Abstract : A novel technique for radiotherapy - hadron therapy - irradiates tumors using a beam of protons or carbon ions. Hadron therapy is an effective technique for cancer treatment, since it enables accurate dose deposition due to the existence of a Bragg peak at the end of particles range. Precise knowledge of the fall-off position of the dose with millimeters accuracy is critical since hadron therapy proved its efficiency in case of tumors which are deep-seated, close to vital organs, or radio-resistant. A major challenge for hadron therapy is the quality assurance of dose delivery during irradiation. Current systems applying positron emission tomography (PET) technologies exploit gamma rays from the annihilation of positrons emitted during the beta decay of radioactive isotopes. However, the generated PET images allow only post-therapy information about the deposed dose. In addition, they are not in direct coincidence with the Bragg peak. A solution is to image the complete spectrum of the emitted gamma rays, including nuclear gamma rays emitted by inelastic interactions of hadrons to generated nuclei. This emission is isotropic, and has a spectrum ranging from $100$~keV up to $20$~MeV. However, the measurement of these energetic gamma rays from nuclear reactions exceeds the capability of all existing medical imaging systems. An advanced Compton scattering detection method with electron tracking capability is proposed, and modeled to reconstruct the high-energy gamma-ray events. This Compton detection technique was initially developed to observe gamma rays for astrophysical purposes. A device illustrating the method was designed and adapted to Hadron Therapy Imaging (HTI). It consists of two main sub-systems: a tracker where Compton recoiled electrons are measured, and a calorimeter where the scattered gamma rays are absorbed via the photoelectric effect. Considering a hadron therapy scenario, the analysis of generated data was performed, passing trough the complete detection chain from Monte Carlo simulations to reconstruction of individual events, and finally to image reconstruction. A list-mode Maximum-Likelihood Expectation-Maximization (MLEM) algorithm was adopted to perform image reconstruction in conjunction with the imaging response, which has to depict the complex behavior of the detector. Modeling the imaging response requires complex calculations, considering the incident angle, all measured energies, the Compton scatter angle in the first interaction, the direction of scattered electron (when measured). In the simplest form, each event response is described by Compton cone profiles. The shapes of the profiles are approximated by 1D Gaussian distributions. A strong correlation was observed between pattern of the reconstructed high-energy gamma events, and location of the Bragg peak. The performance of the imaging technique illustrated by the HTI is a function of the detector performance in terms of detection efficiency, spatial and energy resolution, acquisition time, and the algorithms used to reconstruct the gamma-ray activity. Thus beside optimizations of the imaging system, the applied imaging algorithm has a high influence on the final reconstructed images. The HTI reconstructed images are corrupted by noise due to the low photon counts recorded, the uncertainties induced by finite energy resolution, Doppler broadening, the limited model used to estimate the imaging response, and the artifacts generated when iterating the MLEM algorithm. This noise is spatially varying and signal-dependent, representing a major obstacle for information extraction. Thus image de-noising techniques were investigated. A Wavelet based multi-resolution strategy of list-mode MLEM Regularization (WREM) was developed to reconstruct Compton images. At each iteration, a threshold-based processing step was integrated. The noise variance was estimated at each scale of the wavelet decomposition as the median value of the coefficients from the high-frequency sub-bands. This approach allowed to obtain a stable behavior of the iterative algorithm, presenting lower mean-squared error, and improved contrast recovery ratio.