufacturing using optical imaging and infrared thermography. Addit Manuf. 2023;67:103483. doi: 10.1016/J.ADDMA.2023.103483
  27. Hossain REN, Lewis J, Moore AL. In situ infrared temperature sensing for real-time defect detection in additive manufacturing. Addit Manuf. 2021;47:102328. doi: 10.1016/J.ADDMA.2021.102328
  28. Gugliandolo SG, Margarita A, Santoni S, Moscatelli D, Colosimo BM. In-situ monitoring of defects in extrusion-based bioprinting processes using visible light imaging. Procedia CIRP. 2022;110(C):219-224. doi: 10.1016/J.PROCIR.2022.06.040
  29. Schmieg B, Gretzinger S, Schuhmann S, Guthausen G, Hubbuch J. Magnetic resonance imaging as a tool for quality control in extrusion-based bioprinting. Biotechnol J. 2022;17(5):2100336. doi: 10.1002/BIOT.202100336
  30. Strauß S, Meutelet R, Radosevic L, Gretzinger S, Hubbuch J. Image analysis as PAT-tool for use in extrusion-based bioprinting. Bioprinting. 2021;21:e00112. doi: 10.1016/J.BPRINT.2020.E00112
  31. Uzun-Per M, Gillispie GJ, Tavolara TE, et al. Automated image analysis methodologies to compute bioink printability. Adv Eng Mater. 2021;23(4):2000900. doi: 10.1002/ADEM.202000900
  32. Bone JM, Childs CM, Menon A, et al. Hierarchical machine learning for high-fidelity 3D printed biopolymers. ACS Biomater Sci Eng. 2020;6(12):7021-7031. doi: 10.1021/acsbiomaterials.0c00755
  33. Sedigh A, DiPiero D, Shine KM, Tomlinson RE. Enhancing precision in bioprinting utilizing fuzzy systems. Bioprinting. 2022;25:e00190. doi: 10.1016/J.BPRINT.2021.E00190
  34. Wang L, Xu ME, Luo L, Zhou Y, Si P. Iterative feedback bio-printing-derived cell-laden hydrogel scaffolds with optimal geometrical fidelity and cellular controllability. Sci Rep. 2018;8(1):1-13. doi: 10.1038/s41598-018-21274-4
  35. Wang L, Xu M, Zhang L, Zhou Q, Luo L. Automated quantitative assessment of three-dimensional bioprinted hydrogel scaffolds using optical coherence tomography. Biomed Opt Express. 2016;7(3):894. doi: 10.1364/boe.7.000894
  36. Bonatti AF, Vozzi G, Chua CK, De Maria C. A deep learning approach for error detection and quantification in extrusion-based bioprinting. Mater Today Proc. 2022;70:131-135. doi: 10.1016/J.MATPR.2022.09.006 
  37. Bonatti AF, Vozzi G, Chua CK, De Maria C. A deep learning quality control loop of the extrusion-based bioprinting process. Int J Bioprint. 2022;8(4):307-320. doi: 10.18063/IJB.V8I4.620
  38. Yang S, Chen Q, Wang L, Xu M. In situ defect detection and feedback control with three-dimensional extrusion-based bioprinter-associated optical coherence tomography. Int J Bioprint. 2022;9(1):47-62. doi: 10.18063/IJB.V9I1.624
  39. Jin Z, Zhang Z, Shao X, Gu GX. Monitoring anomalies in 3D bioprinting with deep neural networks. ACS Biomater Sci Eng. 2023;9(7):3945-3952. doi: 10.1021/acsbiomaterials.0c01761
  40. Armstrong AA, Alleyne AG, Wagoner Johnson AJ. 1D and 2D error assessment and correction for extrusion-based bioprinting using process sensing and control strategies. Biofabrication. 2020;12(4):045023. doi: 10.1088/1758-5090/ABA8EE
  41. Armstrong AA, Norato J, Alleyne AG, Johnson AJW. Direct process feedback in extrusion-based 3D bioprinting. Biofabrication. 2019;12(1):015017. doi: 10.1088/1758-5090/AB4D97
  42. Armstrong AA, Pfeil A, Alleyne AG, Johnson AJW. Process monitoring and control strategies in extrusion-based bioprinting to fabricate spatially graded structures. Bioprinting. 2021;21:e00126. doi: 10.1016/J.BPRINT.2020.E00126
  43. Gao T, Gillispie GJ, Copus JS, et al. Optimization of gelatin-alginate composite bioink printability using rheological parameters: a systematic approach. Biofabrication. 2018;10(3):034106. doi: 10.1088/1758-5090/aacdc7
  44. Yaneva A, Shopova D, Bakova D, et al. The progress in bioprinting and its potential impact on health-related quality of life. Bioengineering. 2023;10(8):910. doi: 10.3390/BIOENGINEERING10080910
  45. Moncal KK, Ozbolat V, Datta P, Heo DN, Ozbolat IT. Thermally-controlled extrusion-based bioprinting of collagen. J Mater Sci Mater Med. 2019;30:55. doi: 10.1007/s10856-019-6258-2
  46. Santoni S, Sponchioni M, Gugliandolo SG, Colosimo BM, Moscatelli D. Preliminary tests on PEG-based thermoresponsive polymers for the production of 3D bioprinted constructs. Procedia CIRP. 2022;110(C): 350-355. doi: 10.1016/j.procir.2022.06.062
  47. Suntornnond R, An J, Chua CK. Bioprinting of thermoresponsive hydrogels for next generation tissue engineering: a review. Macromol Mater Eng. 2017;302(1):1600266. doi: 10.1002/mame.201600266
  48. Hsieh FY, Lin HH, Hsu S-H. 3D bioprinting of neural stem cell-laden thermoresponsive biodegradable polyurethane hydrogel and potential in central nervous system repair. Biomaterials. 2015;71:48-57. doi: 10.1016/j.biomaterials.2015.08.028
  49. Bradley D, Roth G. Adaptive thresholding using the integral image. J Graph Tools. 2007;12(2):13-21. doi: 10.1080/2151237X.2007.10129236
  50. Dice LR. Measures of the amount of ecologic association between species. Ecology. 1945;26(3):297-302. doi: 10.2307/1932409
  51. Caltanissetta F, Dreifus G, Hart AJ, Colosimo BM. In-situ monitoring of material extrusion processes via thermal videoimaging with application to big area additive manufacturing (BAAM). Addit Manuf. 2022;58:102995. doi: 10.1016/J.ADDMA.2022.102995
Share
Back to top
International Journal of Bioprinting, Electronic ISSN: 2424-8002 Print ISSN: 2424-7723, Published by AccScience Publishing