Particle volume reconstruction based on a marked point process and application to TOMO-PIV

In this paper, we propose a new tomographic reconstruction method, called IOD-PVRMPP, to reconstruct 3D particle volumes from 2D particle images provided by the Tomographic Particle Image Ve-locimetry (Tomo-PIV) technique. Our method, based on marked point processes (or object processes), allows to solve the problem in a parsimonious way. It facilitates the introduction of prior knowledge and solves memory problem which is inherent to voxel based approaches used by classical tomographic reconstruction methods. The reconstruction of a 3D particle set is obtained by minimizing an energy function which defines the marked point process. To this aim, we use a simulated annealing algorithm based on Reversible Jump Markov Chain Monte Carlo (RJMCMC) method. To speed up the convergence of the simulated annealing, we develop an initialization method which provides the initial distribution of 3D particles. To do that, we proceed by detecting 2D particles located in projection images. Using synthetic data, we show that IOD-PVRMPP method gives better results than MinLOS-MART method for different seeding densities.