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Chest Modeling and Personalized Surgical Planning for Pectus Excavatum

  • Qian Zhao
  • Nabile Safdar
  • Chunzhe Duan
  • Anthony Sandler
  • Marius George Linguraru
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8673)

Abstract

Pectus excavatum is among the most common major congenital anomalies of the chest wall whose correction can be performed via minimally invasive Nuss technique that places a pectus bar to elevate the sternum anteriorly. However, the size and bending of the pectus bar are manually modeled intra-operatively by trial-and-error. The procedure requires intense pain management in the months following surgery. In response, we are developing a novel distraction device for incremental and personalized PE correction with minimal risk and pain, akin to orthodontic treatment using dental braces. To design the device, we propose in this study a personalized surgical planning framework for PE correction from clinical noncontrast CT. First, we segment the ribs and sternum via kernel graph cuts. Then costal cartilages, which have very low contrast in noncontrast CT, are modeled as 3D anatomical curves using the cosine series representation and estimated using a statistical shape model. The size and shape of the correction device are estimated through model fitting. Finally, the corrected/post-surgical chest is simulated in relation to the estimated shape of correction device. The root mean square mesh distance between the estimated cartilages and ground truth on 30 noncontrast CT scans was 1.28±0.81 mm. Our method found that the average deformation of the sterna and cartilages with the simulation of PE correction was 49.71±10.11 mm.

Keywords

Pectus excavatum personalized surgical planning costal cartilage estimation statistical shape models correction device 

References

  1. 1.
    Jaroszewski, D., et al.: Current Management of Pectus Excavatum: A Review and Update of Therapy and Treatment Recommendations. The Journal of the American Board of Family Medicine 23, 230–239 (2010)CrossRefGoogle Scholar
  2. 2.
    Moreira, A.H.J., et al.: Pectus excavatum postsurgical outcome based on preoperative soft body dynamics simulation. In: Proc. SPIE, pp. 83160K–83160K (2012)Google Scholar
  3. 3.
    Vilaça, J.L., et al.: Virtual simulation of the postsurgical cosmetic outcome in patients with Pectus Excavatum. In: Proc. SPIE, pp. 79642L–79642L (2011)Google Scholar
  4. 4.
    Lai, J.-Y., et al.: The measurement and designation of the pectus bar by computed tomography. Journal of Pediatric Surgery 44, 2287–2290 (2009)CrossRefGoogle Scholar
  5. 5.
    Vilaça, J.L., et al.: Automatic Prebent Customized Prosthesis for Pectus Excavatum Minimally Invasive Surgery Correction. Surgical Innovation (2013)Google Scholar
  6. 6.
    Wei, Y., et al.: Pectus Excavatum Nuss Orthopedic finite element simulation. In: Biomedical Engineering and Informatics (BMEI), pp. 1236–1239 (2010)Google Scholar
  7. 7.
    Zhao, Q., et al.: Estimation of Cartilaginous Region in Noncontrast CT of the Chest. In: Proc. SPIE (in press, 2014)Google Scholar
  8. 8.
    Salah, M.B., et al.: Multiregion Image Segmentation by Parametric Kernel Graph Cuts. IEEE Transactions on Image Processing 20, 545–557 (2011)CrossRefMathSciNetGoogle Scholar
  9. 9.
    Fang, Q., Boas, D.A.: Tetrahedral mesh generation from volumetric binary and grayscale images. In: ISBI 2009, pp. 1142–1145 (2009)Google Scholar
  10. 10.
    Vollmer, J., et al.: Improved Laplacian Smoothing of Noisy Surface Meshes. Computer Graphics Forum 18, 131–138 (1999)CrossRefGoogle Scholar
  11. 11.
    Au, O.K.-C., et al.: Skeleton extraction by mesh contraction. ACM Trans. Graph. 27, 1–10 (2008)CrossRefGoogle Scholar
  12. 12.
    Chung, M.K., et al.: Cosine series representation of 3D curves and its application to white matter fiber bundles in diffusion tensor imaging. Statistics and Its Interface 3, 69–80 (2010)CrossRefzbMATHMathSciNetGoogle Scholar
  13. 13.
    Zhao, Q., Okada, K., Rosenbaum, K., Zand, D.J., Sze, R., Summar, M., Linguraru, M.G.: Hierarchical Constrained Local Model Using ICA and Its Application to Down Syndrome Detection. In: Mori, K., Sakuma, I., Sato, Y., Barillot, C., Navab, N. (eds.) MICCAI 2013, Part II. LNCS, vol. 8150, pp. 222–229. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  14. 14.
    Gower, J.C.: Generalized procrustes analysis. Psychometrika 40, 33–51 (1975)CrossRefzbMATHMathSciNetGoogle Scholar
  15. 15.
    Bookstein, F.L.: Principal warps: thin-plate splines and thes decomposition of deformations. PAMI 11, 567–585 (1989)CrossRefzbMATHGoogle Scholar
  16. 16.
    Puri, B., et al.: Nuss procedure for pectus excavatum - An early experience. Medical Journal Armed Forces India 59, 316–319 (2003)CrossRefGoogle Scholar
  17. 17.
    Besl, P.J., McKay, N.D.: A method for registration of 3-D shapes. PAMI 14, 239–256 (1992)CrossRefGoogle Scholar
  18. 18.
    Myronenko, A., Xubo, S.: Point Set Registration: Coherent Point Drift. PAMI 32, 2262–2275 (2010)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Qian Zhao
    • 1
  • Nabile Safdar
    • 1
  • Chunzhe Duan
    • 1
  • Anthony Sandler
    • 2
  • Marius George Linguraru
    • 1
    • 3
  1. 1.Sheikh Zayed Institute for Pediatric Surgical InnovationChildren’s National Medical CenterWashingtonUSA
  2. 2.Department of General and Thoracic SurgeryChildren’s National Medical CenterWashingtonUSA
  3. 3.School of Medicine and Health SciencesGeorge Washington UniversityWashingtonUSA

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