3D Compton Mapping of Point and Distributed Sources with Moving Detectors

Historically, Compton imagers have primarily been operated as stationary imagers. Recent advances in sensors and computing have enabled imagers to move while collecting measurements, enabling the mapping of gamma-ray radiation onto 3D surfaces and volumes. However, traditional Compton imaging methods do not directly generalize to 3D space due to computational complexity and varying sensitivity of the detector. This study builds on previous 3D imaging algorithms for a method applicable to the range of source distributions from point sources to broad distributed sources. Specifically, we propose a maximum-likelihood method iteratively adding point sources or regions of fully voxelized estimates until the measured events are best explained. Furthermore, we describe validation measurements using 500 point sources of Cu-64 to emulate a broadly distributed source and lessons learned for these algorithms.