The use of protons to treat cancer has expanded rapidly in the past two decades. For safe and effective proton therapy, the proton range in a patient’s body must be accurately determined. Current treatment planning is based on X-ray computed tomography images, which might cause uncertainty because of the different behaviour between protons and X-rays. As an alternative, proton Computed Tomography (pCT) has been proposed to directly measure the Relative Stopping Power (RSP) map in the patient and reduce this uncertainty. During a proton CT scan, a high-energy proton beam is directed at the patient. Then, the proton’s residual energy and position are measured with a detector placed behind the patient. This information is used to calculate the volumetric RSP. In the case of using a pixel based detector, a tracking algorithm is required in order to increase the proton intensity capacity of the detector. A proton track reconstruction system has been already developed by Pettersen [1], however, it has some limitations on the track density that can be reconstructed correctly. The algorithm is based on the track-following scheme, in which a growing track searches for deeper- laying activated pixels. This thesis introduces proton therapy and the advantages of pCT and proton radiography for treatment planning. Then, the main track reconstruction techniques found in the literature are reviewed. Improvements in the reconstruction process are proposed and their efficiencies are discussed. While current algorithm begins from the layer closest to the patient, in the present study a new reconstruction algorithm is developed. It differs by starting the reconstruction process from the distal end of the detector. Based on this new algorithm, studies related to its optimization are conducted. Lastly, an algorithm based on the identification of the most probable scenario is developed. The potential algorithms are evaluated on data simulated with GATE (based on Monte Carlo interactions) and PROCASIM (design to simplify the physical interactions between protons and the detector). The fraction of correctly reconstructed tracks and the computational eciency of the algorithms are analyzed to determine the most viable one. [1] H. E. S. Pettersen. A Digital Tracking Calorimeter for Proton Computed Tomography. PhD thesis, University of Bergen, Norway, February 2018.