273 research outputs found
Deformable Registration for Image-Guided Radiation Therapy
No full texthttp://www.creatis.insa-lyon.fr/~dsarrut/mybib/2006/sarrut-zmedphys2006-short-version.pdf articleInternational audienc
Deformable Registration for Image-Guided Radiation Therapy
No full texthttp://www.creatis.insa-lyon.fr/~dsarrut/mybib/2006/sarrut-zmedphys2006-short-version.pdf articleInternational audienc
Deformable Registration for Image-Guided Radiation Therapy
No full texthttp://www.creatis.insa-lyon.fr/~dsarrut/mybib/2006/sarrut-zmedphys2006-short-version.pdf articleInternational audienc
Time-consistent parametrization from dynamic optical surface detection for respiratory motion recovery in radiation oncology
No full textA novel CT-based contrast enhancement technique for markerless lung tumor tracking in X-ray projection images
No full textTumour motion tracking technique based on dynamic surface scanning and 4D CT breathing motion model
No full textGAN for source modeling in PET Monte Carlo simulation
No full textInternational audienceIn previous works [1,2], it has been shown that phase-spaces can be modeled with Generative Adversarial Network (GAN [3]) such that the generator approximates the initial distribution of particles, thereby being able to be used as a source for Monte Carlo simulation (MCS). This approach was applied for modeling photon beams from Linac head in radiation therapy treatment [1], and for gammas exiting phantoms or patient CT during simulation of SPECT systems [2]. Compared to conventional phase-space files, GAN’s generator was shown to be compact, few MB instead of few GB. Moreover, splitting MCS of SPECT imaging system in two parts, 1) tracking emitted particles within the phantom/patient CT, 2) tracking particles in the detector, and modeling the first part with the GAN generator was shown to lead to computational time gain. However, such a GASP (GAN-generated Source of Particles) approach cannot readily be used for the simulations of PET imaging systems because it requires particle time information and the knowledge of the initial emitting event to distinguish between Trues, Scatter and Random coincidences. In this work, we extend the GASP method to consider pairs of particles, timestamps, particle weights. We also propose a new parameterization that improves the training.[1] D. Sarrut, N. Krah, and J. M. Létang. Generative adversarial networks (GAN) for compact beam source modelling in MonteCarlo simulations.Physics in Medicine & Biology, 64(21):215004,76, 2019.[2] D. Sarrut, A. Etxebeste, N. Krah, and J.M. Létang. Modeling complex particles phase space with GAN for Monte Carlo SPECTsimulations: A proof of concept.Physics in Medicine & Biology, 2021.[3] I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville and Y. Bengio. Generative adversarialnets. In Advances in Neural Information Processing Systems, vol 2, pages 2672–2680, 2014
Multi-dimensional respiratory motion tracking from markerless optical surface imaging based on deformable mesh registration
No full textGAN for source modeling in PET Monte Carlo simulation
No full textInternational audienceIn previous works [1,2], it has been shown that phase-spaces can be modeled with Generative Adversarial Network (GAN [3]) such that the generator approximates the initial distribution of particles, thereby being able to be used as a source for Monte Carlo simulation (MCS). This approach was applied for modeling photon beams from Linac head in radiation therapy treatment [1], and for gammas exiting phantoms or patient CT during simulation of SPECT systems [2]. Compared to conventional phase-space files, GAN’s generator was shown to be compact, few MB instead of few GB. Moreover, splitting MCS of SPECT imaging system in two parts, 1) tracking emitted particles within the phantom/patient CT, 2) tracking particles in the detector, and modeling the first part with the GAN generator was shown to lead to computational time gain. However, such a GASP (GAN-generated Source of Particles) approach cannot readily be used for the simulations of PET imaging systems because it requires particle time information and the knowledge of the initial emitting event to distinguish between Trues, Scatter and Random coincidences. In this work, we extend the GASP method to consider pairs of particles, timestamps, particle weights. We also propose a new parameterization that improves the training.[1] D. Sarrut, N. Krah, and J. M. Létang. Generative adversarial networks (GAN) for compact beam source modelling in MonteCarlo simulations.Physics in Medicine & Biology, 64(21):215004,76, 2019.[2] D. Sarrut, A. Etxebeste, N. Krah, and J.M. Létang. Modeling complex particles phase space with GAN for Monte Carlo SPECTsimulations: A proof of concept.Physics in Medicine & Biology, 2021.[3] I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville and Y. Bengio. Generative adversarialnets. In Advances in Neural Information Processing Systems, vol 2, pages 2672–2680, 2014
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