Voxelization based PoCA point cloud filtration algorithm for image reconstruction for Muon Tomography

Abstract

Multiple Coulomb Scattering (MCS) based muon tomography technique has been considered to be a well-known tool to identify, discriminate, and to image the high-density objects placed inside closed volumes. The two most famous reconstruction algorithms are Point of Closest Approach (PoCA) and Maximum Likelihood Expectation Maximization (MLEM). PoCA is fast but purely geometrical and as a result of this, it gives a lot of false positives, i.e. sometimes the PoCA point lies outside the target object and hence it forms an envelope of false-positive which results in a smeared image. On the other hand, MLEM is an iterative algorithm and is much more computation-intensive. In this work a new and innovative method is proposed which is based on the concept of voxelization to handle the known problem of false positives of the PoCA algorithm, and hence provide a clear reconstructed image. These algorithms remove the false positives PoCA points from the 3D point cloud and will give useful information in terms of regions or voxels within a voxelized volume 'V' to do a clear image reconstruction. The advantages of the proposed algorithm to the existing algorithms are also discussed. The status of the experimental setup of the proposed facility using Resistive Plate Chamber(RPC) with spatial resolution of ∼ 1cm as muon detector, is also discussed. The preliminary data from the current experimental setup, showing detector performance and cosmic muon tracks are also shown. Since the experimental setup is not fully ready, the effectiveness of the developed algorithms and the results are evaluated using the data from the Geant4 simulation of the muon tomography setup.