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Objective: Proton therapy is an emerging method in cancer therapy. One of the main developments is to increase the accuracy of the Bragg-peak position calculation, which requires more precise relative stopping power (RSP) measurements. An excellent choice is the application of proton computed tomography (pCT) systems which takes the images under similar conditions, as they use the same irradiation device and hadron beam for imaging and treatment. A key aim is to develop an precise image reconstruction algorithm for pCT systems to reach their maximal performance. Approach: An image reconstruction algorithm was developed in this work, which is suitable to reconstruct pCT images from the deposited energy, position and direction measurement of individual protons. The flexibility of an iterative image reconstruction algorithm was utilised to appropriately model the trajectory of protons. Monte Carlo (MC) simulations of a Derenzo and a CTP404 phantom was used to test the accuracy of the image reconstruction. Main results: The Richardson-Lucy algorithm was applied first and successfully for pCT image reconstruction. Authors used an averaged probability density based approach for the interaction (system) matrix generation, which is a relevant description to consider the uncertainty of the path of the protons in the patient. In case of an idealized setup, 1.43 lp/cm spatial resolution and 0.3% RSP accuracy was achieved. Significance: This work is the first application of the Richardson-Lucy algorithm, for pCT image reconstruction.

Objective. Algorithmic differentiation (AD) can be a useful technique to numerically optimize design and algorithmic parameters by, and quantify uncertainties in, computer simulations. However, the effectiveness of AD depends on how "well-linearizable" the software is. In this study, we assess how promising derivative information of a typical proton computed tomography (pCT) scan computer simulation is for the aforementioned applications. Approach. This study is mainly based on numerical experiments, in which we repeatedly evaluate three representative computational steps with perturbed input values. We support our observations with a review of the algorithmic steps and arithmetic operations performed by the software, using debugging techniques. Main results. The model-based iterative reconstruction (MBIR) subprocedure (at the end of the software pipeline) and the Monte Carlo (MC) simulation (at the beginning) were piecewise differentiable. Jumps in the MBIR function arose from the discrete computation of the set of voxels intersected by a proton path. Jumps in the MC function likely arose from changes in the control flow that affect the amount of consumed random numbers. The tracking algorithm solves an inherently non-differentiable problem. Significance. The MC and MBIR codes are ready for the integration of AD, and further research on surrogate models for the tracking subprocedure is necessary., Comment: 27 pages, 11 figures

Hadron therapy is a novel treatment against cancer. The main advantage of this therapy causes less side effect in comparison to X-ray irradiation methods. Hadron therapy is just ahead of a significant breakthrough since this technique can be more precise, applying proton computer tomograph (pCT) to map the stopping power in the tissues. The research and development of a pCT require a fast detector to measure the energy of hadrons behind the patient. The best detector option is called hadron-tracking calorimeter, which consists of sandwich layers of silicon tracking detectors and absorber layers. The combination of measuring the trajectory (tracking process), and, in parallel, the energy of relativistic particles, can provide high-resolution hadron imaging. This semiconductor-based technology requires stable temperature and homogeneous cooling. I have worked in the development of this detector in the Bergen pCT Collaboration for two years. Last year my work was to investigate the temperature distribution in the calorimeter and examine two cooling concepts in detail. I performed both analytical and numerical calculations to analyze the temperature distribution of the calorimeter. The final decision about the design takes into account many engineering aspects, such as reliability, flexibility, and performance., Comment: BSc thesis of \'Akos Sud\'ar at Budapest University of Technology and Economics