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Accelerated nonrigid intensity-based image registration using importance sampling.

Roshni Bhagalia1, Jeffrey A Fessler, Boklye Kim

  • 1Department of Electrical Engineering and ComputerScience, University of Michigan, Ann Arbor, MI 48109, USA. rbhagali@umich.edu

IEEE Transactions on Medical Imaging
|February 13, 2009
PubMed
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Importance sampling accelerates nonrigid image registration by reducing gradient computation time. This method, using edge-dependent adaptive sampling, speeds up intensity-based registration while maintaining accuracy in medical imaging.

Area of Science:

  • Medical image analysis
  • Computational anatomy
  • Scientific computing

Background:

  • Nonrigid image registration is crucial for estimating deformations in medical imaging.
  • Intensity-based similarity metrics and gradient optimization are common but computationally intensive for large datasets.
  • Stochastic gradient approximation using random voxel subsets can reduce computation time.

Purpose of the Study:

  • To introduce an importance sampling framework to reduce the variance of stochastic gradient approximations in nonrigid image registration.
  • To develop an edge-dependent adaptive sampling distribution for intensity-based registration algorithms.
  • To evaluate the efficiency and accuracy of importance sampling in accelerating registration.

Main Methods:

  • Implemented an importance sampling strategy for gradient approximation in nonrigid registration.

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  • Utilized an edge-dependent adaptive sampling distribution tailored for intensity-based metrics.
  • Compared stochastic approximation with and without importance sampling against deterministic gradient descent.
  • Tested on simulated MRI brain data and real CT lung data from eight subjects.

    • Main Results:

    • Importance sampling significantly reduced the variance of gradient approximations.
    • The combination of stochastic approximation and importance sampling accelerated the registration process.
    • Accuracy of registration was preserved compared to deterministic methods.
    • Demonstrated effectiveness on both simulated and real medical imaging datasets.

    Conclusions:

    • Importance sampling is an effective technique for accelerating nonrigid image registration.
    • The proposed edge-dependent adaptive sampling improves efficiency without compromising accuracy.
    • This approach offers a viable solution for computationally demanding registration tasks in medical imaging.