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Cluster of Workstation Based Non-rigid Image Registration Using Free-Form Deformation

Cluster of Workstation Based Non-rigid Image Registration Using Free-Form Deformation. Xiaofen Zheng , Jayaram Udupa , Xinjian Chen Medical Image Processing Group Department of Radiology University of Pennsylvania Feb 10, 2008 (4:30 – 4:50pm). Outline.

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Cluster of Workstation Based Non-rigid Image Registration Using Free-Form Deformation

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  1. Cluster of Workstation Based Non-rigid Image Registration Using Free-Form Deformation XiaofenZheng, JayaramUdupa, Xinjian Chen Medical Image Processing Group Department of Radiology University of Pennsylvania Feb 10, 2008 (4:30 – 4:50pm)

  2. Outline • 3D nonrigid registration method and its parallelization • Large image data sets • Parallel computing: cluster of workstations (COW) • Results • Time analysis: sequential vs. parallel

  3. Registration Algorithm • Successive 1-D filtering and reduction [Unser1993] Image pyramid Image pyramid B-spline coefficients Optimization Output computing

  4. Registration Algorithm Image pyramid B-spline coefficients Optimization Output computing

  5. Registration Algorithm Image pyramid • Thevenaz and Unser’s image model via cubic Bspline [Thévenaz 2000] B-spline coefficients • B-spline image representation and coefficients using 1-D recursive filters [Unser1991] Optimization Output computing

  6. Registration Algorithm Image pyramid B-spline coefficients • Analytic method of computing gradient of MI [Thévenaz 2000] Optimization • Stochastic gradient descent optimization [Klein 2007] Output computing

  7. Optimization • Derivative of Mutual Information (MI) [Thévenaz 2000]

  8. Registration Algorithm Image pyramid B-spline coefficients • Control points refinement between two levels [Maurer 2000] Optimization Output computing

  9. Registration Algorithm Image pyramid B-spline coefficients Optimization • Thevenaz and Unser’s image model via cubic Bspline [Thévenaz 2000] Output computing • Cubic B-spline Deformation [Mattes 2003]

  10. Experiment • 10 workstations (each has Pentium D 3.4 GHz CPU and 4 GB of main memory) through 1GB/s switch • Large CT image • Size : 512×512×459, voxel: 0.68×0.68×1.5 mm^3 • Control mesh: 27×27×52 (113,724) • 100 iteration of optimization in each level • Regular brain MRI image • Size : 256×256×46, voxel: 0.98×0.98×3 mm^3 • Control mesh: 27×27×15 (10,935) • 100 iteration of optimization in each level

  11. Time analysis (sequential vs. parallel) Scaled time comparison for sequential and parallel computing for each step on each level.

  12. Cumulative Time cost of sequential,parallel and combined solution in each step.

  13. Results (large image) Test image (known deformed image) Reference image (original CT image) Overlay test image with reference image Output image Overlay output image with reference image

  14. Results (regular image) • Overlay reference image with test image • Output image • Reference image (original brain MRI image) • Overlay reference image with output image • Test image (deformed image)

  15. Conclusion • Important to tackle time-critical clinical applications • A general parallel strategy • Complex interplay • Implemented in CAVASS software

  16. Reference • [Klein 2007] Stefan Klein, Marius Staring, Josien P.W. Pluim, “Evaluation of Optimization Methods for Nonrigid Medical Image Registration using Mutual Information and B-splines”, IEEE Transactions on Image Processing, vol. 16, pp. 2879-2890, 2007. • [Thévenaz 2000] Philippe Thévenaz, Michael Unser, “Optimization of Mutual Information for Multiresolution Image Registration”, IEEE Transactions on Image Processing, vol. 9, no. 12, pp. 2083-2099, December 2000. • [Unser1993] Michael Unser, AkramAldroubi, Murray Eden, “The L2 Polynomial Spline Pyramid”, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 15, no. 4, pp. 364-379, April 1993 • [Unser1991] Michael Unser, AkramAldroubi, Murray Eden, “Fast B-Spline Transforms for Continuous Image Representation and Interpolation”, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 13, no. 3, pp. 277-285, March 1991. • [Maurer2003] TorstenRohlfing, Calvin R. Maurer, “Nonrigid Image Registration in Shared-Memory Multiprocessor Environments with Application to Brains, Breasts, and Bees”, IEEE Transactions on Information Technology in Biomedicine, vol. 7, no. 1, pp. 16-25, March 2003. • [Rohlfing2001] TorstenRohlfing, Calvin R. Maurer, Walter G. O’Dell, JianhuiZhong, “Modeling liver motion and deformation during the respiratory cycle using intensity-based free-form registration of gated MR images”, SPIE Medical Imaging Conference Proceedings vol. 4319, pp. 337-348, 2001. • [Mattes 2003] Mattes, D., Haynor, D. R., Vesselle, H., Lewellen, T. K., and Eubank, W., “PET-CT image registration in the chest using free-form deformations,” IEEE Transactions on Medical Imaging 22(1), pp.120–128, 2003. • [Maurer 2001] Rohlfing, T., Maurer, C. R., ODell, W. G., and Zhong, J., “Modeling liver motion and deformation during the respiratory cycle using intensity-based free-form registration of gated MR images,” Medical Imaging, Proc. SPIE 4319, pp. 337–348, 2001.

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