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Design and Validation of an Image-Guided Robot for Small Animal Research

  • Peter Kazanzides
  • Jenghwa Chang
  • Iulian Iordachita
  • Jack Li
  • C. Clifton Ling
  • Gabor Fichtinger
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4190)

Abstract

We developed an image-guided robot system to achieve highly accurate placement of thin needles and probes into in-vivo rodent tumor tissue in a predefined pattern that is specified on a preoperative image. This system can be used for many experimental procedures where the goal is to correlate a set of physical measurements with a corresponding set of image intensities or, more generally, to perform a physical action at a set of anatomic points identified on a preoperative image. This paper focuses on the design and validation of the robot system, where the first application is to insert oxygen measurement probes in a three-dimensional (3D) grid pattern defined with respect to a PET scan of a tumor. The design is compatible with CT and MRI, which we plan to use to identify targets for biopsy and for the injection of adenoviral sequences for gene therapy. The validation is performed using a phantom and includes a new method for estimating the Fiducial Localization Error (FLE) based on the measured Fiducial Distance Error (FDE).

Keywords

Positron Emission Tomography Positron Emission Tomography Image Robot System Target Registration Error Tissue Oxygen Tension 
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.

References

  1. 1.
    Urano, M., Chen, Y., Humm, J., Koutcher, J., Zanzonico, P., Ling, C.: Measurements of tumor tissue oxygen tension using a time-resolved luminescence-based optical oxylite probe: Comparison with a paired survival assay. Radiation Research 158, 167–173 (2002)CrossRefGoogle Scholar
  2. 2.
    Cherry, S., Shao, Y., et al.: MicroPET: A high resolution PET scanner for imaging small animals. IEEE Trans. on Nuclear Science 44, 1161–1166 (1997)CrossRefGoogle Scholar
  3. 3.
    Maurer, C., Fitzpatrick, J., Wang, M., Galloway, R., Maciunas, R., Allen, G.: Registration of head volume images using implantable fiducial markers. IEEE Trans. on Medical Imaging 16, 447–462 (1997)CrossRefGoogle Scholar
  4. 4.
    Kazanzides, P., Zuhars, J., Mittelstadt, B., Taylor, R.: Force sensing and control for a surgical robot. In: IEEE Intl. Conf. on Robotics and Automation, Nice, France, pp. 612–617 (1992)Google Scholar
  5. 5.
    Press, W., Teukolsky, S., Vetterling, W., Flannery, B.: Numerical Recipes in C, 2nd edn., vol. 1. Cambridge University Press, Cambridge (1992)MATHGoogle Scholar
  6. 6.
    Arun, K., Huang, T., Blostein, S.: Least-squares fitting of two 3-D point sets. IEEE Trans. on Pattern Analysis and Machine Intelligence 9, 698–700 (1987)CrossRefGoogle Scholar
  7. 7.
    Umeyama, S.: Least-squares estimation of transformation parameters between two point patterns. IEEE Trans. Pattern Anal. and Mach. Intell. 13, 376–380 (1991)CrossRefGoogle Scholar
  8. 8.
    Fitzpatrick, J., West, J., Maurer, C.: Predicting error in rigid-body point-based registration. IEEE Trans. on Medical Imaging 17, 694–702 (1998)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Peter Kazanzides
    • 1
  • Jenghwa Chang
    • 3
  • Iulian Iordachita
    • 1
  • Jack Li
    • 2
  • C. Clifton Ling
    • 3
  • Gabor Fichtinger
    • 1
  1. 1.Department of Computer ScienceJohns Hopkins UniversityUSA
  2. 2.Department of Mechanical EngineeringJohns Hopkins UniversityUSA
  3. 3.Medical Physics DepartmentMemorial Sloan Kettering Cancer CenterUSA

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