Learning Statistical Correlation of Prostate Deformations for Fast Registration

  • Yonghong Shi
  • Shu Liao
  • Dinggang Shen
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7009)


This paper presents a novel fast registration method for aligning the planning image onto each treatment image of a patient for adaptive radiation therapy of the prostate cancer. Specifically, an online correspondence interpolation method is presented to learn the statistical correlation of the deformations between prostate boundary and non-boundary regions from a population of training patients, as well as from the online-collected treatment images of the same patient. With this learned statistical correlation, the estimated boundary deformations can be used to rapidly predict regional deformations between prostates in the planning and treatment images. In particular, the population-based correlation can be initially used to interpolate the dense correspondences when the number of available treatment images from the current patient is small. With the acquisition of more treatment images from the current patient, the patient-specific information gradually plays a more important role to reflect the prostate shape changes of the current patient during the treatment. Eventually, only the patient-specific correlation is used to guide the regional correspondence prediction, once a sufficient number of treatment images have been acquired and segmented from the current patient. Experimental results show that the proposed method can achieve much faster registration speed yet with comparable registration accuracy compared with the thin plate spline (TPS) based interpolation approach.


Adaptive radiation therapy Fast registration Patient-specific statistical correlation Canonical correlation analysis 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Yonghong Shi
    • 1
    • 2
  • Shu Liao
    • 1
  • Dinggang Shen
    • 1
  1. 1.IDEA Lab, Department of Radiology and BRICUniversity of North CarolinaChapel HillUSA
  2. 2.Digital Medical Research Center, Shanghai Key Lab of MICCAIFudan UniversityChina

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