Xiaofen Zheng, Jayaram Udupa, Xinjian Chen Medical Image Processing Group Department of Radiology University of Pennsylvania Feb 10, 2008 (4:30 – 4:50pm)

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Presentation transcript:

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

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 B-spline coefficients Image pyramid Optimization Output computing Successive 1-D filtering and reduction [Unser1993] Image pyramid

Registration Algorithm B-spline coefficients Image pyramid Optimization Output computing

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

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

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

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) Reference image (original CT image)Test image (known deformed image)Overlay test image with reference imageOutput imageOverlay output image with reference image

Results (large image) Reference image (original CT image)Test image (known deformed image)Overlay test image with reference imageOutput imageOverlay output image with reference image

Results (regular image) Reference image (original brain MRI image)Test image (deformed image)Overlay reference image with test imageOutput imageOverlay reference image with output image

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

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 , [Thévenaz 2000] Philippe Thévenaz, Michael Unser, “Optimization of Mutual Information for Multiresolution Image Registration”, IEEE Transactions on Image Processing, vol. 9, no. 12, pp , December [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 , 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 , March [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 , March [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 , [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, [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.