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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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Presentation Transcript
  • Introduction
  • Medical imaging applications
    • Decompression
    • Registration
  • Conclusion
introduction to gpu based medical imaging
Introduction to GPU-based Medical Imaging
  • Visualization
  • Segmentation
  • Registration
  • Codec

Source: GianlucaPaladini, State of the Art in GPU-Accelerated Techniques for Medical Imaging, GTC09

  • 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
visualization of medical images
Visualization of Medical Images
  • Automatic carving
  • 4D flow visualization
  • Diffusion tractography
  • Virtual endoscopy (ex. artery)
  • Virtual unfolding (ex. colon)
  • Tissue classification
  • Virtual mirrors
  • etc
image segmentation
Image Segmentation
  • “Segmentation refers to the process of partitioning a digital image into multiple segments” –

Source: Gianluca Paladini, State of the Art in GPU-Accelerated Techniques for Medical Imaging, GTC09

image registration
Image Registration


gpu accelerated registration
GPU-Accelerated Registration
  • Adaptive Radiation Therapy
  • Real-time ultrasound / CT registration
application 1
Application 1

GPU-based Decompression for Medical Imaging Applications

Albert Wegener

GPU Technology Conference 2009

problems solutions
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
application 2
Application 2

Medical Image Registration with CUDA

Richard Ansorge

GTC 09

  • Deformation model:
    • Affine
    • B-spline
  • Search strategy
    • Simplex
    • Gradient descent
  • Cost function:
    • correlation coefficient
    • mutual information
2d histogram of intensities of two images
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.
application 3
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

demons algorithm
Demons Algorithm

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

demons algorithm1
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

control flow graph of demons algorithm
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.
  • 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
  • Joseph T Kider Jr.
  • Jonathan McCaffrey
  • Gang Song
  • Dr. Brian Avants
  • Dr. James Gee