generation of planar radiographs from 3d anatomical models using the gpu

Generation of planar radiographs from 3Danatomical models using the GPU

André dos Santos CardosoSupervisor: Jorge M. G. Barbosa

University of PortoFaculty of Engineering of University of Porto

[email protected], [email protected]

July 14, 2010

André dos Santos Cardoso DRR Generation 1 / 19

Contents1 Introduction

Context OverviewProject’s Objective

2 Why is it Important?3 Our Specific Case4 What Has Been Done?

Wrap-Up5 Current Solution6 What’s expected?7 Involved Technologies

GLSLCUDA

8 Work Plan9 Bibliography


Context Overview

Digitally Reconstructed Radiographs(DRRs)

Taking a radiography from 3D digitalanatomical models of vertebraeForm of depth peeling, usingray-casting

Key component in 2D/3D registrationprocess


Context Overview

DRRs are built from vertebrae models represented by 3Dmeshes

DRR generation as mean to validate and/or refine 3Dreconstructions of the spine from multi-planar radiography

Vertebrae Shape Recovery Using 2D/3D Non-RigidRegistration

Important techniques for Scoliosis therapy and follow-ups


Project’s Objective

Build Fast DRR AlgorithmsDRR calculation is a bottleneck

3D reconstruction usage in aroutine clinical environmentrequires high performances

Take advantage of processingpower of new GPUs and APIs

Common workstations could dothe job!


Why is it Important?

DRR generation key componentin many 2D/3D registrationproblems

Allows to compare/use datafrom different sources and timestogetherThe CAS model versus real-timeimagery from the patientNonrigid registration

Many applications in medical area

CAS, Radiotherapy, VolumeRecovery

Known to be a common bottleneck


Why is it Important?

Speed!

Daily work on the field demandson-the-fly results, and highaccuracyAdvantages on using GPUsversus Hardware solutions

CheaperMore accessible

General Purpose Computing onGPUs gaining increasing interestfrom researchers


Our Specific Case

Where will the OptimizedAlgorithms fit?

Shape Recovery of humanspine – attaining a 3D model ofthe spineBi-planar Radiography

Scoliosis evaluationViable alternative to MRIs andCTs – why?

Expensive, Amount ofRadiation, ProlongedProcedures, Require lyingdown

Not the scope of this project!!

Moura, D. et al [6]


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 VolumeRendering

Volumetric integral foreach pixel

Fourier Volume RenderingInverse 2D Fouriertransform of a slice

Shear-WarpingViewing transformations

SplattingThrow voxels into theviewing pane

Ray CastingShoot rays to each pixel


Wrap-Up

Attenuation law for bone materialFew Applications of DRR to 3D Meshes (most work on CTdata – voxels)Using OpenGL Shading Language (GLSL)

Multi Pass Algorithm is availableSingle Pass Algorithm is considered the state of the art, butno applied implementation exists

Compute Unified Device Architecture (CUDA) peelingapplications exist (but not for computing DRRs)


Current Solution

InputsCAD model of vertebraeCamera position, object positions, object orientation, …

Outputssimulated Radiograph!


Current Solution

Ray CastingMultipass AlgorithmRay Casting and DepthPeelingGLSL

Why use CAD models?Problem context requiresdeformations to the 3DmodelFaster to deform CADmodelsGenerally, decreasesamount of computation


What’s expected?

Enhance the current solution1 Modify code to implement the reported single-pass approach2 Port solution to CUDA3 Test and compare solutions

Significant speed-ups are expected


Technologies – GLSL

OpenGL ShadingLanguage

Allows the modificationof fixed functionality ofthe GPU pipelineSimilar syntax to C/C++Modules called Shaders


Technologies – CUDA

Compute Unified DeviceArchitecture

Parallel Computing ArchitectureAllows direct access to parallelprocessors and memoryKernel function executed onGPU device

Allows hierarchicalconfiguration of threads uponkernel launch

Massive data parallelismAllows versatile and morecontrolled programming


Work Plan


Thank You for Listening!Ask Away!


Bibliography

I The opengl shading language.I Lisa Gottesfeld Brown.

A survey of image registration techniques.ACM Comput. Surv., 24(4):325–376, 1992.

I David B. Kirk and Wen mei W. Hwu.Programming Massively Parallel Processors - A Hands-on Approach.Morgan Kaufmann, 2010.

I A. Mitulescu, W. Skalli, D. Mitton, and J. A. De Guise.Three-dimensional surface rendering reconstruction of scoliotic vertebrae using a non stereo-correspondingpoints technique.European Spine Journal, 2002.

I Shinichiro Mori, Masanao Kobayashi, Motoki Kumagai, and Shinichi Minohara.Development of a gpu-based multithreaded software application to calculate digitally reconstructed radiographsfor radiotherapy.Radiological Physics and Technology, 2009.

I 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, pages586–595, Berlin, Heidelberg, 2009. Springer-Verlag.

I Scott D. Roth.Ray casting for modeling solids.j-CGIP, 18(2), 1982.

I 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.


Bibliography

Full Bibliography Listed in:

https://dev.andrecardoso.eu/bibtexbrowser.php?bib=thesisbib.bib


