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Article Dans Une Revue (Data Paper) Physics in Medicine and Biology Année : 2022

Modeling families of particle distributions with conditional GAN for Monte Carlo SPECT simulations

Résumé

Objective. We propose a method to model families of distributions of particles exiting a phantom with a conditional Generative Adversarial Network (condGAN) during Monte Carlo simulation of SPECT imaging devices. Approach. The proposed condGAN is trained on a low statistics dataset containing the energy, the time, the position and the direction of exiting particles. In addition, it also contains a vector of conditions composed of four dimensions: the initial energy and the position of emitted particles within the phantom (a total of 12 dimensions). The information related to the gammas absorbed within the phantom is also added in the dataset. At the end of the training process, one component of the condGAN, the generator (G), is obtained. Main results. Particles with specific energies and positions of emission within the phantom can then be generated with G to replace the tracking of particle within the phantom, allowing reduced computation time compared to conventional Monte Carlo simulation. Significance. The condGAN generator is trained only once for a given phantom but can generate particles from various activity source distributions.
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Dates et versions

hal-03920207 , version 1 (03-01-2023)

Identifiants

Citer

Albert Saporta, Ane Etxebeste, Théo Kaprelian, Jean Michel Létang, David Sarrut. Modeling families of particle distributions with conditional GAN for Monte Carlo SPECT simulations. Physics in Medicine and Biology, 2022, 67 (23), pp.234001. ⟨10.1088/1361-6560/aca068⟩. ⟨hal-03920207⟩
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