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Restoration of Thickness, Density, and Volume for Highly Blurred Thin Cortical Bones in Clinical CT Images

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Abstract

In clinical CT images containing thin osseous structures, accurate definition of the geometry and density is limited by the scanner’s resolution and radiation dose. This study presents and validates a practical methodology for restoring information about thin bone structure by volumetric deblurring of images. The methodology involves 2 steps: a phantom-free, post-reconstruction estimation of the 3D point spread function (PSF) from CT data sets, followed by iterative deconvolution using the PSF estimate. Performance of 5 iterative deconvolution algorithms, blind, Richardson–Lucy (standard, plus Total Variation versions), modified residual norm steepest descent (MRNSD), and Conjugate Gradient Least-Squares were evaluated using CT scans of synthetic cortical bone phantoms. The MRNSD algorithm resulted in the highest relative deblurring performance as assessed by a cortical bone thickness error (0.18 mm) and intensity error (150 HU), and was subsequently applied on a CT image of a cadaveric skull. Performance was compared against micro-CT images of the excised thin cortical bone samples from the skull (average thickness 1.08 ± 0.77 mm). Error in quantitative measurements made from the deblurred images was reduced 82% (p < 0.01) for cortical thickness and 55% (p < 0.01) for bone mineral mass. These results demonstrate a significant restoration of geometrical and radiological density information derived for thin osseous features.

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References

  1. Baino, F. Biomaterials and implants for orbital floor repair. Acta Biomater. 7:3248–3266, 2011.

    Article  CAS  PubMed  Google Scholar 

  2. Bardsley, J. M. Applications of a nonnegatively constrained iterative method with statistically based stopping rules to CT, PET, and SPECT imaging. Electron. Trans. Numer. Anal. 38:34–43, 2011.

    Google Scholar 

  3. Bardsley, J. M., and J. G. Nagy. Covariance-preconditioned iterative methods for nonnegatively constrained astronomical imaging. SIAM J. Matrix Anal. Appl. 27:1184–1197, 2006.

    Article  Google Scholar 

  4. Berisha, S., and J. Nagy. Iterative methods for image restoration. Sig. Process. 4 Image: 1–59, 2013 http://books.google.com/books?hl=en&lr=&id=QJ3HqmLG8gIC&oi=fnd&pg=PA193&dq=Iterative+Methods+for+Image+Restoration&ots=RHpzfDVSi1&sig=srDAMuvYRwHsdCQ4_Gq7tfCFK_Q.

  5. Boone, J. M. Determination of the presampled MTF in computed tomography. Med. Phys. 28:356–360, 2001.

    Article  CAS  PubMed  Google Scholar 

  6. Chang, P. S.-H., T. H. Parker, C. W. Patrick, and M. J. Miller. The accuracy of stereolithography in planning craniofacial bone replacement. J. Craniofac. Surg. 14:164–170, 2003.

    Article  PubMed  Google Scholar 

  7. Dey, N., L. Blanc-Feraud, C. Zimmer, P. Roux, Z. Kam, J.-C. Olivo-Marin, and J. Zerubia. Richardson–Lucy algorithm with total variation regularization for 3D confocal microscope deconvolution. Microsc. Res. Tech. 69:260–266, 2006.

    Article  PubMed  Google Scholar 

  8. Erlandsson, K., I. Buvat, P. H. Pretorius, B. A. Thomas, and B. F. Hutton. A review of partial volume correction techniques for emission tomography and their applications in neurology, cardiology and oncology. Phys. Med. Biol. 57:R119–R159, 2012.

    Article  PubMed  Google Scholar 

  9. Fish, D. A., J. G. Walker, A. M. Brinicombe, and E. R. Pike. Blind deconvolution by means of the Richardson–Lucy algorithm. J. Opt. Soc. Am. A 12:58, 1995.

    Article  Google Scholar 

  10. Geleijns, J., and R. Irwan. Radiation Dose from Multidetector CTPractical Approaches to Dose Reduction: Toshiba Perspective, Berlin: Springer, 2012.

    Google Scholar 

  11. Gervaise, A., B. Osemont, S. Lecocq, A. Noel, E. Micard, J. Felblinger, and A. Blum. CT image quality improvement using Adaptive Iterative Dose Reduction with wide-volume acquisition on 320-detector CT. Eur. Radiol. 22:295–301, 2012.

    Article  PubMed  Google Scholar 

  12. Hansen, P. C., J. G. Nagy, and D. O’Leary. Deblurring Images: Matrices, Spectra, and Filtering. Fundamentals of Algorithms, Vol. 3. Philadelphia: SIAM, 2006.

    Book  Google Scholar 

  13. Helgason, B., F. Taddei, E. Schileo, L. Cristofolini, M. Viceconti, H. Pálsson, and S. Brynjólfsson. A modified method for assigning material properties to FE models of bones. Med. Eng. Phys. 30:444–453, 2008.

    Article  PubMed  Google Scholar 

  14. Hsieh, J. Computed Tomography (2nd ed.). Bellingham: SPIE, 2009. doi:10.1117/3.817303.

    Google Scholar 

  15. Hsieh, J., B. Nett, Z. Yu, K. Sauer, J.-B. Thibault, and C. A. Bouman. Recent advances in CT image reconstruction. Curr. Radiol. Rep. 1:39–51, 2013.

    Article  Google Scholar 

  16. Irwan, B., S. Nakanishi, and A. Blum. AIDR 3D–reduces dose and simultaneously improves image quality. Toshiba Med. Syst. Whitepaper 1–8, 2012. http://scholar.google.com/scholar?hl=en&btnG=Search&q=intitle:AIDR+3D+-+Reduces+Dose+and+Simultaneously+Improves+Image+Quality#0.

  17. Jiang, M., G. Wang, M. W. Skinner, J. T. Rubinstein, and M. W. Vannier. Blind deblurring of spiral CT images. IEEE Trans. Med. Imaging 22:837–845, 2003.

    Article  PubMed  Google Scholar 

  18. Kawata, Y., Y. Nakaya, N. Niki, H. Ohmatsu, K. Eguchi, M. Kaneko, and N. Moriyama. Measurement of three-dimensional point spread functions in multidetector-row CT., 2008.

  19. Kobayashi, F., O. Sasaki, S. Nakajima, and J. Ito. Measurement of layer thickness using spread width of longitudinal image in helical CT. Oral Radiol. 15:85–93, 1999.

    Article  Google Scholar 

  20. Lagendijk, R. L., J. Biemond, and D. E. Boekee. Identification and restoration of noisy blurred images using the expectation-maximization algorithm. IEEE Trans. Acoust. 38:1180–1191, 1990.

    Article  Google Scholar 

  21. Lohfeld, S., V. Barron, and P. E. McHugh. Biomodels of bone: a review. Ann. Biomed. Eng. 33:1295–1311, 2005.

    Article  CAS  PubMed  Google Scholar 

  22. Lucy, L. B. An iterative technique for the rectification of observed distributions. Astron. J. 79:745, 1974.

    Article  Google Scholar 

  23. Maloul, A., J. Fialkov, and C. Whyne. The impact of voxel size-based inaccuracies on the mechanical behavior of thin bone structures. Ann. Biomed. Eng. 39:1092–1100, 2011.

    Article  PubMed  Google Scholar 

  24. Meijering, E. H. W., W. J. Niessen, J. P. W. Pluim, and M. A. Viergever. Quantitative comparison of sinc-approximating kernels for medical image interpolation., 1999.

  25. Meinel, J. F., G. Wang, M. Jiang, T. Frei, M. Vannier, and E. Hoffman. Spatial variation of resolution and noise in multi-detector row spiral CT. Acad. Radiol. 10:607–613, 2003.

    Article  PubMed  Google Scholar 

  26. Mustafa, S. F., P. L. Evans, A. Bocca, D. W. Patton, A. W. Sugar, and P. W. Baxter. Customized titanium reconstruction of post-traumatic orbital wall defects: a review of 22 cases. Int. J. Oral Maxillofac. Surg. 40:1357–1362, 2011.

    Article  CAS  PubMed  Google Scholar 

  27. Newman, D. L., G. Dougherty, A. AlObaid, and H. AlHajrasy. Limitations of clinical CT in assessing cortical thickness and density. Phys. Med. Biol. 43:619–626, 1998.

    Article  CAS  PubMed  Google Scholar 

  28. Nunery, W. R., P. J. Timoney, and H. B. H. Lee. General Principles of Management of Orbital Fractures. In: Smith and Nesi’s Ophthalmic Plastic and Reconstructive Surgery, edited by E. H. Black, F. A. Nesi, G. J. Gladstone, and M. R. Levine. New York: Springer, 2012. doi:10.1007/978-1-4614-0971-7.

    Google Scholar 

  29. Ohkubo, M., S. Wada, A. Kayugawa, T. Matsumoto, and K. Murao. Image filtering as an alternative to the application of a different reconstruction kernel in CT imaging: feasibility study in lung cancer screening. Med. Phys. 38:3915, 2011.

    Article  PubMed  Google Scholar 

  30. Pakdel, A., J. G. Mainprize, N. Robert, J. Fialkov, and C. M. Whyne. Model-based PSF and MTF estimation and validation from skeletal clinical CT images. Med. Phys. 41:011906, 2014.

    Article  PubMed  Google Scholar 

  31. Pakdel, A., J. G. Mainprize, N. Robert, J. Fialkov, and C. M. Whyne. Model-based PSF and MTF estimation and validation from skeletal clinical CT images. Med. Phys. 41:11906, 2014.

    Article  Google Scholar 

  32. Pakdel, A., N. Robert, J. Fialkov, A. Maloul, and C. Whyne. Generalized method for computation of true thickness and x-ray intensity information in highly blurred sub-millimeter bone features in clinical CT images. Phys. Med. Biol. 57:8099–8116, 2012.

    Article  PubMed  Google Scholar 

  33. Papadopoulos, M. A., P. K. Christou, P. K. Christou, A. E. Athanasiou, P. Boettcher, H. F. Zeilhofer, R. Sader, and N. A. Papadopulos. Three-dimensional craniofacial reconstruction imaging. Oral Surg. Oral Med. Oral Pathol. Oral Radiol. Endodontol. 93:382–393, 2002.

    Article  Google Scholar 

  34. Pinheiro, M., and J. L. Alves. A new level-set based protocol for accurate bone segmentation from CT imaging. ArXiv e-prints, 2015.

  35. Prevrhal, S., K. Engelke, and W. A. Kalender. Accuracy limits for the determination of cortical width and density: the influence of object size and CT imaging parameters. Phys. Med. Biol. 44:751–764, 1999.

    Article  CAS  PubMed  Google Scholar 

  36. Richardson, W. H. Bayesian-based iterative method of image restoration. J. Opt. Soc. Am. 62:55, 1972.

    Article  Google Scholar 

  37. Rollano-Hijarrubia, E., R. Manniesing, and W. J. Niessen. Selective deblurring for improved calcification visualization and quantification in carotid CT angiography: validation using micro-CT. IEEE Trans. Med. Imaging 28:446–453, 2009.

    Article  PubMed  Google Scholar 

  38. Schmit, G. Deconvolution in 3D: An ImageJ Plugin. Masters Thesis at Biomedical Image Group, École Polytechnique Fédérale de Lausanne, 2007.

  39. Schneider, C. A., W. S. Rasband, and K. W. Eliceiri. NIH Image to ImageJ: 25 years of image analysis. Nat. Methods 9:671–675, 2012.

    Article  CAS  PubMed  Google Scholar 

  40. Schwarzband, G., and N. Kiryati. The point spread function of spiral CT. Phys. Med. Biol. 50:5307–5322, 2005.

    Article  PubMed  Google Scholar 

  41. Silva, A. C., H. J. Lawder, A. Hara, J. Kujak, and W. Pavlicek. Innovations in CT dose reduction strategy: application of the adaptive statistical iterative reconstruction algorithm. AJR. Am. J. Roentgenol. 194:191–199, 2010.

    Article  PubMed  Google Scholar 

  42. Starck, J. L., E. Pantin, and F. Murtagh. Deconvolution in astronomy: a review. Publ. Astron. Soc. Pacific 114:1051–1069, 2002.

    Article  Google Scholar 

  43. Szwedowski, T. D. T. D. Development and validation of a subject-specific finite element model of the human craniofacial skeleton., 2007. http://www.csa.com/partners/viewrecord.php?requester=gs&amp;collection=TRD&amp;recid=9113853MT.

  44. Thibault, J.-B., K. D. Sauer, C. A. Bouman, and J. Hsieh. A three-dimensional statistical approach to improved image quality for multislice helical CT. Med. Phys. 34:4526, 2007.

    Article  PubMed  Google Scholar 

  45. Treece, G. M., A. H. Gee, P. M. Mayhew, and K. E. S. Poole. High resolution cortical bone thickness measurement from clinical CT data. Med. Image Anal. 14:276–290, 2010.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Varghese, B., D. Short, R. Penmetsa, T. Goswami, and T. Hangartner. Computed-tomography-based finite-element models of long bones can accurately capture strain response to bending and torsion. J. Biomech. 44:1374–1379, 2011.

    Article  PubMed  Google Scholar 

  47. Vio, R., J. Bardsley, and W. Wamsteker. Least-squares methods with Poissonian noise: analysis and comparison with the Richardson-Lucy algorithm. Astron. Astrophys. 436:741–755, 2005.

    Article  Google Scholar 

  48. Vogel, C. R. Computational Methods for Inverse Problems. Philadelphia: SIAM, 2002.

    Book  Google Scholar 

  49. Wang, G., M. W. Vannier, M. W. Skinner, M. G. P. Cavalcanti, and G. W. Harding. Spiral CT image deblurring for cochlear implantation. IEEE Trans. Med. Imaging 17:251–262, 1998.

    Article  CAS  PubMed  Google Scholar 

  50. Westin, C. F., A. Bhalerao, H. Knutsson, and R. Kikinis. Using local 3D structure for segmentation of bone from computer tomography images. Comput. Vis. Pattern Recogn. 1997. doi:10.1109/CVPR.1997.609418.

    Google Scholar 

  51. Wildberger, J. E., A. H. Mahnken, T. Flohr, R. Raupach, C. Weiss, R. W. Günther, and S. Schaller. Spatial domain image filtering in computed tomography: feasibility study in pulmonary embolism. Eur. Radiol. 13:717–723, 2003.

    PubMed  Google Scholar 

  52. Zannoni, C., R. Mantovani, and M. Viceconti. Material properties assignment to finite element models of bone structures: a new method. Med. Eng. Phys. 20:735–740, 1998.

    Article  CAS  PubMed  Google Scholar 

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Acknowledgements

This study was funded by the Natural Science and Engineering Research Council of Canada and the Ontario Graduate Scholarship.

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Authors and Affiliations

  1. Institute of Biomaterials and Biomedical Engineering, University of Toronto, Toronto, ON, Canada

    Amirreza Pakdel & Cari Whyne

  2. Sunnybrook Health Sciences Center, Toronto, ON, Canada

    Michael Hardisty, Jeffrey Fialkov & Cari Whyne

  3. Faculty of Medicine, University of Toronto, Toronto, ON, Canada

    Cari Whyne

  4. Division of Plastic and Reconstructive Surgery, University of Toronto, Toronto, ON, Canada

    Jeffrey Fialkov

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  1. Amirreza Pakdel
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Correspondence to Cari Whyne.

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Associate Editor Agata A. Exner oversaw the review of this article.

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Pakdel, A., Hardisty, M., Fialkov, J. et al. Restoration of Thickness, Density, and Volume for Highly Blurred Thin Cortical Bones in Clinical CT Images. Ann Biomed Eng 44, 3359–3371 (2016). https://doi.org/10.1007/s10439-016-1654-y

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  • DOI: https://doi.org/10.1007/s10439-016-1654-y

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