Use of fast realistic simulations on GPU to extract CAD models from microtomographic data in the presence of strong CT artefacts
| Autor: | Franck Vidal, Jean Michel Létang, Iwan T. Mitchell |
|---|---|
| Přispěvatelé: | Bangor University, Imagerie Tomographique et Radiothérapie, Centre de Recherche en Acquisition et Traitement de l'Image pour la Santé (CREATIS), Université Jean Monnet [Saint-Étienne] (UJM)-Hospices Civils de Lyon (HCL)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Jean Monnet [Saint-Étienne] (UJM)-Hospices Civils de Lyon (HCL)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM) |
| Jazyk: | angličtina |
| Rok vydání: | 2022 |
| Předmět: |
Computer science
CAD Numerical simulation Evolutionary computation 01 natural sciences 030218 nuclear medicine & medical imaging 010309 optics 03 medical and health sciences 0302 clinical medicine High fidelity 0103 physical sciences X-rays Computer vision Optimisation Computed tomography Impulse response Computer simulation Computer aided analysis business.industry Detector General Engineering Process (computing) Image segmentation [INFO.INFO-IA]Computer Science [cs]/Computer Aided Engineering [INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation [INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV] Artificial intelligence High performance computing business |
| Zdroj: | Precision Engineering Precision Engineering, Elsevier, 2022, 74, pp.110-125. ⟨10.1016/j.precisioneng.2021.10.014⟩ |
| ISSN: | 0141-6359 |
| DOI: | 10.1016/j.precisioneng.2021.10.014⟩ |
| Popis: | International audience; The presence of strong imaging artefacts in microtomographic X-ray data makes the CAD modelling process difficult to carry out. As an alternative to traditional image segmentation techniques, we propose to register the CAD models by deploying a realistic X-ray simulation on GPU in an optimisation framework. A user study was also conducted to compare the measurements made manually by a cohort of volunteers and those produced with our framework. Our implementation relies on open source software only. We numerically modelled the real experiment, taking into account geometrical properties as well as beam hardening, impulse response of the detector, phase contrast, and photon noise. Parameters of the overall model are then optimised so that X-ray projections of the registered the CAD models match the projections from an actual experiment. It appeared that manual measurements can be variable and subject to bias whereas our framework produced more reliable results. The features seen in the real CT image, including artefacts, were accurately replicated in the CT image reconstructed from the simulated data after registration: (i) linear attenuation coefficients are comparable for all the materials, (ii) geometrical properties are accurately recovered, and (iii) simulated images reproduce observed experimental artefacts. We showed that the choice of objective function is crucial to produce high fidelity results. We also demonstrated how to automatically produce CAD models as an optimisation problem, producing a high cross-correlation between the experimental CT slice and the simulated CT slice. These results pave the way towards the use of fast realistic simulation for accurate CAD modelling in tomographic X-ray data |
| Databáze: | OpenAIRE |
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