To reduce the probability of complications in proton therapy, while maintaining a high tumor control probability, adaptive treatment can be applied. In adaptive treatments, the treatment plans are frequently assessed and adjusted to, for example, account for anatomical changes. Ideally, daily adaptive treatment would be implemented, where every day a repeat scan is made and the treatment plan is adjusted. However, due to computational limitations, daily adaptive treatment is not possible yet. Quality assurance (QA) is a vital part of the proton therapy workflow. Intending to implement daily adaptive treatment, fast-working quality assurance tools are necessary. The goal of this thesis is to calibrate and adapt a deterministic proton transport algorithm to reconstruct the delivered dose using log files, which can be compared to the planned dose as a QA tool.

To achieve this, Yet anOther Dose Algorithm (YODA) was calibrated to the popular planning software RayStation, by optimizing the input parameters of YODA to minimize the difference in dose between YODA and RayStation for a 0° gantry angle single spot in a homogeneous water phantom. From this calibration, beam data library (BDL) files were created for cases without a range shifter and with range shifters of thicknesses 2 cm, 3 cm and 5 cm and planned spot energies ranging from 70 MeV to 190 MeV. An approximation to improve the inclusion of the effect of nuclear interactions was added to YODA. The lowest passing rate found was 99.47% for a 190 MeV spot without a range shifter, therefore the calibration was successful. The passing rate decreased for high energies, likely due to the crude approximation to deal with nuclear interactions. Other ways to include
the effect of nuclear interaction should be investigated to further improve the calibration results. After the calibration was complete, three experiments were performed.

First, simple treatment plan comparisons were performed using the BDL files and a homogeneous water phantom, including plans containing a single spot irradiated from gantry angles ranging from 10° to 90°. To account for the angled beams, a beam splitting algorithm was used. It was observed that the BDL files are specific for the isocenter to CT volume surface distance, as the boundary conditions of YODA are at the CT volume surface while the treatment plans define spots at the isocenter in air. To include this effect, the BDLs should contain beamlet parameters at the isocenter, from which the beamlet parameters at the CT volume surface can be calculated. The calibration procedure for the spatial spread, angular spread and correlation needs to be adjusted. Alternatives to the beam splitting algorithm should be investigated, as this algorithm inadequately reflects reality when a beam enters the CT volume under an angle.

Second, YODA assumes that spots are laterally symmetrical, however, in reality, spots are ellipse-shaped. To solve this, the asymmetrical spot solution to the Fermi-Eyges equation was derived. The difference in integrated Fermi-Eyges flux between the symmetrical and asymmetrical spot was calculated for realistic combinations of lateral spread, angular spread and correlation. The mean error in integrated Fermi-Eyges flux, relative to the maximum, induced by assuming lateral spot symmetry, around the central beam axis is significant, as it ranges from 0.6853% to 3.071% at the entrance of the CT volume and from 0.4710% to 4.957% at the Bragg peak. Therefore the asymmetrical spot solution should be implemented, after which, YODA should be re-calibrated

Third, the error induced by systematic errors in the log file was investigated. The plan contains a target cube centered in the homogeneous water phantom irradiated by a 0° gantry angle beam. By perturbing each energy layer randomly using a uniform distribution, where the maximum perturbation was 0.8 mm in spot position, 1% for MU and 0.1% for energy, the magnitude of the dose difference was calculated. The mean differences in dose in the target cube induced by randomly perturbing each energy layer were 0.1204% ± 0.0408% in x, 0.1700% ± 0.0324% in z, 0.2592% ± 0.1595% in MU and 0.4802% ± 0.1558% in energy. These differences are small compared to the error induced by assuming symmetrical spots, note that the differences in spatial spread were an order of magnitude bigger than the perturbation in spot position. Before this investigation on the effect of systematic errors can continue, the asymmetrical spot solution needs to be implemented and the problems discovered with angled beams should be solved.

The calibration was successful, and additional extensions and alterations have been identified to further improve YODA before it can be used as the described patient-specific quality assurance tool.

The goal of radiotherapy is to maximize the dose to the target while minimizing the dose to normal tissue. Treatment plans are optimized for this goal and dose delivery is improved in accuracy and precision. The optimization is based on the delineations of the volumes of interest on the medical images of the patient. The problem is that these delineations contain uncertainties and the effect of these uncertainties become more pronounced as the accuracy and precision of dose delivery improves. The delineation uncertainties are caused by various factors such as knowledge and experience of the observers, guidelines, and image quality and modality. The goal of this thesis is to research the effects of delineation uncertainties. Uncertainties are characterized using a rolling ball algorithm (RBA) to modify the delineation. The radius of the ball represents the uncertainty and ranges from -5 mm to 5 mm for the clinical target volume (CTV) and -2 mm to 2 mm for the brain stem. The effect of these uncertainties will be researched for both fixed and re-optimized dose distributions. These are then used as input to create a model that will simulate the dose distributions for different uncertainties using Polynomial Chaos Expansion (PCE). PCE will be used to model dose volume histograms (DVH) of the CTV and the brain stem. The dosimetric effect will be determined by setting confidence intervals in D98% of the CTV and D2% of the brain stem. The widths of the confidence intervals in these two metrics represent the dose uncertainty. This thesis uses three sets of patient data. Each patient had a CTV close to the brain stem. Dose uncertainty in the two metrics was found to increase as the uncertainty of CTV and brain stem delineation increases. For a fixed dose distribution, a delineation uncertainty of 0.75 mm to 1.25 mm in the CTV would lead to a dose uncertainty of 2 Gy in the D98% of the CTV. A delineation uncertainty of 0.25 mm to 0.5 mm in the brain stem would lead to the same dose uncertainty in D2% of the brain stem. For a re-optimized dose distribution, a combined delineation uncertainty of 1.25 mm for two patients and 4.5 mm for one patient would lead to a dose uncertainty of 2 Gy in D98% of the CTV. For the same dose uncertainty in the D2% of the brain stem, there would be a combined delineation uncertainty between 1.0 mm and 4.5 mm. For all three patients, the dose uncertainty in the D2% was more sensitive to delineation uncertainties in the brain stem. The effects of these uncertainties will depend on the nominal situation. For all patients a 2 Gy dose uncertainty corresponds to 2.85% of the prescribed dose to the CTV and 3.33% of the maximum dose constraint of the brain stem. One patient was on the boundary of underdosing the CTV in the nominal situation and a 2 Gy uncertainty would be sufficient to underdose the CTV. The nominal D2% for all three patients was sufficiently low that it is unlikely it would exceed the maximum dose constraint.

Computational power is a challenge when it comes to the high-fidelity modeling of nuclear reactors. Detailed simulations of reactor physics involve complex calculations that require significant computing resources, which can be time-consuming and expensive. Reduced Order Modeling (ROM) allows for an approximation of a complex model by only capturing the essential features, thereby reducing the computational load. A reduced order model provides computationally efficient approximations of a system, but it requires still many evaluations of a high-fidelity model to capture all the dynamics. Using the adaptive sparse grid can reduce the number of evaluations needed, though the construction of the reduced order model is still computationally intensive.

The aim is to minimize the computational workload involved in constructing a reduced-order model during the offline phase. This is achieved by decreasing the number of high-fidelity model evaluations necessary for building the reduced order model while maintaining accurate results. To this end, the existing adaptive proper orthogonal decomposition algorithm is enhanced by employing multi-fidelity techniques. Multi-fidelity methods aim to combine large amount of low-fidelity data with a limited amount of high-fidelity data to compute accurate, yet computationally inexpensive approximations. Two novel multi-fidelity reduced order model methods based on proper orthogonal decomposition are proposed; Filtered Bi-Fidelity Adaptive Proper Orthogonal Decomposition (FB-POD) algorithm and Adapted Bi-Fidelity Proper Orthogonal Decomposition (AB-POD). These models are evaluated on two different test cases, and the balance between the accuracy of each multi-fidelity ROM and the computational cost, measured by the number of high-fidelity evaluations, is investigated. In specific cases, the proposed methods significantly reduce the number of high-fidelity evaluations compared to the single high-fidelity ROM, while yielding comparable accuracy.

This thesis outlines the project conducted at HollandPTC for the Master Biomedical Engineering, track Medical Physics, at Delft University of Technology.

Metal implants create dosimetric uncertainties in treatment planning and delivery for proton therapy. The purpose of this thesis was to determine the most suitable treatment planning strategy for proton therapy in the presence of cranial fixation clips.
A phantom was designed and imaged using computed tomography. Subsequently, base experiments were performed on a mono-energetic proton beamline to investigate the dose perturbation caused by the clip. It was found that the presence of the clip in the radiation field produces a measurable signal perturbation and that the clip affects the field uniformity.

A total of seven different treatment planning strategies were created. These strategies were based on four main concepts that were determined from a literature review and were identified to have potential effectiveness in addressing cranial fixation clips for brain cancer: implementing a density override on the implant, employing either a single-field optimization (SFO) or multi-field optimization (MFO) technique, implementing beam-specific margins or not, and adding an avoidance margin on the implant.
All strategies, except for the one including an avoidance margin around the implant, were successfully implemented into the treatment planning system. The plans were robustly optimized and met the clinical goals regarding clinical target volume (CTV) coverage and organ at risk (OAR) sparing.

The strategies were evaluated by performing experiments on a clinical treatment gantry. Based on the results of the first set of experiments, a Lynx detector was used, which was currently not able to detect the measured signal in dose in Gray. The aim of the experiments was to gain insights into the agreement between the data from the treatment planning system and the experiments, as well as to investigate what the best strategy is for handling cranial fixation clips in proton therapy.

Based on the results of the conducted experiments, there is an indication that the field homogeneity inside the target volume gets perturbed by the clip. This indicates a high need to investigate the clinical implications of this further. In particular, absolute dose measurements must be added to the presented results.

Geothermal energy can be promising to help the energy transition. Warm water is pumped up from a geothermal well and after it is cooled down, the cold water is reinjected into the hot aquifer underground. The temperature change could cause chemical balances to be disturbed and result in precipitation on the porous medium of the aquifer. This process of clogging of an aquifer is not desirable since it re- duces the efficiency of the production of warm water. This research is a stepping stone for modelling these reactions and clogging of the aquifer. The reactions rates depend on the temperature and the concentration of the reactants in the aquifer. For this reason the temperature distribution of the aquifer was numerically derived with finite difference methods. The velocity field is important for modelling both the temperature and the concentrations. Therefore the pressure and velocity field were modelled with use of mass conservation and the Darcy model. The models were solved numerically with finite difference methods and some results were compared to the analytical solution of the model. It was concluded that the numerical model for the velocity field is accurate when compared to analytical solu- tions of the model equations. In the model used, an increase in porosity resulted in a delay of cooling the aquifer. This is because there was no relation between porosity and permeability implemented in the model. A lower flow rate in the aquifer delays the cooling of the aquifer as well. For further research it is recommended to implement the chemical reactions into the model and use this model to look into how to prevent the clogging in the aquifer.

This thesis aimed to validate Beekman's patent on how collimation could virtually reduce the focal spot size of an existing small-animal cone beam Computed Tomography (CT) system to diminish the penumbra effect. The ever-remaining drive to improve the spatial resolution in CT for enhanced image quality introduces the need for small focal spot sizes, as the focal spot size is directly related to the geometric unsharpness in the image. Therefore, a collimation method was proposed to virtually reduce the focal spot of existing systems as an alternative to fully replace the current X-ray tube.

A collimator was designed, consisting of numerous tiny hexagonal-shaped holes focused at the center of the focal spot. Theoretical derivations were formulated for its dimensions, and the collimator's efficacy was validated using Monte Carlo simulations. It was concluded that it is theoretically achievable to use collimation to virtually reduce the focal spot size to an arbitrarily chosen smaller virtual focal spot for existing CT systems, significantly reducing the penumbra effect, without requiring any integral changes to the X-ray tube. However, the collimator's practical suitability and manufacturing feasibility were problematic due to its significantly low collimator sensitivity and exceptionally tiny dimensions.

Future work could build on this thesis by obtaining reconstructions from multiple projections of the virtual focal spot to quantitatively assess their theoretically improved spatial resolution. The quantitative confirmation could further establish the theoretical effectiveness of focal spot collimation for future work to enhance reconstructions to uncover valuable information previously hidden.

High-fidelity models are computationally intensive to work with in many-query applications, such as the design process of small modular reactors. A reduced order model of the high-fidelity model can still accurately determine the quantities of interest with only a fraction of the computational cost, and thus can potentially solve the aforementioned problem. However, reducing a high-fidelity model of a small modular reactor is complicated due to the large number of parameters involved in nuclear reactor models, as that leads to an exponentially increasing parameter space needed to be surveyed.

Disregarding the parameters with the smallest impact on the output and thereby reducing the total number of variables can limit the parameter space, and thus speed up the model reduction at the cost of some accuracy. Perturbation theory utilizes the benefits of adjoint theory to efficiently determine sensitivities of responses to all the variables in the model, which allows one to find the influence on the output of the different variables without the need for evaluating them all repeatedly. It is then possible to apply a reduced order modeling technique such as proper orthogonal decomposition to determine and select only the most important eigenmodes of the high-fidelity model and build a reduced model.

This approach is applied to three different neutronics model of the U-Battery, varying in complexity. The presented method has shown to alleviate computational cost significantly for all examined reduced models. However, the upfront cost of building the reduced models by sampling the high-fidelity models has been considerable, especially when evaluating the resulting accuracy of the reduced models. A proper selection of the proper orthogonal decomposition tolerances must be made to ensure sufficient accuracy and to prevent oversampling. Nonetheless, combining proper orthogonal decomposition with perturbation theory showed to be a promising way of selecting only a few parameters for participation in the building of reduced order models while minimizing the loss of accuracy compared to the high-fidelity model.

Treatment planning for radiation therapy is a complex process, as there are many machine parameters to determine for a treatment. To decrease the required labour and improve the plan quality, auto-planning systems have been developed, which can automatically generate high-quality treatment plans. These plans still have to be checked to ensure their quality, which means an independent and automatic quality assurance method is needed. This is where deep learning comes in, as recent advancements in computer hardware and artificial intelligence make it possible to quickly train neural networks for fast predictions of dose distributions based on the anatomy of a certain patient. Even though these models have proven capable of generating predictions comparable to the actual delivered dose, there is no guarantee that the distributions are actually deliverable. Therefore, this study is conducted to develop a novel deep learning model that uses physical information of how dose is delivered to constrain the neural networks and force realistic and deliverable dose distributions. The models presented in this study are implemented on prostate cancer patients treated with the Volumetric-Modulated Arc Therapy (VMAT) delivery technique.

First, the conventional Anatomy-based dose prediction model is further developed to improve the quality of the predicted dose distributions. The goal is to assess how accurate the predicted dose is from a deep learning model without any physical constraint. The model with U-net architecture has shown to be capable of predicting dose distributions of very high quality with average DVH differences of less than 1 Gy and errors in the dose coverage statistics on the PTV of less than 1\%. Furthermore, the lack of deliverability was confirmed due to the missing ray effects in the dose distributions, showing no clear entry points of the external photon beam. This lack of ray effects was quantified and confirmed using the dip in the S\o rensen-Dice Index around the 30\% isodose contours, where the ray effects are most apparent.

Second, the Physics-guided prediction model was developed by combining the Anatomy-based dose prediction model with the newly developed Segment prediction model, which predicts Multi-Leaf Collimator (MLC) positions and beam intensity values for all directions from which the dose is delivered. These predictions are based on the prediction from the Anatomy-based dose prediction model and the Beam's Eye View images of the CT and the delineated structures. The segment prediction is used in a stand-alone dose engine, based on the Matrad dose calculation algorithm, to calculate a dose distribution from the predicted treatment plan, which is deliverable by definition. The performance of the Physics-guided prediction model is underwhelming, scoring considerably worse on most evaluation metrics compared to the Anatomy-based dose prediction model. The lack of performance is mainly caused by the poor performance of the Segment prediction model component.

To circumvent the issue of the Segment prediction model, a dose mimicking model has been developed and investigated, where a dose mimicking algorithm is used to mimic a prediction from the Anatomy-based dose prediction model by optimizing the segments on which the dose is delivered. This model has only been tested on a single patient but seems to be performing better than the Physics-guided prediction model, with the downside that an optimisation process is involved. Still, the performance is not on par with the Anatomy-based dose prediction model.

Further investigation on the Segment prediction model will be needed before the Physics-guided prediction model can be considered as a serious quality assessment tool. Once performance of the Segment prediction model has increased, it could prove an interesting model that could even replace an auto-planning system in the far future.

Treatment of early stage breast cancer is generally invasive to a patient's daily life while being treated. Therefore, to diminish the physical and psychological impact during and after recovery, a newly proposed minimally invasive therapy for early-stage breast cancer treatment is proposed. Within this study, this proposed treatment is modelled to gain more knowledge of the behavior of the treatment material in tissue. The treatment includes magnetic thermal ablation, which is combined with permanent Low Dose Rate (LDR) brachytherapy, both performed simultaneously.
The goal of the treatment is to diminish the physical and psychological impact during treatment and after recovery, since it requires only a single medical intervention. The treatment material consists of radioactive palladium-103 superparamagnetic iron-oxide nanoparticles (Pd-103 SPIONs) incorporated in a solid gel, forming a seed that is implanted into the tumor. To investigate the effectiveness and limitations of the combined therapy, computational simulations were performed in Matlab using the Finite Element Method (FEM).
These simulations allowed for the prediction of the treatment results, by calculating the temperature distribution based on Pennes' bioheat equation, the nanoparticle concentration distribution and the dose distribution over time. The sensitivity of the results to the relevant physical properties and optimization parameters was analyzed. The latter resulted in a recommended optimization approach that ultimately could be used for treatment planning. First, an initial simulation was performed using property values from literature. Then, the temperature and dose results were tested on their sensitivity to model parameter changes. The temperature model was found to be most sensitive to changes in the nanoparticle heat source value Qnp, to an increased heat conduction coefficient k and to a decreased blood perfusion rate wb. The cumulative dose results are sensitive to both the initial concentration ci and to a decreased diffusion coefficient D. It is concluded that accurate values for these temperature and concentration model parameters are necessary to perform relevant simulations.
Furthermore, the possible optimization parameters were identified. For dose optimization, these parameters are the activity of the nanoparticles A, which is not easily modified, and the initial nanoparticle concentration in the seeds ci, which affects the temperature distribution as well. The temperature distribution specific variables that were found, are the strength of the magnetic field H and the time t of magnetic field application, which both can be adjusted during the treatment. The seed location and number of seeds are two additional adjustable variables used for optimization of both temperature and dose distribution. Lastly, it was concluded that the internal radiation part of the treatment is limiting in the reaching treatment goals and in number of optimization possibilities, compared to the thermal ablation part. Therefore, treatment optimization should be performed on the dose distribution first. Because most limitations of the models are a result of the 2D representation and because these limitations strongly affect the outcomes of the models, it is recommended to transform these models to 3D. These limitations make it impossible to do proper treatment planning with the 2D model, which requires a 3D view of the results. With all these findings, this study has contributed by providing basic knowledge of the state-of-the-art early stage breast cancer combined therapy, bringing it one step closer to clinical implementation.

The delineation of the regions of interest (ROIs) plays an important role in the radiotherapy workflow. The ROIs include the gross tumor volume (GTV), the clinical target volume (CTV), the planning target volume (PTV) and organs at risk (OARs). There are however uncertainties related to the delineation of the ROIs due to inter­observer variability between radiation oncologists caused by e.g., a lack of consensus on anatomic definition or a lack of contrast in medical images. The largest inter­observer variability has been found in esophageal cancers, head and neck cancers, and lung cancers. To pre­ vent inter­observer variability, auto­contouring software can be used to delineate the ROIs. There are however also uncertainties in the delineations made by auto­contouring software. The purpose of this study was to perform an accurate evaluation of the dosimetric effect of delineation uncertainties. To do so, first a method to characterize the delineation uncertainties had to be found. The delineations uncertainties were characterized with principal component analysis (PCA). By performing PCA on a set of delineations, the eigenmodes which represent the variations between the delineations can be found and new random delineations based on the eigenmodes can be formed. To determine the dosimetric impact of the delineation uncertainties, two cases were investigated: (1) the dosimetric impact of delineation uncertainties for a fixed dose distribution; and (2) the dosimetric impact of delin­ eation uncertainties for a dose distribution reoptimized for every possible realization of a delineation. For the fixed dose distribution, Polynomial Chaos Expansion (PCE) was used as a meta­model for the dose volume histogram (DVH) of the target, and for the reoptimized dose distribution PCE served as a meta­model for the total dose distribution and the DVHs of the target and other ROIs. The delineation uncertainties of two data sets were analyzed: (1) 12 manual delineations of the GTV of a hepatocellular carcinoma patient; and (2) 90 auto­contours of the CTV and brainstem of a head and neck patient. The variation of the manual delineations of the GTV could be accurately described by 5 eigenmodes, while 45 eigenmodes were needed to describe the variations of the brainstem auto­ contours. The delineation uncertainties in the auto­contours of the CTV could not be characterized by PCA due to the shape of the CTV. The dosimetric effect of the delineation uncertainties of the manual delineations for a fixed dose distri­ bution was investigated for both an intensity modulated proton therapy (IMPT) plan and a volumetric­ modulated arc therapy (VMAT) plan for 10,000 random delineations of the CTV. For this patient the CTV was equal to the GTV. In the VMAT plan, the CTV received sufficient dose for all delineations, but the PTV was underdosed in 17.1% of the delineations. In the IMPT plan, the CTV was underdosed in 69.0% of the delineations. This percentage of underdosed CTV delineations in the IMPT plan was reduced when the plan was made robustly. The results for the fixed IMPT dose distribution should however be verified with a more accurate PCE model. A proof of principle for the PCE as a meta­model for the reoptimized dose distribution and DVHs of the target and other ROIs was shown. However, a more accurate PCE model with a higher polynomial order would be needed to analyze the effects of the reoptimization on the dose delivered to the ROIs. It has been shown that the dosimetric impact of delineation uncertainties can be modelled using PCE. This is a first step towards systematically and quantitatively taking into account delineation uncertainties in radiotherapy treatment planning. In future research the analysis of the dosimetric impact of the un­ certainties can e.g., be used in adaptive radiotherapy in which auto­contouring is used. By knowing the dosimetric impact of the uncertainty, treatment plans could be optimized such that a structure receives its target dose with a certain probability or critical areas where the dosimetric impact of the delineation uncertainties is large could be flagged such that these areas are checked before treatment.

Radiotherapy is one of the main treatment modalities available to treat cancer. Radiotherapy treatment plans are created based on CT scans of the patient. In such scans the macroscopic tumor is visible, but microscopic disease present in the surrounding tissue cannot be observed. To achieve an optimal clinical outcome, both the macroscopic and the microscopic disease must be treated. Currently, the macroscopic tumor is extended by a margin into the Clinical Target Volume (CTV) to include the microscopic disease in the treated volume. The same margin is used for all patients, although the extent of microscopic disease is patient-specific and can vary largely among patients.

In this study, probabilistic treatment planning was investigated as a method to replace the margin concept. Probabilistic models were created by explicitly modeling uncertainties in the microscopic disease into an objective function used in the treatment plan optimization. By optimizing either the expected Tumor Control Probability (ETCP) or the expected Logarithmic Tumor Control Probability (ELTCP), optimal dose distributions could be obtained. Two different one-dimensional models for probabilistic treatment planning were investigated.
In the first model, the uncertainty in the extent of the microscopic disease was modeled into an objective function. This was done using a function that describes the probability of finding microscopic disease at a certain distance from the macroscopic disease. In the second model, the uncertainty in the tumor cell density in the microscopic disease area was modeled into an objective function. The uncertainty was modeled by defining the tumor cell density field as a random field and generating different realizations of the tumor cell density field using a Karhunen-Loève (KL) expansion.
For the first model, both the ETCP and the ELTCP were used as objective functions and in the second model, only the ETCP was used as an objective function. Furthermore, a penalized ETCP objective function was investigated for both models. In this penalized objective function a penalty on the dose was used to allow for controlling the balance between tumor control and sparing of normal tissue.

Using the first model, two different types of dose distributions were found. When the ETCP was optimized, the maximum dose was given to as large a volume as possible and no dose was given in the rest of the investigated volume. When the ELTCP was optimized, dose was given throughout the volume, so that the whole volume received as much dose as possible. Optimization of both objectives resulted in good tumor control. When the penalized ETCP was optimized, dose was given to a much smaller part of the volume than with the unpenalized objective, while the tumor control was still good.
Using the second model, it was shown that the KL-expansion is a promising method to model the uncertainty in tumor cell density. Different shapes of the input mean tumor cell density field were investigated. Optimizing the ETCP resulted in realistic dose distributions. Good tumor control was obtained for the different shapes of the input mean tumor cell density field. Furthermore, using the penalized ETCP, good tumor control was retained, while the dose deposited in the volume was decreased.

In conclusion, probabilistic treatment planning promises to be a good alternative to the current margin concept. It was shown that good tumor control could be achieved in the microscopic disease area using probabilistic objective functions. Both models showed promising results and the penalized objectives showed that it is possible to balance between tumor control in the microscopic disease area and sparing of normal tissue. Additional research is necessary to extend the one-dimensional KL-model into a more detailed three-dimensional model. Furthermore, the objectives need to be implemented in treatment planning systems to create real patient plans. Such studies should be performed in cooperation with clinicians and radiologists.

Proton radiotherapy has a dosimetric advantage over photon therapy to spare healthy tissue closely positioned to the tumor mainly due to the absent exit dose. In The Netherlands, the Proton therapy centers currently take a relative biological effectiveness (RBE) of 1.1 compared to photons to deliver an iso-effective treatment. However, initial clinical evidence indicates a variable proton RBE in brain patients with the linear energy transfer (LET) as an important physical parameter. The LET significantly increases at the end of the radiation field, and contributes to an increased probability to develop brain lesions. With the introduction of radiation response models, the first goal of this thesis is to evaluate the impact of the RBE/LET effect in intensity-modulated proton therapy (IMPT) plans. Furthermore, the main goal is to reduce the RBE/LET effect in treatment planning.

We incorporated the probability of lesions origin (POLO) model published in literature to determine the RBE model-based normal tissue complication probability (NTCP) for three glioma patients treated with IMPT at HollandPTC, The Netherlands. The dose and LET distributions were computed using a Monte Carlo system. For the investigation of the RBE/LET effect in treatment planning, we modified several beam settings of the clinical IMPT plan, including the beam angle, beam energy, and robustness. Furthermore, we combined treatment modalities to reduce the NTCP.

We compared the results of the clinically used IMPT plan with the results obtained by the modified IMPT plans. The local redistribution of LETd leads to a decrease in NTCP up to the point when the LETd becomes uniform. The robustness did not reveal deviations in terms of the NTCP. By choosing appropriate beam angles that result in a smeared out
LETd distribution, the NTCP does not improve for small deep located tumors, improves relatively modest by 11.6% for elongated tumors, and significantly improves by 37.0% for large, superficially located tumors. The inclusion of partial transmission beams lowers the NTCP by 30-50% relative to the clinical IMPT plan while limiting the relative increase in mean brain dose by 5-16%. When comparing the IMPT plan with the photon plan used for plan comparison, the VMAT plan always results in the lowest NTCP and provides a relative improvement in NTCP by 60-75%. Meanwhile, the mean brain dose significantly increases by 50-80% compared to the clinical IMPT plan. Intermediate NTCP-Dmean(brain minus CTV) values are achieved when combining protons with the photons or by including proton transmission beams.

In general, we can conclude that the inclusion of partial proton transmission beams is more promising than choosing appropriate beam angles to lower the RBE/LETd effect. However, further optimization of transmission beams is required. Moreover, an improvement in NTCP is always at the cost of the mean dose to healthy tissue. On top, our results support further investigation to combine different modalities, like protons and photon fractionation.

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.

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.

In proton therapy, the calculation of the dose distribution is of great importance in order to find the best treatment plan. For a treatment plan with high quality, several error scenarios are investigated, in order to come up with a general plan that suits best in these scenarios. All of the scenarios have a different simulated error, e.g., in patient displacement and proton beam range. In order to cope with a large number of errors scenarios, a quick calculation method is needed. In this work, a method that used Reduced Order Modelling (ROM) and subsequently polynomial regression to overcome the computational problem is presented, as well as its results on a real head and neck cancer patient. Before error scenarios are simulated, using inverse optimisation a treatment plan is calculated for a perfectly known position and organ structure of the patient, i.e. the nominal scenario. Then, several error scenarios are simulated and for each of these scenarios, the real dose on tissue is calculated, using the treatment parameters that were previously set for the nominal scenario. The dose distributions of all these scenarios are stored in a matrix, on which a Singular Value Decomposition (SVD) is executed. A reduced order of singular values and vectors is used, together with regression models on the right singular vectors, to reconstruct the dose distribution matrix. This is done in order to create a computationally
cheap method for dose distribution calculations. The right singular vectors are now a function of the used parameters: positioning errors dx, dy, dz and proton beam range error dρ. The dose distribution matrix is reconstructed from the left singular vectors, first few singular values and the function for the right singular vectors. Range errors with a uniform distribution with a maximum of 3% and positioning errors with a normal distribution with a standard deviation of 3 mm in each direction were simulated. For 100 error scenarios, the dose distribution matrix could be reconstructed with an average accepted error of 1% on a voxel relative to the maximum dose for 96:5% of the voxels, using a voxel-by-voxel comparison. These results were obtained for a 17th order of the SVD and a 7th order polynomial in the regression. On unseen test data, the acceptance was 81:0% of the voxels with an allowed error of 1%. For an allowed error of 3%, 92:5% of the voxels of this test set were accepted. These results are promising and encouraging for future research. First of all, it is recommended to examine the regression of the right singular vectors more closely, especially the regression of the higher order right singular vectors. Further subjects of interest would be to perform the same procedure that is done on the dose distribution matrix on the dose influence matrix. Also, harder-to-reach error scenarios,
like organ movement and tumour deformation could be taken into account. This research was conducted at the Medical Physics and Technology section, a part of the Department of Radiation Science and Technology at Delft University of Technology. The project was part of the Bachelor program of Applied Physics at the Technical University of Delft.

The freeze plug is a key safety component of the molten salt fast reactor (MSFR), one of six next-generation nuclear reactor technologies being developed under the Generation IV International Forum (GIF). It should be designed to melt if an accident occurs, allowing the MSFR to drain before it incurs structural damage.
Two freeze plug concepts have been considered in recent years, in which the plug is melted either through the decay heat produced in the core, or through heat generated by special heating rings and stored in steel blocks adjacent to the freeze plug. Variations consisting of both a single freeze plug, and multiple smaller plugs contained in a metal plate, have been proposed. This work seeks to evaluate the feasibility of these designs and study how parameters such as the sub-cooling of the plug affect melting times. Additionally, an alternative, wedge-shaped freeze plug design is proposed for increased reliability. 
Simulations performed in COMSOL showed that the decay heat plug melts within 600 s only if placed within 0.01 m of the mixed core flow. Because such a placement makes the plug vulnerable to temperature and velocity fluctuations in the core during regular operation of the reactor, this design is considered unfeasible and is not recommended for further study. On the other hand, melting times under 600 s were possible with the heating ring design for a range of sub-cooling amounts and plug configurations, suggesting that this
design is promising. A thin frozen layer was shown to form on top of the metal grate in the multi-plug configurations, preventing heat transfer through the top of the plate. Although the melting behavior of this layer warrants further investigation, its insulating effect was found to generally cause the single-plug designs to melt faster than the multi-plug designs.

A simplified, isothermal model of the wedge-shaped plug was simulated using the enthalpy-porosity approach to account for convection. To model the sinking of the wedge, an extended Darcy term approach was developed based on an analytical solution which was validated experimentally, with good agreement. This model shows that melting of the wedge is unsteady, and that melting times depend linearly on the wedge angle and sub-cooling. Unfortunately, melting times of the wedge plug could not be estimated with realistic, non-isothermal, time-dependent boundary conditions. For future study, a customizable numerical solver such as OpenFOAM is recommended, which would allow the sinking of the solid phase to be modeled more robustly through an immersed boundary method.

Feasibility study of Dutch concept of deep geological repository for radioactive waste is at present widely conducted in the Netherlands. In this project, transport properties of selenate ion in clay materials were investigated. Apparent diffusion coefficients of selenate ion were obtained as results. To study possible alternations of Boom Clay by alkaline concrete pore water, elemental composition and pore structure of raw Boom Clay samples were characterized.

In medical images intratumour heterogeneity well translates specific cancer cells properties in a fast and non-invasive way. Medical community and researchers have developed proper tools (texture features, shape features…) permitting the quantification and the analysis of this tumour heterogeneity in particular in PET images. Yet so far, both doctors and researchers have mainly focused on the use of those tools before assessing their relevance and robustness. In this study, a new heterogeneous PET phantom allowing an extensive understanding of the key concept of heterogeneity quantification in PET images is designed and partially developed. This phantom aims at representing a complex enough environment mimicking clinical conditions to properly challenge PET heterogeneity data extraction and quantification methods further than commercial phantom already do. This study focuses, first, on defining sound specifications, design methodology and manufacturing method for this new object. Second, the study focuses on designing and developing the defined solution. Third, a proof-of-concept analysis is conducted within the study to test and validate the developed PET phantom prototype. It can be concluded that the produced PET phantom was successfully designed and developed and can be used by other operators; yet, there is still room for improvement. Finally, besides developing a novel PET phantom, this study yields recommendations to improve the work done toward a more handy, flexible and realistic tool.