Capability of MLEM and OE to Detect Range Shifts With a Compton Camera in Particle Therapy

To identify range deviations by using Compton cameras (CCs), tomographic image reconstruction of CC data is needed. Within this context, image reconstruction is usually performed using maximum likelihood expectation maximization (MLEM), and more recently, the origin ensemble (OE) algorithm. In this article, we investigate how MLEM and OE affect the precision and accuracy of estimated range deviations. In particular, we focus on the effects of data selection, statistical fluctuations, and artifact reduction. The use of external information of the beam path through a hodoscope was also explored. Additionally, two methods to calculate range deviations were tested. To this aim, realistic proton beams were simulated using GATE and data from single spots as well as from seven contiguous spots of an energy layer were reconstructed. MLEM and OE reacted differently to the poor data statistics. In general, both algorithms were able to detect range shifts for single spots, particularly when multiple coincidences were also considered. Selection of events corresponding to the most relevant energy peaks decreased the identification performance due to the lower statistics. When data from several contiguous spots were jointly reconstructed, the accuracy of the results degraded significantly, and nonzero shifts were assigned when no shifts had occurred. The limited size of the cameras and the subsequent restriction in the orientation and aperture of detected cones, as well as in the number of detected events are major challenges. Future efforts should be devoted to noise regularization and compensation for data truncation.