Ultrasound-Fluoroscopy Registration for Intraoperative Dynamic Dosimetry in Prostate Brachytherapy

  • Ehsan Dehghan
  • Nathanael Kuo
  • Anton Deguet
  • Yi Le
  • Elwood Armour
  • E. Clif Burdette
  • Danny Y. Song
  • Gabor Fichtinger
  • Jerry L. Prince
  • Junghoon Lee


Low-dose-rate prostate brachytherapy is a treatment option for low- and mid-risk prostate cancer through introduction of radioactive seeds into the prostate. Seed placement deviations are common and associated with postoperative complications. Dynamic dosimetry is a method to accurately localize the true position of the seeds inside the tissue, calculate the delivered dose, and adapt the implant plan accordingly to compensate for seed placement deviations in the operating room. A practical method for dynamic dosimetry relies on localization of the implanted seeds in 3D space from several C-arm images and registering them to a 3D ultrasound volume of the prostate region. In this chapter we introduce a system and workflow for intraoperative dosimetry for prostate brachytherapy. In the suggested workflow, C-arm images are acquired from different angles and are used to reconstruct the seeds in 3D space. For this purpose, we rely on a method based on dimensionality reduced linear programming to match the projections of a seed in different images and localize the seed positions after automatic C-arm pose correction. In the next step of the workflow, the reconstructed seeds are registered to an ultrasound volume of the prostate in a point-to-volume registration scheme. We tested our method on data from 16 patients and compared our dosimetry results with results from Day-1 CT. In comparison, we achieved absolute error of 2.2 ± 1.8 % (mean ± STD) in estimating the percentage of the prostate volume that receives 100 % of the prescribed dose (V100) and absolute error of 10.5 ± 9.5 % in prediction of the minimum dose delivered to 90 % of the prostate (D90).


Prostate Volume Registration Algorithm TRUS Probe Ultrasound Volume Prostate Brachytherapy 
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.



This research was supported in part by the National Institute of Health/National Cancer Institute (NIH/NCI) under grant 2R44CA099374 and grant 1R01CA151395, and in part by the Department of Defense (DOD) under grant W81XWH-05-1-0407. The first author was with the Queen’s University and the Johns Hopkins University while carrying out this research and was supported in part by the Ontario Ministry of Research and Innovation Postdoctoral Fellowship.


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Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  • Ehsan Dehghan
    • 1
  • Nathanael Kuo
    • 2
  • Anton Deguet
    • 3
  • Yi Le
    • 4
  • Elwood Armour
    • 4
  • E. Clif Burdette
    • 5
  • Danny Y. Song
    • 4
  • Gabor Fichtinger
    • 6
  • Jerry L. Prince
    • 7
  • Junghoon Lee
    • 4
  1. 1.Clinical Informatics, Interventional, and Translational Solutions (CIITS)Philips Research North AmericaBriarcliff ManorUSA
  2. 2.Department of Biomedical EngineeringJohns Hopkins UniversityBaltimoreUSA
  3. 3.Laboratory for Computational Sensing and Robotics, Whiting School of EngineeringJohns Hopkins UniversityBaltimoreUSA
  4. 4.Department of Radiation Oncology and Molecular Radiation SciencesJohns Hopkins UniversityBaltimoreUSA
  5. 5.Acoustic MedSystems, Inc.SavoyUSA
  6. 6.School of ComputingQueen’s UniversityKingstonCanada
  7. 7.Department of Electrical and Computer EngineeringJohns Hopkins UniversityBaltimoreUSA

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