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Some Applications of GPU-Based Medical Imaging

Some Applications of GPU-Based Medical Imaging. Baohua Wu. Roadmap. Introduction Medical imaging applications Decompression Registration Conclusion. Introduction to GPU-based Medical Imaging. Visualization Segmentation Registration Codec

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Some Applications of GPU-Based Medical Imaging

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  1. Some Applications of GPU-Based Medical Imaging Baohua Wu

  2. Roadmap • Introduction • Medical imaging applications • Decompression • Registration • Conclusion

  3. Introduction to GPU-based Medical Imaging • Visualization • Segmentation • Registration • Codec Source: GianlucaPaladini, State of the Art in GPU-Accelerated Techniques for Medical Imaging, GTC09

  4. Motivations • Challenges from medical imaging • Large volume of data (gigabytes to terabytes) • Processing time on CPU (minutes, hours or even days) • Limitations of some hardware • parallel computers • FPGA, dedicated devices • GPU’s emergence offers a solution

  5. Visualization of Medical Images • Automatic carving • 4D flow visualization • Diffusion tractography • Virtual endoscopy (ex. artery) • Virtual unfolding (ex. colon) • Tissue classification • Virtual mirrors • etc

  6. Image Segmentation • “Segmentation refers to the process of partitioning a digital image into multiple segments” – wikipedia.org Source: Gianluca Paladini, State of the Art in GPU-Accelerated Techniques for Medical Imaging, GTC09

  7. Image Registration Source: http://www.siam.org/meetings/op08/Modersitzki.pdf

  8. GPU-Accelerated Registration • Adaptive Radiation Therapy • Real-time ultrasound / CT registration

  9. Application 1 GPU-based Decompression for Medical Imaging Applications Albert Wegener GPU Technology Conference 2009

  10. Faster Imaging System

  11. Problems & Solutions • Serial coding with VLC (Variable Length Code) • Data are stored in packets that can be decoded in parallel • Small shared memory prevents storing one entire packet per thread • n symbols at a time • Conditionals lead to divergent warps • Replace conditionals with lookup tables

  12. Data-driven look-up table

  13. Application 2 Medical Image Registration with CUDA Richard Ansorge GTC 09

  14. Method • Deformation model: • Affine • B-spline • Search strategy • Simplex • Gradient descent • Cost function: • correlation coefficient • mutual information

  15. 2D histogram of intensities of two images • Source: F. E. M. S. Matthias Tessmann, Christian Eisenacher and P. Hastreiter. Gpu accelerated normalized mutual information and b-spline transformation. In Eurographics Workshop on Visual Computing for Biomedicine (EG VCBM), pages 117–124, 2008.

  16. Application 3 Fast deformable registration on the gpu: A cuda implementation of demons P. Muyan-Ozcelik, J. Owens, J. Xia, and S. Samant IEEE Conference on Computational Sciences andIts Applications, 2008

  17. Demons Algorithm Source: J.-P. Thirion, Image matching as a diffusion process: an analogy with Maxwell’s Demons, MIA 98

  18. Demons Algorithm • v: the displacement where S: the static image, M: the moving image, i: a position in the image • Similarity measure of Correlation Coefficient: where D: the deformed moving image

  19. Control flow graphof Demons algorithm • Source: X. Gu, H. Pan, Y. Liang, R. Castillo, D. Yang, D. Choi, E. Castillo, A. Majumdar, T. Guerrero, and S. B. Jiang. Implementation and evaluation of various demons deformable image registration algorithms on a gpu. Physics in Medicine and Biology, 55(1):207-219, 2010.

  20. CUDA Kernels

  21. Speedups

  22. Conclusion • GPU opens the prelude of a new era for medical imaging • Post-processing to real-time processing with speedups from tens to hundreds of times • More automated workflow in surgical operations • Interventional medical imaging • Adaptive radiation therapies

  23. Acknowledgement • Joseph T Kider Jr. • Jonathan McCaffrey • Gang Song • Dr. Brian Avants • Dr. James Gee

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