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Objective: Proton therapy is a emerging method against cancer. One of the main development 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 take the images under similar conditions to treatment as they use the same irradiation device and hadron beam for imaging and treatment. A key aim is to develop an accurate 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 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. Probability density based approach was applied for interaction (system) matrix generation, which is an advanced approach to consider the uncertain path of the protons in the patient. Significance: The track of protons are scattered when they travel through material at the hadron therapy energies. This property limits the achievable spatial resolution, especially for single-sided pCT setups investigated in this study. The main motivation of the presented research is to test new approaches for the image reconstruction, focusing on the achieved spatial- and density resolution and the image noise. Realistic imaging setup were simulated with reasonably low proton statistics, to achieve results, which is likely to be reproducible in clinical environment.

The heat pulse experiment is a well-known method for determining thermal diffusivity. However, neither the measurement nor the evaluation methodologies are straightforward for heterogeneous, highly porous materials. In the present paper, we focus on two open-cell carbon foam types, differing in their porosity but having the same size. Recent experiments showed that a non-Fourier behaviour, called 'over-diffusive' propagation, is probably present for such a complex structure. The (continuum) Guyer-Krumhansl equation stands as a promising candidate to model such transient thermal behaviour. In order to obtain a reliable evaluation and thus reliable thermal parameters, we utilize a novel, state-of-the-art evaluation procedure developed recently using an analytical solution of the Guyer-Krumhansl equation. Based on our observations, it turned out that the presence of high porosity alone is necessary but not satisfactory for non-Fourier behaviour. Additionally, the mentioned non-Fourier effects are porosity-dependent. However, porous samples can also follow the Fourier law. These data serve as a basis to correctly identify the characteristic heat transfer mechanisms and their corresponding time scales, which altogether result in the present non-Fourier behaviour. Keywords: flash experiments, non-Fourier heat conduction, highly porous carbon foams.

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

The thermal modeling of biological systems has increasing importance in developing more advanced, more precise techniques such as ultrasound surgery. One of the primary barriers is the complexity of biological materials: the geometrical, structural, and material properties vary in a wide range, and they depend on many factors. Despite these difficulties, there is a tremendous effort to develop a reliable and implementable thermal model. In the present paper, we focus on the continuum modeling of heterogeneous materials with biological origin. There are numerous examples in the literature for non-Fourier thermal models. However, as we realized, they are associated with a few common misconceptions. Therefore, we first aim to clarify the basic concepts of non-Fourier thermal models. These concepts are demonstrated by revisiting two experiments from the literature in which the Cattaneo-Vernotte and the dual phase lag models are utilized. Our investigation revealed that using these non-Fourier models is based on misinterpretations of the measured data, and the seeming deviation from Fourier's law originates in the source terms and boundary conditions.

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