Transcript 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
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Visualization
Segmentation
Registration
Codec
Source: Gianluca Paladini, State of the Art in GPU-Accelerated
Techniques for Medical Imaging, GTC09
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
Visualization of Medical Images
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Automatic carving
4D flow visualization
Diffusion tractography
Virtual endoscopy (ex. artery)
Virtual unfolding (ex. colon)
Tissue classification
Virtual mirrors
etc
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
Image Registration
Source: http://www.siam.org/meetings/op08/Modersitzki.pdf
GPU-Accelerated Registration
• Adaptive Radiation Therapy
• Real-time ultrasound / CT registration
Application 1
GPU-based Decompression for Medical Imaging
Applications
Albert Wegener
GPU Technology Conference 2009
Faster Imaging System
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
Data-driven look-up table
Application 2
Medical Image Registration with CUDA
Richard Ansorge
GTC 09
Method
• Deformation model:
– Affine
– B-spline
• Search strategy
– Simplex
– Gradient descent
• Cost function:
– correlation coefficient
– mutual information
2D histogram of intensities of two
images
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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
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 and
Its Applications, 2008
Demons Algorithm
Source: J.-P. Thirion, Image matching as a diffusion process: an analogy
with Maxwell’s Demons, MIA 98
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
• 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.
CUDA Kernels
Speedups
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
Acknowledgement
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Joseph T Kider Jr.
Jonathan McCaffrey
Gang Song
Dr. Brian Avants
Dr. James Gee