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

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Master Thesis first presentation on DRR generation.

Master Thesis first presentation on DRR generation.

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  • 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 May 10, 2010 André dos Santos Cardoso DRR Generation 1 / 11
  • 2. Contents 1 Introduction Context Overview Project’s Objective 2 State of the Art 3 Detailed Objectives Technologies 4 Work Plan 5 Bibliography André dos Santos Cardoso DRR Generation 2 / 11
  • 3. Context Overview Digitally Reconstructed Radiographs (DRRs) Taking a radiography from 3D digital anatomical models – vertebrae models in this case Form of depth peeling, using ray-casting Key component in 2D/3D registration process André dos Santos Cardoso DRR Generation 3 / 11
  • 4. Context Overview DRRs are taken from vertebrae models built with 3D meshes DRR generation as mean to validate and/or correct the reconstructed 3D models Vertebrae Shape Recovery Using 2D/3D Non-Rigid Registration Important techniques for Scoliosis treatment and follow-ups Volume recovery using Biplanar Radiography Techniques Alternatives to MRIs and CTs André dos Santos Cardoso DRR Generation 4 / 11
  • 5. Project’s Objective Build Fast DRR Algorithms DRR calculation is a bottleneck 3D reconstruction usage in a daily basis requires high performances Take advantage of processing power of new GPUs Common workstations could do the job! André dos Santos Cardoso DRR Generation 5 / 11
  • 6. State of the Art Algorithms are variations of depth peeling using ray-casting, and 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 examples exist (no DRR examples) André dos Santos Cardoso DRR Generation 6 / 11
  • 7. Detailed Objectives Enhance the existing solution Implement Single Pass Algorithm using GLSL Technology Implement Single Pass Algorithm using CUDA Technology Compare and evaluate attained solutions with existing approaches André dos Santos Cardoso DRR Generation 7 / 11
  • 8. Technologies C/C++ programming using OpenGL and CUDA Intended solution working both on Windows and *nix systems Visual Studio 2008 / Vim :) Possible packaging of solution as open-source library GLSL is part of the OpengGL standard provides mechanism to change graphics pipeline, using shaders CUDA is a Nvidia proprietary technology Nvidia’s CUDA SDK provides C/C++ extensions to execute paralell code directly on the GPU André dos Santos Cardoso DRR Generation 8 / 11
  • 9. Work Plan André dos Santos Cardoso DRR Generation 9 / 11
  • 10. Thank You for Listening! Ask Away! André dos Santos Cardoso DRR Generation 10 / 11
  • 11. Bibliography Cass Everitt. Interactive order-independent transparency. NVIDIA OpenGL Applications Engineering. 05/15/2001. Accessed in April 29, 2010. http://developer.nvidia.com/object/Interactive_Order_Transparency.html. Fang Liu, Meng-Cheng Huang, Xue-Hui Liu, and En-Hua Wu. Freepipe: a programmable parallel rendering architecture for efficient multi-fragment effects. In I3D ’10: Proceedings of the 2010 ACM SIGGRAPH symposium on Interactive 3D Graphics and Games, pages 75–82, New York, NY, USA, 2010. ACM. 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, Jorge G. Barbosa, João Manuel R. S. Tavares, and Ana M. Reis. Calibration of Bi-planar Radiography with a Rangefinder and a Small Calibration Object, pages 572–581. Springer Berlin / Heidelberg, 2008. 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. Daniel Russakoff, Torsten Rohlfing, Daniel Rueckert, Ramin Shahidi, Daniel Kim, Daniel Kima, Calvin R. Maurer, and Jr. Fast calculation of digitally reconstructed radiographs using light fields, 2003. 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, June 2009. André dos Santos Cardoso DRR Generation 11 / 11

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