A machine learning approach in the estimation of a radioactive source position using a coded aperture device

Abstract

Coded Aperture γ-cameras have been used for more than three decades for imaging radioactive source distributions encountered in astrophysics, in decommissioning of nuclear facilities, and in nuclear medicine. These devices enable the identification of the coordinates of γ-emitters located within their Field of View (FOV) with the use of the coded-aperture shadow projected on pixelated detectors. In this work we have developed machine learning algorithms based on Gradient Boosted Decision Trees (BDTG) and Deep Neural Networks (DNN). The algorithms have been trained using 21000 shadowgrams created with simulation. A custom fast simulation tool was used to produce the shadowgrams due to sources placed randomly at different positions within the FOV at distances from 20 cm up to 20 m from the detector plane. The performance of the algorithms has been evaluated with the aid of a different independent simulation sample of shadowgrams and verified with real data.