Journal of Biomedical Engineering Research (대한의용생체공학회:의공학회지)

The Korean Society of Medical and Biological Engineering (대한의용생체공학회)
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Trabecular bone Thickness Measurement of Rat Femurs using Zoom-in Micro-tomography and 3D Fuzzy Distance Transform

Zoom-in Micro-tomography와 3차원 Fuzzy Distance Transform을 이용한 쥐 대퇴부의 해면골 두께 측정

  • 박정진 (경희대학교 동서의료공학과) ;
  • 조민형 (경희대학교 동서의료공학과) ;
  • 이수열 (경희대학교 동서의료공학과)
  • Published : 2006.08.01
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Abstract

Micro computed tomography (micro-CT) has been used for in vivo animal study owing to its noninvasive and high spatial resolution capability. However, the sizes of existing detectors for micro-CT systems are too small to obtain whole-body images of a small animal object with $\sim$10 micron resolution and a part of its bones or other organs should be extracted. So, we have introduced the zoom-in micro-tomography technique which can obtain high-resolution images of a local region of an live animal object without extracting samples. In order to verify our zoom-in technique, we performed in vivo animal bone study. We prepared some SD (Sprague-Dawley) rats for making osteoporosis models. They were divided into control and ovariectomized groups. Again, the ovariectomized group is divided into two groups fed with normal food and with calcium-free food. And we took 3D tomographic images of their femurs with 20 micron resolution using our zoom-in tomography technique and observed the bone changes for 12 weeks. We selected ROI (region of interest) of a femur image and applied 2D FDT (fuzzy distance transform) to measure the trabecular bone thickness. The measured results showed obvious bone changes and big differences between control and ovariectomized groups. However, we found that the reliability of the measurement depended on the selection of ROI in a bone image for thickness calculation. So, we extended the method to 3D FDT technique. We selected 3D VOI (volume of interest) in the obtained 3D tomographic images and applied 3D FDT algorithm. The results showed that the 3D technique could give more accurate and reliable measurement.

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References

  1. T. Hildebrand and P. Ruegsegger, 'A new method for the model-independent assessment ofthickness in three-dimensional images,' J. Micros., vol. 185, pt. 1, pp. 67-75,1997 https://doi.org/10.1046/j.1365-2818.1997.1340694.x
  2. J. S. Thomsen, A. Laib, B. Koller, S. Prohaska, LI. Mosekilde and W. Gowin, 'Stereological measures of trabecular bone structure: comparison of 3D micro computed tomography with 2D histological sections in human proximal tibial bone biopsies,' J. Micros., vol. 218, pt. 2, pp. 171-179,2005 https://doi.org/10.1111/j.1365-2818.2005.01469.x
  3. A. Odgaard, 'Three-dimensional methods for quantification of cancellous bone architecture,' Bone, vol. 20, no. 4, pp. 315-328, 1997 https://doi.org/10.1016/S8756-3282(97)00007-0
  4. A. Barbier, C. Martel, M. C. De Vemejoulm F. Tirode, M. Nys, G. Mocaer, C. Morieux, H. Murakami and F. Lacheretz, 'The visualization and evaluation of bone architecture in the rat using three-dimensional X-ray microcomputed tomography,' J. Bone and Min. Research, vol. 17, pp 37-44, 1999 https://doi.org/10.1007/s007740050061
  5. P. Ruegsegger, B. Koller and R. Muller, 'A microtomographic system for the nondestructive evaluation of bone architecture', Calcified Tissue Int. Vol. 58 pp. 24-29, 1996 https://doi.org/10.1007/BF02509542
  6. Kinney JH, Lane NE, Haupt DL., 'In vivo, three-dimensional microscopy of trabecular bone,' J. Bone and Min. Research, vol.10, pp264-270, 1995
  7. I. K. Chun, M. H. Cho, S. C. Lee, M. H. Cho and S. Y. Lee, 'X-ray micro-tomography system for small-animal imaging with zoom-in imaging capability,' Phys. Med. BioI., vol. 49, pp. 3889-3902, 2004 https://doi.org/10.1088/0031-9155/49/17/005
  8. A. M. Parfitt, M. K. Drezner, F. H. Glorieux, J. A. Kanis, H. Malluche, P. J. Meunier, S. M. Ott and R. R. Recker, 'Bone histomorphometry: standardization of nomenclature, symbols and units,' J. Bone and Min. Research, vol. 2, no. 6 pp. 595-610, 1987
  9. D. Ulrich, B. van Rietbergen, A. Laib and P. Ruegsegger, 'The ability ofthree-dimensional structural indices to reflect mechanical aspects oftrabecular bone,' Bone, vol. 25, no. 1, pp. 55-60,1999 https://doi.org/10.1016/S8756-3282(99)00098-8
  10. I. K. Chun, M. H. Cho, J. H. Park and Y. Lee, 'In vivo trabecular thickness measurement in cancellous bones: longitudinal rat imaging studies,' Phys. Med. BioI., vol. 27, pp. 695-702, 2006
  11. M. H. Cho, I, K. Chun, S. C. Lee, M. H. Cho, J. H. Park and Y. Lee, 'Trabecular thickness measurement in cancellous bones: postmortem rat studies with the zoom-in micro-tomography technique,' Phys. Med. BioI., vol. 26, no. 5, pp. 667-676,2005
  12. S. C. Lee, H. K. Kim, J. K. Chun, S. Y. Lee and M. H. Cho, 'A flat panel detector based micro-CT system: performance evaluation for small-animal imaging,' Phys. Med. BioI., vol. 48, pp. 4173-4185,2003 https://doi.org/10.1088/0031-9155/48/24/014
  13. L. A. Feldkamp, L. C. Davis and J. W. Kress, 'Practical cone -beam algorithm,' J. Opt. Soc. Am. A, vol. 1, no. 6 pp.612-619, 1984 https://doi.org/10.1364/JOSAA.1.000612
  14. P. K. Saha and F. W. Wehrli, 'Measurement of trabecular bone thickness in the limited resolution region of in vivo MRl by fuzzy distance transform,' IEEE Trans. Med. Imag., vol. 23, pp. 53-62, 2004 https://doi.org/10.1109/TMI.2003.819925
  15. P. K. Saha and F. W. Wehrli, 'Fuzzy distance transform: theory, algorithms, and applications' Comput. Vis. Image Und., vol. 88, pp.171-190,2002
  16. I. Ragnemalm, 'The Euclidean distance transform in arbitrary dimensions,' Pattern Recognition, vol. 14, pp. 883-888, 1993 https://doi.org/10.1016/0167-8655(93)90152-4
  17. C. M. Ma and M. Sonka, 'A fully parallel 3D thinning algorithm and its applications,' Comput. Vis. Image Und., vol. 64, no. 3, pp. 420-433,1996 https://doi.org/10.1006/cviu.1996.0069
  18. E. L. William and E. C. Harvey, 'Marching cubes: a high resolution 3D surface construction algorithm,' Com. Graphics, vol. 21, no. 4, pp. 163-169,1987 https://doi.org/10.1145/37402.37422
  19. D. A. Rajon and W. E. Bolch, 'Marching cube algorithm: review and trilinear interpolation adaptation for image-based dosimetric modes,' Com. Med. Imag. And Graphics, vol. 27, pp. 411-435, 2003 https://doi.org/10.1016/S0895-6111(03)00032-6
  20. R. C. Gonzalez and R. E. Woods, Digital image processing, Addison-Wesley Publishing Company, Chap. 8, 1993, pp. 491-494