Accurate micro-computed tomography imaging of pore spaces in collagen-based scaffold

  • Jan ZidekEmail author
  • Lucy Vojtova
  • A. M. Abdel-Mohsen
  • Jiri Chmelik
  • Tomas Zikmund
  • Jana Brtnikova
  • Roman Jakubicek
  • Lukas Zubal
  • Jiri Jan
  • Jozef Kaiser
Tissue Engineering Constructs and Cell Substrates Original Research
Part of the following topical collections:
  1. Tissue Engineering Constructs and Cell Substrates


In this work we have used X-ray micro-computed tomography (μCT) as a method to observe the morphology of 3D porous pure collagen and collagen-composite scaffolds useful in tissue engineering. Two aspects of visualizations were taken into consideration: improvement of the scan and investigation of its sensitivity to the scan parameters. Due to the low material density some parts of collagen scaffolds are invisible in a μCT scan. Therefore, here we present different contrast agents, which increase the contrast of the scanned biopolymeric sample for μCT visualization. The increase of contrast of collagenous scaffolds was performed with ceramic hydroxyapatite microparticles (HAp), silver ions (Ag+) and silver nanoparticles (Ag-NPs). Since a relatively small change in imaging parameters (e.g. in 3D volume rendering, threshold value and μCT acquisition conditions) leads to a completely different visualized pattern, we have optimized these parameters to obtain the most realistic picture for visual and qualitative evaluation of the biopolymeric scaffold. Moreover, scaffold images were stereoscopically visualized in order to better see the 3D biopolymer composite scaffold morphology. However, the optimized visualization has some discontinuities in zoomed view, which can be problematic for further analysis of interconnected pores by commonly used numerical methods. Therefore, we applied the locally adaptive method to solve discontinuities issue. The combination of contrast agent and imaging techniques presented in this paper help us to better understand the structure and morphology of the biopolymeric scaffold that is crucial in the design of new biomaterials useful in tissue engineering.


Silver Nanoparticles Adaptive Method Porous Scaffold Collagen Scaffold Pure Collagen 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



This research was carried out under the project CEITEC 2020 (LQ1601) with financial support from the Ministry of Education, Youth and Sports of the Czech Republic under the National Sustainability Programme II.

Supplementary material

10856_2016_5717_MOESM1_ESM.pdf (983 kb)
Supplementary material 1 (PDF 983 kb) (30.2 mb)
Supplementary material 2 (ZIP 30880 kb) (16.4 mb)
Supplementary material 3 (ZIP 16805 kb)
10856_2016_5717_MOESM4_ESM.png (3.3 mb)
Supplementary material 4 (PNG 3351 kb)
10856_2016_5717_MOESM5_ESM.png (659 kb)
Supplementary material 5 (PNG 660 kb)


  1. 1.
    Oliveira SM, Ringshia RA, Legeros RZ, Clark E, Yost MJ, Terracio L, Teixeira CC. An improved collagen scaffold for skeletal regeneration. J Biomed Mater Res A. 2010;94:371–9. doi: 10.1002/jbm.a.32694.Google Scholar
  2. 2.
    Prosecka E, Rampichova M, Vojtova L, Tvrdik D, Melcakova S, Juhasova J, Plencner M, Jakubova R, Jancar J, Necas A, Kochova P, Klepacek J, Tonar Z, Amler E. Optimized conditions for mesenchymal stem cells to differentiate into osteoblasts on a collagen/hydroxyapatite matrix. J Biomed Mater Res A. 2011;99:307–15. doi: 10.1002/jbm.a.33189.CrossRefGoogle Scholar
  3. 3.
    Prosecka E, Rampichova M, Litvinec A, Tonar Z, Kralickova M, Vojtova L, Kochova P, Plencner M, Buzgo M, Mickova A, Jancar J, Amler E. Collagen/hydroxyapatite scaffold enriched with polycaprolactone nanofibers, thrombocyte-rich solution and mesenchymal stem cells promotes regeneration in large bone defect in vivo. J Biomed Mater Res A. 2015;103:671–82. doi: 10.1002/jbm.a.35216.CrossRefGoogle Scholar
  4. 4.
    Camp JJ, Hann CR, Johnson DH, Tarara JE, Robb RA. Three-dimensional reconstruction of aqueous channels in human trabecular meshwork using light microscopy and confocal microscopy. Scanning. 1997;19:258–63. doi: 10.1002/sca.4950190402.CrossRefGoogle Scholar
  5. 5.
    Oliveira AL, Malafaya PB, Costa SA, Sousa RA, Reis RL. Micro-computed tomography (μ-CT) as a potential tool to assess the effect of dynamic coating routes on the formation of biomimetic apatite layers on 3D-plotted biodegradable polymeric scaffolds. J Mater Sci. 2007;18:211–23. doi: 10.1007/s10856-006-0683-8.Google Scholar
  6. 6.
    Moore MJ, Jabbari E, Ritman EL, Lu L, Currier BL, Windebank AJ, Yaszemski MJ. Quantitative analysis of interconnectivity of porous biodegradable scaffolds with micro-computed tomography. J Biomed Mater Res A. 2004;71:258–67. doi: 10.1002/jbm.a.30138.CrossRefGoogle Scholar
  7. 7.
    Mather ML, Morgan SP, White LJ, Tai H, Kockenberger W, Howdle SM, Shakesheff KM, Crowe JA. Image-based characterization of foamed polymeric tissue scaffolds. Biomed Mater. 2008;3:015011. doi: 10.1088/1748-6041/3/1/015011.CrossRefGoogle Scholar
  8. 8.
    Taboas J, Maddox R, Krebsbach P, Hollister S. Indirect solid free form fabrication of local and global porous, biomimetic and composite 3D polymer–ceramic scaffolds. Biomaterials. 2003;24:181–94. doi: 10.1016/S0142-9612(02)00276-4.CrossRefGoogle Scholar
  9. 9.
    Hofmann S, Hagenmuller H, Koch AM, Miller R, Vunjak-Novakovic G, Kaplan DL, Merkle HP, Meinel L. Control of in vitro tissue-engineered bone-like structures using human mesenchymal stem cells and porous silk scaffolds. Biomaterials. 2007;28:1152–62. doi: 10.1016/j.biomaterials.2006.10.019.CrossRefGoogle Scholar
  10. 10.
    Meng J, Xiao B, Zhang Y, Liu J, Xue H, Lei J, Kong H, Huang Y, Jin Z, Gu N, Xu H. Super-paramagnetic responsive nanofibrous scaffolds under static magnetic field enhance osteogenesis for bone repair in vivo. Sci Rep. 2013;3:2655. doi: 10.1038/srep02655.Google Scholar
  11. 11.
    Kaiser J, Hola M, Galiova M, Novotny K, Kanicky V, Martinec P, Scucka J, Brun F, Sodini N, Tromba G, Mancini L, Koristkova T. Investigation of the microstructure and mineralogical composition of urinary calculi fragments by synchrotron radiation X-ray microtomography: a feasibility study. Urol Res. 2011;39:259–67. doi: 10.1007/s00240-010-0343-9.CrossRefGoogle Scholar
  12. 12.
    Momose A, Takeda T, Itai Y, Hirano K. Phase–contrast X-ray computed tomography for observing biological soft tissues. Nat Med. 1996;2:473–5. doi: 10.1038/nm0496-473.CrossRefGoogle Scholar
  13. 13.
    Bech M, Jensen TH, Feidenhans R, Bunk O, David C, Pfeiffer F. Soft-tissue phase-contrast tomography with an X-ray tube source. Phys Med Biol. 2009;54:2747–53. doi: 10.1088/0031-9155/54/9/010.CrossRefGoogle Scholar
  14. 14.
    Cedola A, Campi G, Pelliccia D, Bukreeva I, Fratini M, Burghammer M, Rigon L, Arfelli F, Chen RC, Dreossi D, Sodini N, Mohammadi S, Tromba G, Cancedda R, Mastrogiacomo M. Three dimensional visualization of engineered bone and soft tissue by combined X-ray micro-diffraction and phase contrast tomography. Phys Med Biol. 2014;59:189–201. doi: 10.1088/0031-9155/59/1/189.CrossRefGoogle Scholar
  15. 15.
    Komlev VS, Mastrogiacomo M, Peyrin F, Cancedda R, Rustichelli F. X-ray synchrotron radiation pseudo-holotomography as a new imaging technique to investigate angio- and microvasculogenesis with no usage of contrast agents. Tissue Eng Pt C. 2009;15:425–30. doi: 10.1089/ten.tec.2008.0428.CrossRefGoogle Scholar
  16. 16.
    Iassonov P, Gebrenegus T, Tuller M. Segmentation of X-ray computed tomography images of porous materials: a crucial step for characterization and quantitative analysis of pore structures. Water Resour Res. 2009;45:W09415. doi: 10.1029/2009WR008087.CrossRefGoogle Scholar
  17. 17.
    Van Aarle W, Batenburg KJ, Van Gompel G, Van de Casteele E, Sijbers J. Super-resolution for computed tomography based on discrete tomography. IEEE T Image Process. 2014;23:1181–93. doi: 10.1109/TIP.2013.2297025.CrossRefGoogle Scholar
  18. 18.
    Arbelaez P, Maire M, Fowlkes C, Malik J. Contour detection and hierarchical image segmentation. IEEE TPAMI. 2011;33:898–916. doi: 10.1109/TPAMI.2010.161.CrossRefGoogle Scholar
  19. 19.
    Miyazaki N, Esaki M, Ogura T, Murata K. Serial block-face scanning electron microscopy for three-dimensional analysis of morphological changes in mitochondria regulated by Cdc48p/p97 ATPase. J Struct Biol. 2014;187:187–93. doi: 10.1016/j.jsb.2014.05.010.CrossRefGoogle Scholar
  20. 20.
    Jones S, Boyde A, Pawley J. Osteoblasts and collagen orientation. Cell Tissue Res. 1975;159:73–80. doi: 10.1007/BF00231996.CrossRefGoogle Scholar
  21. 21.
    Wentz KU, Mattle HP, Edelman RR, Kleefield J, O’Reilly GV, Liu C, Zhao B. Stereoscopic display of MR angiograms. Neuroradiology. 1991;33:123–5. doi: 10.1007/BF00588249.CrossRefGoogle Scholar
  22. 22.
    Talukdar A, Wilson D. Modeling and optimization of rotational C-arm stereoscopic X-ray angiography. IEEE Trans Med Imaging. 1999;18:604–16. doi: 10.1109/42.790460.CrossRefGoogle Scholar
  23. 23.
    Stewart N, Lock G, Hopcraft A, Kanesarajah J, Coucher J. Stereoscopy in diagnostic radiology and procedure planning: does stereoscopic assessment of volume-rendered CT angiograms lead to more accurate characterisation of cerebral aneurysms compared with traditional monoscopic viewing. J Med Imaging Radiat Oncol. 2014;58:172–82. doi: 10.1111/1754-9485.12146.CrossRefGoogle Scholar
  24. 24.
    Coquard R, Rousseau B, Echegut P, Baillis D, Gomart H, Iacona E. Investigations of the radiative properties of Al–NiP foams using tomographic images and stereoscopic micrographs. Int J Heat Mass Trans. 2012;55:1606–19. doi: 10.1016/j.ijheatmasstransfer.2011.11.017.CrossRefGoogle Scholar
  25. 25.
    Chou PC, Porter MM, McKittrick J, Chen PY. Vapor deposition polymerization as an alternative method to enhance the mechanical properties of bio-inspired scaffolds. Ceram Trans. 2014;247:3–12.Google Scholar
  26. 26.
    Metscher BD. MicroCT for comparative morphology: simple staining methods allow high-contrast 3D imaging of diverse non-mineralized animal tissues. BMC Physiol. 2009;9:11. doi: 10.1186/1472-6793-9-11.CrossRefGoogle Scholar
  27. 27.
    Scheller EL, Troiano N, VanHoutan JN, Bouxsein MA, Fretz JA, Xi Y, Nelson T, Katz G, Berry R, Church CD, Doucette CR, Rodeheffer MS, MacDougald OA, Rosen CJ, Horowitz MC. Use of osmium tetroxide staining with microcomputerized tomography to visualize and quantify bone marrow adipose tissue in vivo. Method Enzymol. 2014;537:123–39. doi: 10.1016/B978-0-12-411619-1.00007-0.CrossRefGoogle Scholar
  28. 28.
    Cormode DP, Roessl E, Thran A, Skajaa T, Gordon RE, Schlomka J-P, Fuster V, Fisher EA, Mulder WJM, Proksa R, Fayad ZA. Atherosclerotic plaque composition: analysis with multicolor CT and targeted gold nanoparticles. Radiology. 2010;256:774–82. doi: 10.1148/radiol.10092473.CrossRefGoogle Scholar
  29. 29.
    Liu Y, Ai K, Liu J, Yuan Q, He Y, Lu L. Hybrid BaYbF 5 Nanoparticles: novel binary contrast agent for high-resolution in vivo X-ray computed tomography angiography. Adv Healthc Materials. 2012;1:461–6. doi: 10.1002/adhm.201200028.CrossRefGoogle Scholar
  30. 30.
    Seo SY, Lee GH, Lee SG, Jung SY, Lim JO, Choi JH. Alginate-based composite sponge containing silver nanoparticles synthesized in situ. Carbohyd Polym. 2012;90:109–15. doi: 10.1016/j.carbpol.2012.05.002.CrossRefGoogle Scholar
  31. 31.
    Khan A, El-Toni AM, Alrokayan S, Alsalhi M, Alhoshan M, Aldwayyan AS. Microwave-assisted synthesis of silver nanoparticles using poly-N-isopropylacrylamide/acrylic acid microgel particles. Colloid Surface A. 2011;377:356–60. doi: 10.1016/j.colsurfa.2011.01.042.CrossRefGoogle Scholar
  32. 32.
    Chakrabarty A, Maitra U. Organogels from dimeric bile acid esters. in situ formation of gold nanoparticles. J Phys Chem B. 2013;117:8039–46. doi: 10.1021/jp4029497.CrossRefGoogle Scholar
  33. 33.
    Cho EJ, Sun B, Wilson EM, Torregrosa-Allen S, Elzey BD, Yeo Y. Intraperitoneal delivery of platinum with in situ crosslinkable hyaluronic acid gel for local therapy of ovarian cancer. Biomaterials. 2015;2015(37):312–5. doi: 10.1016/j.biomaterials.2014.10.039.CrossRefGoogle Scholar
  34. 34.
    Ahmed M, Yamany S, Mohamed N, Farag A, Moriarty T. A modified fuzzy c-means algorithm for bias field estimation and segmentation of MRI data. IEEE Trans Med Imaging. 2002;21:193–9. doi: 10.1109/42.996338.CrossRefGoogle Scholar
  35. 35.
    Singh TR, Roy S, Singh OI, Sinam T, Singh KM. A new local adaptive thresholding technique in binarization. Int J Comput Sci Issues. 2011;8:271–7.Google Scholar
  36. 36.
    Otsu N. A threshold selection method from gray-level histograms. IEEE Trans Syst Man Cyb. 1979;9:62–6. doi: 10.1109/TSMC.1979.4310076.CrossRefGoogle Scholar
  37. 37.
    Karmazyn B, Liang Y, Klahr P, Jennings SG. Effect of tube voltage on ct noise levels in different phantom sizes. Am J Roentgenol. 2013;200:1001–5. doi: 10.2214/AJR.12.9828.CrossRefGoogle Scholar
  38. 38.
    Marin D, Nelson RC, Schindera ST, Richard S, Youngblood RS, Yoshizumi TT, Samei E. Low-tube-voltage, high-tube-current multidetector abdominal CT: improved image quality and decreased radiation dose with adaptive statistical iterative reconstruction algorithm—initial clinical experience. Radiology. 2010;254:145–53. doi: 10.1148/radiol.09090094.CrossRefGoogle Scholar
  39. 39.
    Nazarian A, Snyder BD, Zurakowski D, Müller R. Quantitative micro-computed tomography: a non-invasive method to assess equivalent bone mineral density. Bone. 2008;43:302–11. doi: 10.1016/j.bone.2008.04.009.CrossRefGoogle Scholar
  40. 40.
    Walton LA, Bradley RS, Withers PJ, Newton VL, Watson REB, Austin C, Sherratt MJ. Morphological Characterisation of unstained and intact tissue micro-architecture by X-ray computed micro- and nano-tomography. Sci Rep. 2015;5:10074. doi: 10.1038/srep10074.CrossRefGoogle Scholar
  41. 41.
    Ghani MU, Zhou Z, Ren L, Wong M, Li Y, Zheng B, Yang K, Liu H. Investigation of spatial resolution characteristics of an in vivo microcomputed tomography system. Nucl Instrum Methods Phys Res Sect A. 2016;807:129–36. doi: 10.1016/j.nima.2015.11.007.CrossRefGoogle Scholar
  42. 42.
    Pyka G, Kerckhofs G, Schrooten J, Wevers M. The effect of spatial micro-CT image resolution and surface complexity on the morphological 3D analysis of open porous structures. Mater Charact. 2014;87:104–15. doi: 10.1016/j.matchar.2013.11.004.CrossRefGoogle Scholar
  43. 43.
    van Loo D, Bouckaert L, Leroux O, Pauwels E, Dierick M, van Hoorebeke L, Cnudde V, de Neve S, Sleutel S. Contrast agents for soil investigation with X-ray computed tomography. Geoderma. 2014;213:485–91. doi: 10.1016/j.geoderma.2013.08.036.CrossRefGoogle Scholar
  44. 44.
    Singhana B, Chen A, Slattery P, Yazdi IK, Qiao Y, Tasciotti E, Wallace M, Huang S, Eggers M, Melancon MP. Infusion of iodine-based contrast agents into poly(p-dioxanone) as a radiopaque resorbable IVC filter. J Mater Sci Mater Med. 2015;26:124. doi: 10.1007/s10856-015-5460-0.CrossRefGoogle Scholar
  45. 45.
    Lusic H, Grinstaff MW. Review: X-ray-computed tomography contrast agents. Chem Rev. 2013;113:1641–66. doi: 10.1021/cr200358s.CrossRefGoogle Scholar
  46. 46.
    Shilo M, Reuveni T, Motiei M, Popovtzer R. Review: nanoparticles as computed tomography contrast agents: current status and future perspectives. Nanomedicine. 2012;7:257–69. doi: 10.2217/nnm.11.190.CrossRefGoogle Scholar
  47. 47.
    Baker KC, Maerz T, Saad H, Shaheen P, Kannan RM. In vivo bone formation by and inflammatory response to resorbable polymer-nanoclay constructs. Nanomed Nanotechnol Biol Med. 2015;11:1871–81. doi: 10.1016/j.nano.2015.06.012.CrossRefGoogle Scholar
  48. 48.
    Yang S, Zhang R, Qu X. Optimization and evaluation of metal injection molding by using X-ray tomography. Mater Charact. 2015;104:107–15. doi: 10.1016/j.matchar.2015.04.014.CrossRefGoogle Scholar
  49. 49.
    Xu F, Beyazoglu T, Hefner E, Gurkan UA, Demirci U. Automated and adaptable quantification of cellular alignment from microscopic images for tissue engineering applications. Tissue Eng Part C Methods. 2011;17:641–9. doi: 10.1089/ten.tec.2011.0038.CrossRefGoogle Scholar
  50. 50.
    Loh QL, Choong C. Three-dimensional scaffolds for tissue engineering applications: role of porosity and pore size. Tissue Eng Part B Rev. 2013;19:485–502. doi: 10.1089/ten.teb.2012.0437.CrossRefGoogle Scholar
  51. 51.
    Lee M, Wu BM, Dunn JCY. Effect of scaffold architecture and pore size on smooth muscle cell growth. J Biomed Mater Res, Part A. 2008;87A:1010–6. doi: 10.1002/jbm.a.31816.CrossRefGoogle Scholar
  52. 52.
    Chang HI, Wang Y (2011) Cell responses to surface and architecture of tissue engineering scaffolds. In: Eberli D (ed.) regenerative medicine and tissue engineering-cells and biomaterials. InTech. doi: 10.5772/21983

Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  • Jan Zidek
    • 1
    Email author
  • Lucy Vojtova
    • 1
    • 3
  • A. M. Abdel-Mohsen
    • 1
    • 4
  • Jiri Chmelik
    • 2
  • Tomas Zikmund
    • 1
  • Jana Brtnikova
    • 1
  • Roman Jakubicek
    • 2
  • Lukas Zubal
    • 1
  • Jiri Jan
    • 2
  • Jozef Kaiser
    • 1
  1. 1.CEITEC-Central European Institute of TechnologyBrno University of TechnologyBrnoCzech Republic
  2. 2.Institute of Biomedical Engineering, FEECBrno University of TechnologyBrnoCzech Republic
  3. 3.SCITEGBrnoCzech Republic
  4. 4.Textile Research DivisionNational Research CentreCairoEgypt

Personalised recommendations