Ghost particle suppression multiplicative algebraic reconstruction technique for tomographic PIV

The article explores the advancements in Tomographic Particle Image Velocimetry (Tomo-PIV) with a focus on the Multiplicative Algebraic Reconstruction Technique (MART). Since its proposal by Elsinga et al. in 2006, Tomo-PIV has become a mainstream method for 3D velocity field measurement due to its simplicity and high precision. The MART method, which is well-suited for reconstructing objects with sharp gradients, has been the benchmark for particle reconstruction. The article delves into the theoretical underpinnings of MART, its advantages over other methods, and the challenges it faces, particularly the issue of ghost particles. To address these challenges, the authors propose a novel ghost particle suppression strategy that combines variational denoising and regularized inverse diffusion. This approach not only reduces the number of ghost particles but also enhances the contrast of true particles, leading to improved reconstruction quality and velocity estimation accuracy. The method is validated through simulations and experimental data, demonstrating its effectiveness in enhancing the performance of Tomo-PIV. The article also discusses the computational efficiency of the proposed method and suggests potential improvements for future research.