Proton therapy is a form of radiation therapy, that leverages the unique properties of protons to maximize dose deposition in treatment volumes. The usefulness of proton therapy treatment, in sparing healthy tissue, becomes even more evident with the incorporation of the FLASH effect. FLASH delivers ultra-high dose rates with minimal treatment time while maintaining therapeutic efficiency in eradicating tumours. How- ever, due to practical challenges such as energy layer switching in pencil beam scanning systems the clinical applications are limited. This thesis researched the development of patient-specific ridge filters (RFs) for proton therapy using an optimization algorithm. Ridge filters are energy modulators that help enable dose delivery without energy layer switching, making the FLASH effect feasible as was shown in 2018 [1]. Previous studies used static and dynamic methods [2] and fluence-based optimization [3] to construct patient-specific RFs. This study presents a novel framework for optimizing patient-specific RFs using a combination of TOPAS, a Monte Carlo particle simulation software that accurately models particle transport, and PyGAD, a Python-based genetic algorithm (GA) module. This class of optimizers is effective in cases where no derivatives are avail- able or are very difficult to compute. GAs do this by testing various simulations and evaluating these using a fitness function. The methodology involves simulating dose distribution in a scoring volume, optimizing ridge pin geometry, and evaluating performance using fitness functions. The results demonstrate that the proposed framework effectively generates patient-specific RFs with min- imal deviation from the desired dose distribution in simple cases, with a maximum dose difference of 2.66 % and mean dose of 99.21 % over the region of interest. Comparative analysis with prior approaches shows that the framework achieves similar results. However, applying the framework to cases with obstructions in the scoring volume requires further refinement of the algorithm. The findings provide a basis for using GAs for constructing patient-specific RFs for FLASH proton therapy. Future work should be aimed at refining the GA and Monte Carlo simulation and assessing the viability of producing the generated RFs.

The goal of the thesis is to find a Reduced Order Modelling method that speeds up burnup simulations—as encountered in the core of a Molten Salt Fast Reactor—while keeping
accuracy loss minimal. Four different methods have been tested: Proper Orthogonal Decomposition (POD), heuristically corrected POD, Balanced Truncation (BT), and Balanced
Proper Orthogonal Decomposition (BPOD). The first two methods are inadequate, because
of stability issues. The third is stable, but fails to execute for burnup simulations. The
fourth, a midway method between the first and third, does work for burnup simulations.
Using BPOD with 4 orders for a 10 year burnup simulation of 1650 nuclides with 1000
simulation steps, we find a normalised relative error of 10^-5 both for the total model and
each nuclide individually. The execution time per simulation step is reduced to 10^-5 s.
These results are a factor 1000 better than known alternatives such as the ORIGEN burnup
program.

The conclusion contains recommendations for incorporating different fuel mixtures and non-
linearity of the burnup equation in the BPOD. The method could be generalised to handle
arbitrary burnup problems with a single Reduced Order Model.

Background: Real-time adaptive radiotherapy workflows require fast spatial dose calculations with clinical accuracy. Modern physics-based dose calculation algorithms often compromise between speed and accuracy. In contrast, deep learning methods have shown to be effective at predicting spatial dose distributions with high accuracy in sub-second times. Very High Energy Electrons (VHEE) have shown potential as a treatment modality in recent years due to their penetrative ability and conformality. Creating a need for fast and accurate VHEE spatial dose calculations.
Purpose: This study presents a deep learning-based algorithm that utilizes convolutional layers and the self-attention mechanism to predict VHEE beam spatial dose distributions in sub-second times.
Methods: The presented Electron-Dose Transformer Algorithm (E-DoTA) maps the 3D patient geometry and beam characteristics (using a vector representing the beam energy and a 3D Gaussian with a specific Gaussian positional spread representing the beam shape) to
a 3D dose distribution. E-DoTA uses a series of 3D convolutional layers to extract features from the patient geometries and beam shape, followed by a transformer to route information between the extracted features and the added energy vector, which are then upsampled to
the 3D dose distribution. E-DoTA is trained on 60,000 combinations of patient geometries and beam characteristics, derived from 15,000 independent patient geometries based on 12 distinct patient CT scans from the abdomen region. The model’s accuracy and prediction
speed are assessed using 8,000 previously unseen patient geometries and beam characteristics.
Results: E-DoTA predicts dose distributions of VHEE beams with high accuracy, achieving a gamma pass rate of 97.05% ± 3% (3mm, 1%) and an average relative dose error of 0.254% ± 0.096% in approximately 131 ms.
Conclusions: The fast and high-accuracy dose predictions allow the speed-up of VHEE spatial dose distribution calculations, which is currently only provided by slow Monte Carlo algorithms. Further optimizations to E-DoTA could allow for more accurate and faster dose calculations, thereby potentially accelerating VHEE radiotherapy workflows.

Proton irradiation therapy is a powerful form of cancer treatment, promising better dose conformity as compared to conventional radiotherapy. Due to the complex scattering properties of protons, the optimization process that is needed to accurately target the cancer cells whilst causing minimal damage to
the surrounding healthy tissue and minimizing detrimental effects, is very time-consuming. This means that the CT scan it is based on has lost part of its accuracy in describing the to be irradiated tissue. To account for this, the surrounding tissue is irradiated more to ensure effective treatment, reducing dose conformity and thus increasing the probability of detrimental side effects.
To solve this problem, a new proton therapy method concept, called Online Adaptive Proton Therapy, or OAPT, calls for a CT scan to be taken about 30 seconds before the treatment, allowing the planned treatment to be adapted to any anatomical changes that have occurred since the previous scan where the original treatment planning was based on. The computations required for this adaptation, and the required quality assurance, currently still take significantly longer than 30 seconds on current computational hardware, making the concept not yet viable in its current form. However, using GPU-acceleration, parts of the algorithm that is used for this can be significantly sped up to reduce overall computational time, potentially making the concept viable for real world application. In this thesis, the research question ”How can GPU-offloading decrease computation time for proton therapy dose calculations?” is answered by accelerating two model algorithms representative of two time-consuming steps in the proton therapy optimization process and analyzing the performance, on
an NVIDIA V100S GPU, of the accelerated code. Furthermore, a model is postulated and validated to characterize and predict the performance of a GPU accelerated algorithm. Using OpenACC, both algorithms achieved speedups between 30x and 440x excluding data transfer time, and between 0.88x and 40x including data transfer time, with both values depending on the problem size, with larger problems yielding larger speedups. Further research is needed on the validity of the derived model on different hardware and for different algorithms. Furthermore, additional research on the effect of implementing these accelerated algorithms on the total computation time of the real world algorithm is advised.

In order to create radiotherapy treatment plans for cancer patients, dose calculations need to be done as quickly as possible to get accurate results. However, current dose calculation algorithms take too much time to be deployed effectively. The current in house algorithm of the Medical Physics and Technology Section at the TU Delft, attempts to solve this problem by utilising a deterministic algorithm that has a significant time advantage over Monte Carlo algorithms. However, this comes with the cost of inaccuracy, one of which is that it assumes
all dose is deposited locally along the beam path. This is inaccurate as secondary particles created from non-elastic nuclear interactions can deposit their dose far from the beam path due to retaining significant kinetic energy. This thesis attempts to reduce this inaccuracy by mapping and quantifying the secondary particles to assess their contribution in non-local dose deposition. And analysing the relevant particle’s energy and angle distributions to gain insight into the development of the particle's characteristics with depth. Thereafter the relevant
particle’s are then utilised as a source to emulate their production in a primary proton beam at different depths to obtain the relevant 3D dose distributions. The analysis concluded that secondary protons are the most relevant secondary particle as they contribute to 88% of the secondary dose and have a significant range to deposit their dose non locally. By utilising the secondary protons as a source, it was found that the relative error between the integrated depth dose (IDD) of the scored protons and the IDD obtained directly from Monte Carlo simulations is equal to 5.1% in the z-direction and 3.4% in the x and y-direction. The absolute difference was found to be 1.54 × 10−5 Gy which is equal to 0.096% of the total dose and 2.75% of the dose contributed by all secondary particles. The results show that the methodology can produce accurate 3D dose matrices for secondary protons at different depths, which can then be used to improve the accuracy of the in house algorithm by adding the precalculated 3D dose matrices to the algorithm

The main challenge of radiotherapy, used for treating cancer, is to deposit the prescribed dose in the tumor volume while sparing the surrounding tissue.The depth-dose distribution of protons makes proton therapy an alternative to conventional radiotherapy for some tumor sites. A better knowledge of the proton radiobiological mechanisms can improve the effectiveness of radiotherapy treatments.

Holland Proton Therapy Center (HPTC) is one of the Dutch proton therapy centers. One of the purposes of its experimental beamline is to perform radiobiological experiments. To conduct different types of pre-clinical experiments, the beamline must be equipped to provide large field irradiation with precise dose characterization. Moreover, having a reliable Monte Carlo(MC) model of the system allows to perform in silico verification of the beamline design and contribute to its optimization. In this context, the goals of this project were: implementing a dual-ring scattering system in the experimental room of HPTC to produce homogeneous fields of different sizes; creating a MC model of the HPTC passive beamline able to reproduce the experimental setup.

A dual-ring double scattering system was implemented in the HPTC horizontal beamline, starting from a single 150MeV pencil beam. The resulting passive irradiation fields were characterized by measuring and analyzing the lateral beam profiles, the depth-dose distributions and the relative dose at target position. Moreover, the beam characteristics and setup were implemented in theTOPAS MC code. The beam source parameters, input of the MC model, were found by comparing the experimental and simulated beam envelope and depth-dose distributions of the pencil beam. Then, the passive system model was benchmarked with experimental data by evaluating the lateral profiles, Braggcurves and dose distributions.

The results show that the implemented passive system can achieve dose uniformity between 96% and 99% for field sizes between 4x4cm² and 20x20cm²for a 150 MeV proton beam. Moreover, using a collimator with a 5x5cm²aperture, uniformity of at least 97% in the different Bragg peak regions is achieved. Good uniformity is also obtained for beam energies in the range 115MeV-150 MeV, showing robustness of the setup. Furthermore, the range of the proton beams traversing the beam-shaping elements, as well as the energy arriving to target, were studied. Moreover, with a ridge modulator, Spread-OutBragg peaks were obtained with a width up to 3,4cm and of uniformity 98,5%.

The MC model produced in TOPAS was first benchmarked against a 150MeVproton beam in air. Secondly, the experimental data of the large fields were compared with the simulated ones. The simulation of depth-dose distributions and lateral beam profiles agreed with the experimental data. Furthermore, the model was used to estimate the number of initial protons required to achieve an experimental dose and to estimate the location of the Bragg peaks.

All in all, the dual-ring passive scattering setup has been successfully implemented and is ready to be used to perform radiobiological experiments. Also, the TOPAS MC model can be used to assist in the preparation of these experiments and to further optimize the beamline design.

The scope of a radiotherapy treatment is to induce damage to tumor cells by irradiating them with ionizing radiation. The radiation should target the tumor as visible in images, also called the macroscopic disease. There is, however, a risk of small, invisible groups of cancer cells in the area surrounding the macroscopic tumor, the microscopic disease. For an optimal clinical outcome of radiotherapy, the microscopic disease needs to be irradiated as well. In current treatment planning, the macroscopic tumor is extended with a margin to better ensure full disease coverage. Because there is uncertainty in the extent of the microscopic disease, also the margin definition is uncertain. Moreover, in current planning, the same margin is used for all patients, and there is no patient-specific exploration of the trade-offs between dose extensions to cover potentially present microscopic disease vs radiation-induced toxicity because of enhanced dose in organs at risk (OARs) surrounding the tumor. This study aimed to explore whether the margin concept could be replaced by more advanced, individualized approaches for irradiation of volumes just outside the macroscopic tumor. All plans were generated automatically to avoid bias by human planners and to reduce workload. Two novel treatment planning approaches were investigated. The first focused on increasing dose coverage of the microscopic disease while at the same time controlling the resulting increased dose to the healthy tissues surrounding the macroscopic tumor, including OARs. Several treatment plans were generated to explore a range of trade-offs between dose in microscopic disease and dose in OARs. The results showed that for optimal microscopic disease irradiation, both low and high OAR doses needed to worsen. However, the most significant increase in microscopic disease coverage could be obtained when accepting higher OAR low doses, that is generally less important for induction of negative side-effects.
In the second approach, an expected Tumor Control Probability (expected TCP) cost function was used to control dose delivery in areas close to the macroscopic tumor. Basis of the expected TCP model was a function describing the probability of finding microscopic disease at a specific distance from the macroscopic tumor. The results again demonstrated opportunities to increase dose to areas close to the tumor, at the cost of enhanced doses in OARs. In conclusion, both approaches had a positive impact on microscopic disease dose coverage. However, improved irradiation of the microscopic disease was always at a price of enhanced dose in OARs. Complementary studies, involving clinicians, need to be carried out to investigate if and how the approaches could be used to replace the current planning with fixed margins.

Numerical models are paramount in describing the complex physical world around us. They are often based on non-linear functions, which have to be solved in order to run a simulation. The first goal of this thesis is to compare the Jacobian-free Newton-Krylov method against the regular Newton-Raphson method for solving non-linear equations. When doing this, preconditioning was only used for the Newton-Raphson method. As a second goal, the optimal parameters for solving a coupled system with the Schur complement method are determined. For computational purposes, these analyses are performed using PETSc. The comparison between JFNK and Newton-Raphson were performed by solving a heat equation. The physical system which was analyzed is a one-dimensional radiating rod, with a heterogeneous thermal conductivity and Dirichlet boundary conditions. Firstly this rod was modelled as one single system. For this case, it was found that Newton-Raphson outperformed the JFNK method by a factor of 5-1700, depending on the type of preconditioning used. Secondly, the rod was modelled as a coupled system by solving two parts of the rod separately. In this case, it was found that the JFNK method outperformed the preconditioned Newton-Raphson method by a factor of 9.1± 0.3. It was concluded that the JFNK is only favorable over regular Newton-Raphson for coupled systems. To achieve the second goal, an incompressible Navier-Stokes coupled system was solved using the Schur complement method. The coupled system originated from incompressible flow in a back-step pipe, using a finite element method. For the Schur complement parameters, it was shown that using an approximation of the Schur complement offered a significant increase in efficiency. The momentum and pressure subsystems were analyzed separately. The momentum subsystem performed best with a relative tolerance of τ_r =10^-4.5, while the pressure subsystem performed best with a tolerance of 10^-2. With these parameters, the Schur complement method had the same computational time as the pressure-correction method, which was used as a benchmark. For future research, there are three main recommendations. Firstly, to use preconditioning for the JFNK method. This was not done in this thesis because of technical constraints, but it is theoretically possible. Secondly, if the JFNK method is preconditioned, it could be used for the incompressible Navier-Stokes simulation. Finally, there are combinations of parameters which were not tested for the Schur complement method. Trying out more combinations might result in finding an even more optimized method.

Purpose/Objective: The aim of the thesis project is to design and build a sample-holder that is suited for radiobiological experiments on cell and tissue level in Holland PTC. In order to create accurate dose plans for radiobiological experiments, dosimetry and range calculations need to be worked out. Finally the sample-holder is used for the first radiobiological experiment at Holland PTC. Materials and Methods: The sample-holder is constructed as a slab phantom that allows secure and easy positioning of a 6-well cultivating plate, fully made out of polystyrene. Water equivalent thickness (WET) measurements are used to calculate the density of the slabs, which can be used for dose calculations in treatment planning and Monte Carlo software. Radiochromic EBT3 film is used to perform dosimetry during experiments, the films are calibrated using an ionization chamber to be able to perform absolute dosimetry. A 70 MeV single pencil beam is delivered to the sample-holder with EBT3 films in between the slabs to compare a reconstructed Bragg peak from the relative doses of the films with dose calculations performed in RayStation and TOPAS MC. Cell survival experiments with U2OS cells are performed by irradiating with X-rays and protons. Circumstances where such that the influence of cells being on room temperature for several hours and cells being without medium inside the wells during irradiation could be studied. With protons, cells were irradiated both in the plateau region and in the Bragg peak. LETd calculations are performed in TOPAS MC in order to study the RBE-LET relation. Results: The slabs of the phantom are uniform and have a density of either 1.04 g/cm3 or 1.06 g/cm3 Using these density values the range of a 70 MeV can be calculated with a 0.5 mm accuracy using TOPAS MC and RayStation. The relative central axis dose profile calculated with TOPAS was accurate within 5% compared to the measured values with EBT3 film. This was 20\% for calculations with RayStation. Absolute dose measurements with EBT3 film inside a RW3 phantom agree within 1\% with the delivered dose according to the ionization chamber. In the polystyrene sample-holder this difference is 11%. For X-ray irradiation, incubated cells were able to form more colonies after irradiation compared to cells that had been on room temperature for four hours. Cells that were irradiated without medium in the well had a higher surviving fraction than cells that had medium in the well. For proton irradiation the results are limited since all cells that received more than 0.91 Gy had died, but from the available data it can be seen that irradiation in the Bragg peak results in more cell death compared to irradiation in the plateau region. In addition to this, cells that had medium in the wells during irradiation formed less colonies than cells that had no medium in the wells, which is consistent with the results after X-ray irradiation. Dose-averaged LET (LET_d) values are calculated at cell depth for proton irradiation in the plateau region and in the Bragg peak and are 1.30 keV/um and 3.78 keV/um respectively. Conclusion: The sample-holder is well defined for use in RayStation and TOPAS MC and range calculations of a 70 MeV pencil beam are accurate within 0.5 mm. Absolute dose values obtained with EBT3 film inside the sample-holder are higher than expected, additional measurements are recommended. Cell survival experiments show that there is a difference in irradiating cells that are in medium vs cells that are not in medium and that long exposure to room temperature increases cell death after irradiation. Radiobiological experiments should be performed with cells that can be prepared and stored on site. The obtained LETd values are comparable with values found in literature. Due to the limited amount of cell survival data after proton irradiation, not link between RBE and LET can be studied.

In Intensity Modified Proton Therapy, the number of energy layers and the number of beamlets determine the radiation time and the plan calculation time. The purpose of this research project is to test sparsity inducing terms to reduce both the energy layers and the beamlets.

Purpose - To develop and evaluate a fast and automated multi-criterial treatment planning strategy for High-Dose-Rate (HDR) brachytherapy (BT) for patients with locally advanced cervical cancer. This automated strategy avoids suboptimal and slow manual treatment planning and results in reproducible and conformal treatment plans with a clinically favorable trade-off between multiple treatment objectives. Methods and Materials - An automated treatment planning approach was developed using the Erasmus- iCycle framework. A wish-list containing hard constraints and prioritized objectives is required as input was configured according to the clinical protocol using 22 single-fraction training plans. Special at- tention was paid to establishing the clinically desired ‘pear-shaped’ dose distribution. To evaluate the dwell time optimization approach, 66 automatically generated single-fraction plans (PLANauto) were compared against the clinically delivered plans (PLANref ), both by blind-pairwise comparison carried out by an expert clinician and by the analysis of dosimetric plan parameters. Subsequently, for 17 complete fractioned BT treatments each consisting of 3 single-fraction BT plans, automatically generated plans (TREATMENTauto) were compared against the clinically delivered plans (TREATMENTref) to evaluate dosimetric plan parameters according to the clinical protocol. The possibility of extending the algorithm with a needle selection objective was also explored on 13 test cases and its performance was evaluated by studying the number of needles selected in the optimized plans and its effect on the remaining treatment objectives.Results - All PLANauto were considered clinically acceptable by the clinician. The clinician’s plan comparison pointed strongly at an overall preference for the automated plans: in 62/66 cases the clincian preferred PLANauto over PLANref , in three cases the overall quality was considered equal and for one case the clinical plan was preferred. For PLANauto, the mean HR-CTV D90% improved while also the rectum was spared compared to PLANref . The average optimization time was 19.5 seconds (range [4.4 – 106.4] s). The mean D90% for TREATMENTauto improved by + 3.0 Gy (in EQD2) (p<0.005) over the whole radiotherapy treatment with differences ranging from -4.3 to +6.0 Gy, while the bladder and rectum were spared similarly (p=0.01, p=0.02 respectively). In 6/13 of the plans, the number of needles that were implanted could be reduced while still establishing sufficient plan quality. Conclusions - Fast automated multi-criterial treatment planning for locally advanced cervical cancer HDR-BT is feasible. High-quality treatment plans are automatically generated within a clinically acceptable time frame and treatment plans have a clinically preferable trade-off between all treat- ment objectives. The observed improvement in dosimetric parameters, mainly the improvement of the dose to the HR-CTV, is clinically relevant. The algorithm can be extended with an approach for the optimization of the implant geometry, which could allow interactive intra-operative treatment planning.