Generation of planar radiographs from 3D
   anatomical models using the GPU
             André dos Santos Cardoso
        ...
Contents
 1   Introduction
        Context Overview
        Project’s Objective
 2   Why is it Important?
 3   Our Specific...
Context Overview



   Digitally Reconstructed Radiographs
   (DRRs)
       Taking a radiography from 3D digital
       an...
Context Overview



    DRRs are built from vertebrae models represented by 3D
    meshes
        DRR generation as mean t...
Project’s Objective



Build Fast DRR Algorithms
   DRR calculation is a bottleneck
       3D reconstruction usage in a
  ...
Why is it Important?

   DRR generation key component
   in many 2D/3D registration
   problems
       Allows to compare/u...
Why is it Important?

   Speed!

       Daily work on the field demands
       on-the-fly results, and high
       accuracy
...
Our Specific Case

   Where will the Optimized
   Algorithms fit?
        Shape Recovery of human
        spine – attaining ...
What has been done in this area?
 Attenuation Law – monochromatic x-ray radiation
             Nout (E ) = Nin (E ) × e − ...
Wrap-Up



   Attenuation law for bone material
   Few Applications of DRR to 3D Meshes (most work on CT
   data – voxels)...
Current Solution


    Inputs
        CAD model of vertebrae
        Camera position, object positions, object orientation...
Current Solution


   Ray Casting
       Multipass Algorithm
       Ray Casting and Depth
       Peeling
       GLSL
   Wh...
What’s expected?




    Enhance the current solution
     1   Modify code to implement the reported single-pass approach
...
Technologies – GLSL



   OpenGL Shading
   Language

      Allows the modification
      of fixed functionality of
      th...
Technologies – CUDA


   Compute Unified Device
   Architecture
      Parallel Computing Architecture
      Allows direct a...
Work Plan




            André dos Santos Cardoso   DRR Generation   16 / 19
Thank You for Listening!
                    Ask Away!




    André dos Santos Cardoso   DRR Generation   17 / 19
Bibliography
   The opengl shading language.
   Lisa Gottesfeld Brown.
   A survey of image registration techniques.
   AC...
Bibliography




 Full Bibliography Listed in:

  https://dev.andrecardoso.eu/bibtexbrowser.php?bib=
                     ...
Upcoming SlideShare
Loading in...5
×

Generation of planar radiographs from 3D anatomical models using the GPU

1,472

Published on

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

Published in: Technology, Art & Photos
0 Comments
0 Likes
Statistics
Notes
  • Be the first to comment

  • Be the first to like this

No Downloads
Views
Total Views
1,472
On Slideshare
0
From Embeds
0
Number of Embeds
1
Actions
Shares
0
Downloads
12
Comments
0
Likes
0
Embeds 0
No embeds

No notes for slide

Generation of planar radiographs from 3D anatomical models using the GPU

  1. 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. 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 André dos Santos Cardoso DRR Generation 2 / 19
  3. 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. 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 André dos Santos Cardoso DRR Generation 4 / 19
  5. 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! André dos Santos Cardoso DRR Generation 5 / 19
  6. 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 André dos Santos Cardoso DRR Generation 6 / 19
  7. 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 André dos Santos Cardoso DRR Generation 7 / 19
  8. 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] André dos Santos Cardoso DRR Generation 8 / 19
  9. 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 André dos Santos Cardoso DRR Generation 9 / 19
  10. 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) André dos Santos Cardoso DRR Generation 10 / 19
  11. 11. Current Solution Inputs CAD model of vertebrae Camera position, object positions, object orientation, … Outputs simulated Radiograph! André dos Santos Cardoso DRR Generation 11 / 19
  12. 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 André dos Santos Cardoso DRR Generation 12 / 19
  13. 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 André dos Santos Cardoso DRR Generation 13 / 19
  14. 14. Technologies – GLSL OpenGL Shading Language Allows the modification of fixed functionality of the GPU pipeline Similar syntax to C/C++ Modules called Shaders André dos Santos Cardoso DRR Generation 14 / 19
  15. 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 André dos Santos Cardoso DRR Generation 15 / 19
  16. 16. Work Plan André dos Santos Cardoso DRR Generation 16 / 19
  17. 17. Thank You for Listening! Ask Away! André dos Santos Cardoso DRR Generation 17 / 19
  18. 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. André dos Santos Cardoso DRR Generation 18 / 19
  19. 19. Bibliography Full Bibliography Listed in: https://dev.andrecardoso.eu/bibtexbrowser.php?bib= thesisbib.bib André dos Santos Cardoso DRR Generation 19 / 19
  1. A particular slide catching your eye?

    Clipping is a handy way to collect important slides you want to go back to later.

×