A Learning-Based Patient Repositioning Method from Limited-Angle Projections

  • Chen-Rui Chou
  • C. Brandon Frederick
  • Sha X. Chang
  • Stephen M. Pizer
Part of the Advances in Intelligent and Soft Computing book series (AINSC, volume 83)


This paper presents a novel patient repositioning method from limitedangle tomographic projections. It uses a machine learning strategy. Given a single planning CT image (3D) of a patient, one applies patient-specific training. Using the training results, the planning CT image, and the raw image projections collected at the treatment time, our method yields the difference between the patient’s treatmenttime postition and orientation and the planning-time position and orientation. In the training, one simulates credible treatment-time movements for the patient, and by regression it formulates a multiscale model that expresses the relationship giving the patient’s movements as a function of the corresponding changes in the tomographic projections. When the patient’s real-time projection images are acquired at treatment time, their differences from corresponding projections of the planning-time CT followed by applications of the calculated model allows the patient’s movements to be estimated. Using that estimation, the treatment-time 3D image can be estimated by transforming the planning CT image with the estimated movements,and from this, changes in the tomographic projections between those computed from the transformed CT and the real-time projection images can be calculated. The iterative, multiscale application of these steps converges to the repositioning movements. By this means, this method can overcome the deficiencies in limited-angle tomosynthesis and thus assist the clinician performing an image-guided treatment. We demonstrate the method’s success in capturing patients’ rigid motions with subvoxel accuracy with noise-added projection images of head and neck CTs.


Projection Image Rigid Transformation Active Appearance Model Digital Tomosynthesis Simultaneous Algebraic Reconstruction Technique 
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.


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  1. 1.
    Maltz, J.S., Sprenger, F., Fuerst, J., Paidi, A., Fadler, F., Bani-Hashemi, A.R.: Fixed gantry tomosynthesis system for radiation therapy image guidance based on a multiple source x-ray tube with carbon nanotube cathodes. Medical Physics 36(5), 1624–1636 (2009)CrossRefGoogle Scholar
  2. 2.
    Andersen, A.H., Kak, A.C.: Simultaneous algebraic reconstruction technique (SART): a superior implementation of the ART algorithm. Ultrasonic Imaging 6(1), 81–94 (1984)CrossRefGoogle Scholar
  3. 3.
    Sadowsky, O., Ramamurthi, K., Ellingsen, L.M., Chintalapani, G., Prince, J.L., Taylor, R.H.: Atlas-assisted tomography: registration of a deformable atlas to compensate for limited-angle cone-beam trajectory. In: 3rd IEEE International Symposium on Biomedical Imaging: Nano to Macro, pp. 1244–1247 (2006)Google Scholar
  4. 4.
    Thilmann, C., Nill, S., Tucking, T., Hoss, A., Hesse, B., Dietrich, L., Bendl, R., Rhein, B., Haring, P., Thieke, C.: Correction of patient positioning errors based on in-line cone beam CTs: clinical implementation and first experiences. Radiation Oncology 1(1), 16 (2006)CrossRefGoogle Scholar
  5. 5.
    Wu, Q.J., Godfrey, D.J., Wang, Z., Zhang, J., Zhou, S., Yoo, S., Brizel, D.M., Yin, F.F.: On-board patient positioning for head-and-neck IMRT: comparing digital tomosynthesis to kilovoltage radiography and cone-beam computed tomography. International Journal of Radiation Oncology, Biology, Physics 69(2), 598–606 (2007)Google Scholar
  6. 6.
    Yoo, S., et al.: Clinical evaluation of positioning verification using digital tomosynthesis (DTS) based on bony anatomy and soft tissues for prostate image-guided radiation therapy (IGRT). International Journal of Radiation Oncology, Biology, Physics 73(1), 296 (2009)Google Scholar
  7. 7.
    Zhang, J., Wu, Q.J., Godfrey, D.J., Fatunase, T., Marks, L.B., Yin, F.F.: Comparing digital tomosynthesis to Cone-Beam CT for position verification in patients undergoing partial breast irradiation. International Journal of Radiation Oncology, Biology, Physics 73(3), 952–957 (2009)Google Scholar
  8. 8.
    Russakoff, D., Rohlfing, T., Maurer, C.: Fast intensity-based 2D-3D image registration of clinical data using light. In: Proceedings. Ninth IEEE International Conference on Computer Vision, vol. 1, pp. 416–422 (2003)Google Scholar
  9. 9.
    Cootes, T.F., Edwards, G.J., Taylor, C.J.: Active appearance models. IEEE Transactions on Pattern Analysis and Machine Intelligence 23(6), 681–685 (2001)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Chen-Rui Chou
    • 1
  • C. Brandon Frederick
    • 2
  • Sha X. Chang
    • 3
  • Stephen M. Pizer
    • 4
  1. 1.Department of Computer ScienceUniversity of North Carolina at Chapel HillChapel HillUSA
  2. 2.Department of Biomedical EngineeringUniversity of North Carolina at Chapel HillChapel HillUSA
  3. 3.Department of Radiation OncologyUniversity of North Carolina at Chapel HillChapel HillUSA
  4. 4.Departments of Computer Science and Radiation OncologyUniversity of North Carolina at Chapel HillChapel HillUSA

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