Transcript Cluster of Workstation Based Image Registration Using Free

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
 3D nonrigid registration method and its parallelization
 Large image data sets
 Parallel computing: cluster of workstations (COW)
 Results
 Time analysis: sequential vs. parallel
Registration Algorithm
Image pyramid
B-spline coefficients
Optimization
Output computing
Successive 1-D filtering and
reduction [Unser1993]
Registration Algorithm
Image pyramid
B-spline coefficients
Optimization
Output computing
Registration Algorithm
Image pyramid
B-spline coefficients
Optimization
Output computing
Thevenaz and Unser’s image model
via cubic Bspline [Thévenaz 2000]
B-spline image representation and
coefficients using 1-D recursive
filters [Unser1991]
Registration Algorithm
Image pyramid
B-spline coefficients
Optimization
Output computing
Analytic method of computing
gradient of MI [Thévenaz 2000]
Stochastic gradient descent
optimization [Klein 2007]
Optimization
 Derivative of Mutual Information (MI) [Thévenaz 2000]
Registration Algorithm
Image pyramid
B-spline coefficients
Optimization
Output computing
Control points refinement between
two levels [Maurer 2000]
Registration Algorithm
Image pyramid
B-spline coefficients
Optimization
Output computing
Thevenaz and Unser’s image model
via cubic Bspline [Thévenaz 2000]
Cubic B-spline Deformation
[Mattes 2003]
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
Time analysis (sequential vs. parallel)
Scaled time comparison for sequential and parallel computing
for each step on each level.
Cumulative Time cost of sequential, parallel and combined
solution in each step.
Results (large image)
Overlay
Overlay
Reference
Test image
output
test image
(known
Output
image
(original
with
with
image
deformed
reference
reference
CT image)
image
image
Results (regular image)
Reference
Overlay
Overlay
Test
reference
reference
image
image
Output
(original
(deformed
image
image
image
with
brain
with
image)
output
test
MRIimage
image)
image
Conclusion
 Important to tackle time-critical clinical applications
 A general parallel strategy
 Complex interplay
 Implemented in CAVASS software
Reference
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[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, Akram Aldroubi, 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, Akram Aldroubi, 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] Torsten Rohlfing, 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] Torsten Rohlfing, Calvin R. Maurer, Walter G. O’Dell, Jianhui Zhong, “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.