This follow-up proposal focuses on image reconstruction and compensation of image degradation phenomena for Compton cameras (CC). It builds on a project devoted to CC-based prompt-gamma imaging (PGI) for range verification in particle therapy. Within this project, we have developed advanced reconstruction algorithms and proposed novel approaches to cope with the very low-count levels typical of CC-based PGI and truncation artefacts. In the future project, in addition to PGI, the possible use of CC for planing and control of radionuclide-based therapy will be also explored. Firstly, we will improve and investigate in depth our current approaches, which include Bayesian reconstruction with dedicated priors (also called penalty or regularization functions) based on the information provided by a hodoscope. These algorithms aim to reduce the noise contribution as well as image degradation and allow for a robust and accurate identification of range shifts. The suggested penalty functions will be integrated into regularized (Bayesian) reconstruction schemes. Additionally, we will apply regularization via priors to the Origin Ensembles (OE) algorithm, a promising approach not explored yet. The degradation caused by the unknown energy of the PG radiation will be further tackled. Later, the availability of hodoscope data will not be longer assumed and the required information will be extracted from the images. To this end, alternative optimization procedures, image processing and machine learning techniques will be considered. Special attention will be put on degradation phenomena and their compensation, as this topic has been hardly addressed so far, although compensation of certain phenomena is mandatory towards clinical application. For a realistic application in particle therapy, the high PG emission rates need to be considered. Therefore, a dedicated simulation pipeline based on GATE and custom-made programmes will be developed. The objective is to reproduce the time structure of the therapeutic beams, the modulation of the PG rates, and the subsequent detection of accidental coincidences. This information is required to design estimation and compensation methods for the random contribution, an issue which has not been contemplated for CC imaging yet. For this purpose, models able to estimate the random contribution will be developed. The degradation caused by patient scatter and attenuation will be also analyzed, for both range verification and theranostics. Compensation methods for these phenomena will be developed and integrated within the reconstruction framework. The envisioned reconstruction algorithms are not restricted to a particular Compton camera, and should be easily adaptable to any configuration. In the same vein, our approaches could be also of use for other applications and modalities, such as multi-isotope imaging or even for in-beam PET.