International audienceWe propose a simulation framework to simulate the computed tomography acquisition process. It includes five components: anatomic data, respiration modelling, automatic parametrisation, X-ray simulation, and tomography reconstruction. It is used to generate motion artefacts in reconstructed CT volumes. Our framework can be used to evaluate CT reconstruction algorithm with motion artefact correction in a controlled environment

Vidal, Franck,

Villard, Pierre-Frédéric

Vidal, Franck, P.

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HAL Id: hal-01237833https://hal.inria.fr/hal-01237833Submitted on 3 Dec 2015HAL is a multi-disciplinary open accessarchive for the deposit and dissemination of sci-entific research documents, whether they are pub-lished or not. The documents may come fromteaching and research institutions in France orabroad, or from public or private research centers.L’archive ouverte pluridisciplinaire HAL, estdestinée au dépôt et à la diffusion de documentsscientifiques de niveau recherche, publiés ou non,émanant des établissements d’enseignement et derecherche français ou étrangers, des laboratoirespublics ou privés.Simulated Motion Artefact in Computed TomographyFranck Vidal, Pierre-Frédéric VillardTo cite this version:Franck Vidal, Pierre-Frédéric Villard. Simulated Motion Artefact in Computed Tomography. Euro-graphics Workshop on Visual Computing for Biology and Medicine, 2015, Chester, United Kingdom.￿10.2312/vcbm.20151228￿. ￿hal-01237833￿The definitive version is available at http://diglib.eg.org/.F. P. Vidal, and P.-F. Villard: Simulated Motion Artefact in Computed Tomography. InEurographics Workshop on Visual Computing for Biology and Medicine, Sept 2015, TheEurographics AssociationDOI: 10.2312/vcbm.20151228@inproceedings {vcbm.20151228,booktitle = {Eurographics Workshop on Visual Computing for Biology and Medicine},editor = {Katja B\"uhler and Lars Linsen and Nigel W. John},title = {{Simulated Motion Artefact in Computed Tomography}},author = {Vidal, Franck P. and Villard, Pierre-Fr\’ed\’eric},year = {2015},pages = {213--214},publisher = {The Eurographics Association},ISSN = {2070-5786},ISBN = {978-3-905674-82-8},DOI = {10.2312/vcbm.20151228},ABSTRACT = {We propose a simulation framework to simulate the computed tomography acquisitionprocess. It includes five components: anatomic data, respiration modelling, automaticparametrisation, X-ray simulation, and tomography reconstruction. It is usedto generate motion artefacts in reconstructed CT volumes. Our framework can be usedto evaluate CT reconstruction algorithm with motion artefact correction ina controlled environment.},}Simulated Motion Artefact in Computed TomographyF.P. Vidal 1, and P.-F. Villard 21 School of Computer Science, Bangor University, LL57 1UT, United Kingdom2 LORIA, University of Lorraine, FranceAbstractWe propose a simulation framework to simulate the computed tomography acquisition process. Itincludes five components: anatomic data, respiration modelling, automatic parametrisation, X-ray sim-ulation, and tomography reconstruction. It is used to generate motion artefacts in reconstructed CTvolumes. Our framework can be used to evaluate CT reconstruction algorithm with motion artefactcorrection in a controlled environment.1 Respiration ModellingAnatomical data from 4D CT scans is segmented to provide polygon meshes. The motion due to the res-piration is computed in realtime using three different methods [4]. The rib cage motion is simulated by anarticulated model of the rib rotations. The diaphragm behaviour is simulated by a deformable model withan intrinsic force to apply contraction and relaxation on its central tendon. The liver behaviour is simulatedby a deformable model where boundary conditions are applied by both the rib cage and the diaphragmmotion. The 3D Chainmail method is used to deform soft-tissues in real-time [1]. The model includes manyparameters, which are automatically tuned using artificial evolution [3].2 X-ray SimulationIn [2] we proposed a method to compute the X-ray attenuation law on the GPU. The code has been revisedand a quantitative validation has been performed. It shows the accuracy of results produced with ourimplementation. The result is gVirtualXRay, Virtual X-Ray Imaging Library on GPU, an OpenSource Libraryavailable at http://gvirtualxray.sourceforge.net/.Fig. 1 shows the virtual scanning environment. The geometry of the virtual patient is positioned in thePatientDetectorSourceFigure 1: Scanning environment.centre of the CT scanner. For each anatomical structure (skin, liver, diaphragm, lungs, ribs, and spine), aHounsfield value is associated to a polygon mesh. The CT scanner is made of a rotation axis, a parallel X-raysource, and a detector. The source and detector rotate around the patient. The source can be monochromaticor polychromatic and it has an initial position. The detector is defined by its initial position and its size(number of pixels and pixel resolution).3 CT AcquisitionThe respiration frequency of the patient and angular frequency of the source/detector couple can be tuned.In the illustrations, we use realistic parameters: 20 breaths per minutes and 15 second to perform a completecircular revolution. Successive projections are then concatenated and finally transformed into a sinogram(i.e. a Radon transform) (see Fig. 2).(a) (b) (c) (d)Figure 2: Simulated CT data acquisition; (a) & (b) X-ray projections; (a) without respiration; (b) withrespiration; (c) & (d) corresponding sinograms.4 CT ReconstructionTo reconstruct the CT slices corresponding to Fig. 2, the inverse Radon transform of sinograms needs to becomputed. We use the famous filtered back projection (FBP) method. No motion artefact can be seen inthe CT slice corresponding to the sinogram simulated without respiration (see Fig. 3(a)). When respirationis used, ghost artefacts can be seen (see Fig. 3(b)).2(a) Without respiration.Ghost artefacts(b) With respiration.Figure 3: Reconstructed CT slices.5 ConclusionsThis poster focused on the simulation of CT data acquisition with respiration modelling. It involves twosimulation components: Respiration and X-ray. Both run with real-time performance. Slices reconstructedwith the resulting data include ghosting artefacts due to motion. We demonstrated that realistic data cannow be simulated in a controlled environment. Our framework can be used to evaluate motion correctionmethods in CT reconstruction algorithms.6 AcknowledgementsThis work has been partially funded by FP7-PEOPLE-2012-CIG project Fly4PET – Fly Algorithm in PETReconstruction for Radiotherapy Treatment Planning (http://fly4pet.fpvidal.net).References[1] Y. Li and K. Brodlie. Soft Object Modelling with Generalised ChainMail - Extending the Boundaries ofWeb-based Graphics. Comput Graph Forum, 22(4):717–727, 2003.[2] F. P. Vidal, M. Garnier, N. Freud, J. M. Létang, and N. W. John. Simulation of X-ray attenuation onthe GPU. In Proc Theor Pract Comput Graph, pages 25–32. Eurographics Association, 2009.[3] F. P. Vidal, P. Villard, and E. Lutton. Tuning of patient specific deformable models using an adaptiveevolutionary optimization strategy. IEEE IEEE Trans Biomed Eng, 59(10):2942–2949, 2012.[4] P. Villard, F. P. Vidal, C. Hunt, F. Bello, N. W. John, S. Johnson, and D. A. Gould. Simulation ofpercutaneous transhepatic cholangiography training simulator with real-time breathing motion. Int JComput Assist Radiol Surg, 4(9):571–578, 2009.3Figure 4: Poster presented at Eurographics Workshop on Visual Computing for Biology and Medicine,Chester, UK, May 14–15, 2015.4

Simulated Motion Artefact in Computed Tomography

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Simulated Motion Artefact in Computed Tomography

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