Second PDIS presentation at FEUP. Master's Thesis presentation.

1. Generation of planar radiographs from 3D
anatomical models using the GPU
André dos Santos Cardoso
Supervisor: Jorge M. G. Barbosa
University of Porto
Faculty of Engineering of University of Porto
andre.cardoso@fe.up.pt, jbarbosa@fe.up.pt
July 14, 2010
André dos Santos Cardoso DRR Generation 1 / 19

2. Contents
1 Introduction
Context Overview
Project’s Objective
2 Why is it Important?
3 Our Specific Case
4 What Has Been Done?
Wrap-Up
5 Current Solution
6 What’s expected?
7 Involved Technologies
GLSL
CUDA
8 Work Plan
9 Bibliography
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3. Context Overview
Digitally Reconstructed Radiographs
(DRRs)
Taking a radiography from 3D digital
anatomical models of vertebrae
Form of depth peeling, using
ray-casting
Key component in 2D/3D registration
process
André dos Santos Cardoso DRR Generation 3 / 19

4. Context Overview
DRRs are built from vertebrae models represented by 3D
meshes
DRR generation as mean to validate and/or refine 3D
reconstructions of the spine from multi-planar radiography
Vertebrae Shape Recovery Using 2D/3D Non-Rigid
Registration
Important techniques for Scoliosis therapy and follow-ups
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5. Project’s Objective
Build Fast DRR Algorithms
DRR calculation is a bottleneck
3D reconstruction usage in a
routine clinical environment
requires high performances
Take advantage of processing
power of new GPUs and APIs
Common workstations could do
the job!
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6. Why is it Important?
DRR generation key component
in many 2D/3D registration
problems
Allows to compare/use data
from different sources and times
together
The CAS model versus real-time
imagery from the patient
Nonrigid registration
Many applications in medical area
CAS, Radiotherapy, Volume
Recovery
Known to be a common bottleneck
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7. Why is it Important?
Speed!
Daily work on the field demands
on-the-fly results, and high
accuracy
Advantages on using GPUs
versus Hardware solutions
Cheaper
More accessible
General Purpose Computing on
GPUs gaining increasing interest
from researchers
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8. Our Specific Case
Where will the Optimized
Algorithms fit?
Shape Recovery of human
spine – attaining a 3D model of
the spine
Bi-planar Radiography
Scoliosis evaluation
Viable alternative to MRIs and
CTs – why?
Expensive, Amount of
Radiation, Prolonged
Procedures, Require lying
down
Not the scope of this project!!
Moura, D. et al [6]
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9. What has been done in this area?
Attenuation Law – monochromatic x-ray radiation
Nout (E ) = Nin (E ) × e − µ(E ,ρ(x ),Z (x ))dx
(1)
Focus on GPU Implementations!
Monte Carlo Volume Shear-Warping
Rendering Viewing transformations
Volumetric integral for Splatting
each pixel Throw voxels into the
Fourier Volume Rendering viewing pane
Inverse 2D Fourier Ray Casting
transform of a slice Shoot rays to each pixel
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10. Wrap-Up
Attenuation law for bone material
Few Applications of DRR to 3D Meshes (most work on CT
data – voxels)
Using OpenGL Shading Language (GLSL)
Multi Pass Algorithm is available
Single Pass Algorithm is considered the state of the art, but
no applied implementation exists
Compute Unified Device Architecture (CUDA) peeling
applications exist (but not for computing DRRs)
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11. Current Solution
Inputs
CAD model of vertebrae
Camera position, object positions, object orientation, …
Outputs
simulated Radiograph!
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12. Current Solution
Ray Casting
Multipass Algorithm
Ray Casting and Depth
Peeling
GLSL
Why use CAD models?
Problem context requires
deformations to the 3D
model
Faster to deform CAD
models
Generally, decreases
amount of computation
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13. What’s expected?
Enhance the current solution
1 Modify code to implement the reported single-pass approach
2 Port solution to CUDA
3 Test and compare solutions
Significant speed-ups are expected
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14. Technologies – GLSL
OpenGL Shading
Language
Allows the modification
of fixed functionality of
the GPU pipeline
Similar syntax to C/C++
Modules called Shaders
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15. Technologies – CUDA
Compute Unified Device
Architecture
Parallel Computing Architecture
Allows direct access to parallel
processors and memory
Kernel function executed on
GPU device
Allows hierarchical
configuration of threads upon
kernel launch
Massive data parallelism
Allows versatile and more
controlled programming
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16. Work Plan
André dos Santos Cardoso DRR Generation 16 / 19

17. Thank You for Listening!
Ask Away!
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18. Bibliography
The opengl shading language.
Lisa Gottesfeld Brown.
A survey of image registration techniques.
ACM Comput. Surv., 24(4):325–376, 1992.
David B. Kirk and Wen mei W. Hwu.
Programming Massively Parallel Processors - A Hands-on Approach.
Morgan Kaufmann, 2010.
A. Mitulescu, W. Skalli, D. Mitton, and J. A. De Guise.
Three-dimensional surface rendering reconstruction of scoliotic vertebrae using a non stereo-corresponding
points technique.
European Spine Journal, 2002.
Shinichiro Mori, Masanao Kobayashi, Motoki Kumagai, and Shinichi Minohara.
Development of a gpu-based multithreaded software application to calculate digitally reconstructed radiographs
for radiotherapy.
Radiological Physics and Technology, 2009.
Daniel C. Moura, Jonathan Boisvert, Jorge G. Barbosa, and João Manuel Tavares.
Fast 3d reconstruction of the spine using user-defined splines and a statistical articulated model.
In ISVC ’09: Proceedings of the 5th International Symposium on Advances in Visual Computing, pages
586–595, Berlin, Heidelberg, 2009. Springer-Verlag.
Scott D. Roth.
Ray casting for modeling solids.
j-CGIP, 18(2), 1982.
F. P. Vidal, M.Garnier, N. Freud, J. M. Létang, and N.W. John.
Simulation of x-ray attenuation on the gpu.
In Proceeding of TCPG’09 - Theory and Practice of Computer Graphics, pages 25–32. Eurographics, 2009.
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19. Bibliography
Full Bibliography Listed in:
https://dev.andrecardoso.eu/bibtexbrowser.php?bib=
thesisbib.bib
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