Compton cameras are radiation detectors that provide spatial information on the origin of the \(\gamma \) -ray sources based on Compton scattering. Many applications require these detectors to be used at high counting rate. As such, the preprocessing of the detections as well as the imaging algorithms are required to be time-efficient in order for the data to be processed in real time. In this work, optimizations to the preprocessing of events in Compton cameras based on monolithic crystals, with special focus on event identification, were implemented using a parallelizable algorithm. Regarding imaging, an established 3D back projection algorithm was parallelized and implemented using SYCL [1]. The parallel implementation of the algorithm was included without and with several optimizations such as the pre-computing values, discarding low impact contributions based on angle, and selecting an efficient shape of the image universe. The implementations were tested with Intel CPUs, Intel GPUs, and NVIDIA GPUs. An outlook into the study of algorithms to reconstruct the position of interaction within Compton cameras based on monolithic crystals into segmented regions and other next steps is included.