The reconstruction of 3D radiation field based on sparse measurement data

With the development of nuclear technology, an increasing number of occupational workers are exposed to 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 dose rate field based on sparse measurement data. In this work, the net function interpolation method is introduced to reconstruct 3D radiation field using sparse sampling nodes. In contrast to traditional dose rate interpolation methods, we interpolated the value of the inverse of the dose rate. By using the inverse, the field to be interpolated follows a power function, which fits the net function much better than the direct interpolation. The proposed method includes interpolation on sampling grid lines, interpolation on sampling grid plane and interpolation in the three-dimensional space. Additionally, four simulation experi-ments are carried out to prove the effectiveness and accuracy of the proposed method. The first two cases are radiation fields formed by a single radioactive source without and with shielding. Radioactive environments caused by multiple radioactive sources without and with shielding are conducted in Case 3 and Case 4. The reconstruction radiation field is compared to the radiation field simulated by the Monte Carlo method. The average relative errors of the four cases are 0.82%, 2.13%, 1.71% and 4.27% respectively using approximately 1% measurement data. The experimental data provided by other researchers are also used to validate the effectiveness of the proposed method and the minimum relative error of Case 5 is 4.67%. Therefore, the net function is an interpolation method suitable for the radiation field and it can reconstruct the 3D radiation field accurately with sparse data.