CT-Based Non-Destructive Quantification of 3D-Printed Hydrogel Implants
- 14 Downloads
Additive manufacturing of hydrogel-based implants, as e.g for the human skull are becoming more important as they should allow a modelling of the different natural layers of the skullcap, and support the bone healing process. Nevertheless, the quality, structure and consistency of such 3D-printed hydrogel implants are important for the reliable production, quality assurance and further tests of the implant production. One possibility for non-destructive imaging and quantification of such additive manufactured hydrogels is computed tomography combined with quantitative image analysis. Hence, the goal of this work is the quantitative analysis of the hydrogel-air relationship as well as the automated computation of the hydrogel angles between the different hydrogel layers. This is done by application and evaluation of various classical image analysis methods such as thresholdig, morphological operators, region growing and Fourier transformation of the CT-slices. Results show, that the examined quantities (channels in the hydrogel lattice, angles between the layers) are in the expected ranges, and it can be concluded that the additive manufacturing process may yield usable hydrogel meshes for reconstructive medicine.
Unable to display preview. Download preview PDF.
- 1.Diebowski S. Kombination von CT und Hartgewebehistologie bei der Auswertung von in-vivo Untersuchungen an Knochenersatzmaterialien für den Schädelbereich [PhD thesis]; 2014.Google Scholar
- 2.Rosildo J, Barbara R. Reconstruction of the skull inverting the deformed surface of the bone after exeresis of a frontal arachnoid cyst. Autopsy & case reports. 2017;7(2):69–73.Google Scholar
- 3.Jang TS, Jung HD, Pan H, et al. 3D printing of hydrogel composite systems: Recent advances in technology for tissue engineering. Int J Bioprinting. 2018;4(01).Google Scholar
- 4.Detsch R, Sarker B, Grigore A, et al. Alginate and gelatine blending for bone cell printing and biofabrication. In: Proc’s Biomed. Eng. 2013. p. 451–455.Google Scholar
- 5.Zehnder T, Sarker B, Boccaccini A, et al. Evaluation of an alginate-gelatine crosslinked hydrogel for bioplotting. Biofabrication. 2015;7(2).Google Scholar
- 6.Otsu N. A threshold selection method from gray-level histograms. IEEE Trans Systems, Man & Cybernetics. 1979;9(1):62–66.Google Scholar
- 7.Tsai WH. Moment-preserving thresolding: A new approach. Computer Vision, Graphics & Image Processing. 1985;2(3):377–93.Google Scholar
- 8.Rezakhaniha R, et al. Experimental investigation of collagen waviness and orientation in the arterial adventitia using confocal laser scanning microscopy. Biomechanics & modeling in mechanobiology. 2011;11(7):461–73.Google Scholar
- 9.Podder P, Khan T, Khan MH, et al. Comparative performance analysis of hamming, hanning and blackman window. Int J Comp App_2014;96(18):1–7.Google Scholar
- 10.Liu Z. Scale space approach to directional analysis of images. Applied Optics. 1991;30(11):1369–73.Google Scholar