All Title Author
Keywords Abstract


Computer Aided Modeling and Dynamic Analysis of A New Surgical Instrument

DOI: 10.4236/ss.2012.35047, PP. 242-244

Keywords: Surgical Instrument, Mechanical Advantage, Suture, Occluding

Full-Text   Cite this paper   Add to My Lib

Abstract:

One major difference among surgical instruments is the level of bodily disruption and tissue trauma that surgical devices might cause the patients. This newly designed and developed surgical instrument aims at minimally invasive therapy procedure, more reliable and durable function, less operational force, and reduced manufacturing cost. The computer aided modeling and simulation have been applied to help this new instrument design and analysis. This improved new surgical instrument is designed to use in general surgery to prevent patient's vessels and tissues from being damaging due to reliable motion control of surgical clips with no unexpected clip drop. It can also be applied to surgical education purpose to educate medical students for their future surgical careers. The prototype testing indicated that the handle operational force to close surgical clips is lower than current surgical clip instruments, product manufacturing is cost-effective due to less dimensional tolerance control of this new instrument design, more reliable instrument function, and good mechanical advantage.

References

[1]  S. A. Reinsberg, S. J. Doran, E. M. Charles-Edwards and M. O. Leach, “A Complete Distortion Correction for MR Images: II. Rectification of Static-Field In homogeneities by Similarity-Based Profile Mapping,” Physics in Medicine and Biology, Vol. 50, No. 11, 2005, pp. 2651-2661. doi:10.1088/0031-9155/50/11/014
[2]  L. Zagorchev and A. Goshtasby, “A Comparative Study of Transformation Functions for Nonrigid Image Registration,” IEEE Transactions Image Processing, Vol. 15, No. 3, 2006, pp. 529-538. doi:10.1109/TIP.2005.863114
[3]  M. Takao, N. Sugano, T. Nishii, H. Miki, T. Koyama, J. Masumoto, Y. Sato, S. Tamura and H. Yoshikawa, “Application of 3D-MR Image Registration to Monitoring Diseases Around the Knee Joint,” Journal of Magnetic Resonance Imaging, Vol. 22, No. 5, 2005, pp. 656-660. doi:10.1002/jmri.20435
[4]  F. J. S. Castro, C. Pollo, R. Meuli, P. Maeder, O. Cuisenaire, M. B. Cuadra, J.-G. Villemure and J.-P. Thiran, “A Cross Validation Study of Deep Brain Stimulation Targeting: From Experts to Atlas-Based, Segmentation-Based and Automatic Registration Algorithms,” IEEE Transactions on Medical Imaging, Vol. 25, No. 11, 2006, pp. 1440-1450. doi:10.1109/TMI.2006.882129
[5]  P. Qiu, “Jump Surface Estimation, Edge Detection, and Image Restoration,” Journal of the American Statistical Association, Vol. 102, No. 478, 2007, pp. 745-756. doi:10.1198/016214507000000301

Full-Text

comments powered by Disqus