The Reconstruction of 3d Radiation Field Based on Sparse Measurement Data

Abstract

Abstract With the development of nuclear technology, more and more occupational workers work in radioactive environments. The three-dimensional (3D) dose rate field is of great significance in the radiation protection field. Many researchers focus on the reconstruction of 2D radiation fields but few of them paid attention to the reconstruction of the 3D radiation field based on sparse measurement data. In this work, an interpolation method is introduced to reconstruct 3D radiation field using sparse sampling nodes. The proposed method includes interpolation on sampling grid lines, interpolation on sampling grid plane and interpolation in the three-dimensional space. Additionally, two simulation cases are conducted in MATLAB to prove the effectiveness and accuracy of the proposed method. Case1 and Case 2 are 3D radiation fields with a single radioactive source and two radioactive sources respectively. The reconstruction radiation field is compared to the dose rate field simulated by the Monte Carlo method. By using less than 1% of measurement data, the proposed method precisely reconstructs radiation fields with an average relative error of 0.82% and 1.71% respectively. What’s more, the accuracy of the proposed method was studied at different sampling node ratios. As the ratio of sampling nodes decreases, the average error and maximum of the relative error increase obviously. The proposed method is an interpolation method suitable for the radiation field and it can reconstruct the 3D radiation field accurately with sparse data.