Molten Salt Reactors (MSRs) are a promising Generation IV reactor concept, but their simulation is challenging because neutronics, delayed-neutron precursor transport, thermal hydraulics, and temperature feedback are strongly coupled. This motivates the use of reduced-order models, which aim to reproduce the dominant full-order dynamics at lower computational cost. This thesis compares two POD-based (Proper Orthogonal Decomposition) reduced-order modelling strategies for a one-dimensional molten salt reactor model: intrusive POD--Galerkin projection and non-intrusive POD--SINDy (Sparse Identification of Nonlinear Dynamical systems.

A full-order model was developed for a simplified molten salt reactor loop. The model couples point kinetics with advective delayed-neutron precursor transport, a temperature equation with heat removal, temperature-reactivity feedback, and a lumped momentum balance for the mass flow rate. The model was benchmarked against results from the MIMOSA project and showed good agreement for the reactor power and temperature response, although deviations remained in the precursor concentrations.

Using this full-order model as reference, a POD--Galerkin reduced-order model was derived by projecting the governing equations onto a POD basis. For the 50\% mass flow reduction transient, the Galerkin model achieved relative root-mean-square errors of order $10^{-5}$ for power, temperature, and average precursor concentration. It also showed stable behaviour in time extrapolation, reliable interpolation when training transients were sufficiently close in parameter space, and efficient treatment of time-dependent mass flow due to the affine dependence of the transport operator on the mass flow rate.

The POD--SINDy model was able to reproduce selected training trajectories, but only for low sparsity thresholds, resulting in relatively dense systems with approximately 450 fitted coefficients. Increasing sparsity degraded the accuracy, and extrapolation tests showed sensitivity to both the amount and location of the training data, with several unstable solutions. A direct comparison between the SINDy and Galerkin coefficient matrices further showed that SINDy did not recover the physical reduced-order equations and introduced non-physical couplings.

The results indicate that, for the present model, scaling, candidate library, derivative estimation, and regression procedure, POD--SINDy should be interpreted as a data-driven surrogate rather than a physically interpretable reduced-order model. In contrast, POD--Galerkin provided the most reliable approach, combining high accuracy, stable long-time behaviour, and efficient online simulation. ... Read more

A new generation of nuclear reactor designs promises new advantages, including inherent safety, high outlet temperatures, the ability to burn long-lived waste, or to produce new fissile fuel. One such design is the prismatic high-temperature gas-cooled reactor (HTGR), a graphite-moderated core using helium coolant. Uranium fuel is encapsulated in heat-resistant particles that retain fission products up to 1,600 °C.
Before licensing, computational models must demonstrate that the reactor can shut down passively while keeping the peak fuel temperature below 1,600 °C. This requires a code that is both sufficiently fast to simulate transients lasting up to 1,000 hours, and sufficiently detailed to capture all relevant physics. To reduce the computational burden, the physics are often simplified by assuming axial symmetry or modeling only part of the core. However, incidents with an interplay of local and global effects require whole-core pin-level modeling.... ... Read more

In recent years, various systems have been developed which integrate a linear accelerator with an MR system, enabling high quality imaging during radiotherapy. However, these systems produce strong magnetic fields that cause the secondary electrons to deflect. This leads to significant changes in the dose distribution since the path of the electrons is altered in this magnetic field. Several methods have been developed to accurately determine the influence of a magnetic field on the dose distribution, however, these methods are impractical due to their long computation times. In this work we develop a deterministic DGFEM method for solving the Linear Boltzmann transport equation (LBTE) in magnetic fields for photon therapy. For this purpose, we first developed a deterministic Boltzmann solver based on the discrete ordinate methods. This algorithm was extended to use the DGFEM method and finally a magnetic field term was implemented to determine the influence of a magnetic field on the dose distribution.

The results acquired with the algorithm based on the DGFEM method were compared to exact solutions, these results were consistent with the exact solutions and reported high levels of accuracy. The accuracy of these methods was comparable to those achieved by using discrete ordinates. Furthermore, the cost of the DGFEM algorithm were compared to those of the discrete ordinate method, here it has been shown that the DGFEM algorithm is only slightly more computationally expensive.

The DGFEM based solution algorithm was extended by implementing the magnetic field operator into the algorithm. The deterministic results in the presence of a magnetic field were compared against the MCNP and TOPAS Monte Carlo codes. These results showed similar dose distributions compared to MCNP, however, the deterministic results were not in accordance with the TOPAS simulation. It is suspected that the discrepancy in dose distribution originates from the difference in source spectrum between the two methods.

In order to investigate the influence of a magnetic field, dose distributions were determined with a magnetic field perpendicular to the photon beam. The results showed that the buildup region decreases for stronger magnetic fields and that higher values for the dose are formed at the boundaries between materials with different densities. This increased dose is caused by the electron return effect and becomes more condensed for stronger magnetic fields. Furthermore, a lateral shift in the dose distribution has been observed in the direction of the Lorentz force. These results show that the developed deterministic Boltzmann solver is able to generate accurate dose distributions in the presence of a magnetic field. ... Read more

Nuclear energy offers a promising solution to decarbonize the maritime industry. With the increasing urgency to meet climate goals, innovative nuclear reactor technologies are being investigated to serve as a clean alternative to traditional marine fuels. One of the most promising reactor designs for this purpose are small modular reactors based on the high-temperature gas-cooled reactor concept, due to their compact, passively safe design. However, accurately simulating and optimizing this type of reactor requires computationally intensive models. Therefore, there has been an increasing interest in the development of Reduced-Order Models (ROMs) to speed up reactor simulations. This thesis aims to construct a predictive, non-intrusive ROM that not only simulates the time evolution of the coupled neutronics and thermal-hydraulics dynamics of a high-temperature gas-cooled reactor, but can also predict the system’s behaviour under varying parameter sets (e.g. different initial conditions, material properties, or boundary forcing). The ROMs were developed based on a combination of proper orthogonal decomposition and sparse identification of nonlinear dynamics, and tested on a representative high-fidelity Full-Order Model (FOM). The FOM was developed as part of this thesis and simulates the coupled dynamics of the one-dimensional neutron diffusion equations and the one-dimensional heat equation, capturing the interaction between neutronics and thermal hydraulics. The ROMs were built with transient FOM data to predict the evolution of the neutron flux, the temperature and the precursor concentration in the reactor. The ROMs were evaluated on both accuracy and computational efficiency. The ROM developed to simulate the high-fidelity FOM achieved a reduction in spatial dimensions from 14,318 Degrees of Freedom (DoF) to just 8 DoF, corresponding to a reduction factor of approximately 1,750, while maintaining high predictive accuracy. The ROM was trained and tested on a depressurized loss of forced cooling-like transient event, varying the heat transfer coefficient at the boundaries of the temperature domain as an input parameter. The maximum Relative Root Mean Square Error (RRMSE) the ROMs achieved was 9.9 × 10−3. The maximum RRMSE in the power was found to be 0.05, which corresponds to a power of 4.5 kW, and the maximum RRMSE in the temperature was found at 1.5 × 10−3 , translating to an error of 1.2 K. Additionally, the ROM demonstrated accurate predictive performance when tested on transients with time-dependent heat transfer coefficients, despite being trained only on constant-coefficient transients, highlighting its potential for control-oriented applications. The ROM’s accurate predictive capabilities and significant reduction highlight its potential as a valuable tool for reactor design and safety analysis. Additionally, the way the ROM treats the external forcing parameter shows promising results for control applications. ... Read more

Background: While intensity modulated proton therapy (IMPT) plans allow for increased normal tissue sparing in comparison to photon radiotherapy, IMPT is also more sensitive to treatment uncertainties, including geometrical shifts in patient anatomy and range errors. Additionally, for thoracic treatment sites, e.g. the oesophagus, interference between breathing motion and IMPT treatment delivery, ‘the interplay effect’, can significantly degrade the quality of the dose distribution. To ensure IMPT plans are robust against treatment uncertainties, the dose distribution should ideally be evaluated in a high number of error scenarios. In this way, probabilistic robustness evaluation (PRE) enables the derivation of the probability distribution of relevant dose distribution parameters. Previous research has shown that polynomial chaos expansion (PCE) methods can efficiently perform PRE for head-and-neck and neurological IMPT plans, accounting for geometrical shifts and range errors. The goal of this thesis was to extend the PCE methodology for PRE to include the interplay effect, in addition to geometrical shifts and range errors, for evaluation of clinical IMPT plans of oesophageal cancer patients.
Methods: To include the interplay effect in the PCE methodology, we simulated 4D dynamic dose distributions with 1 set of PCE models per breathing signal. Sinusoidal breathing signals with a period between 3 s and 7 s were assumed. For 1 patient, we studied the effect of PCE parameters – Monte Carlo (MC) noise level, grid order (GO), polynomial order (PO), and PCE coefficient calculation methods – on PCE model accuracy. PCE model parameters and PRE parameters were selected with cost-accuracy analysis and validated for 2 patients. To evaluate the performance of the clinical protocol that was used to create the IMPT plans, PRE was performed including geometrical shifts, range errors, and different breathing signals for 20 clinical IMPT plans of oesophageal cancer patients treated at Holland PTC. We evaluated the probability of adequate CTV dose and scenario distributions of OAR (heart) dose and normal tissue complication probability (NTCP). Finally, we analysed the effect of several factors on PRE outcomes for 1 patient, including presence/absence of fractionation and repainting, extreme breathing signals, and presence/absence of geometrical shifts and range errors.
Results: Lowering the MC noise level improved PCE model accuracy. Selection of the GO3E1PO4 MC1.0% regression model resulted in accurate models for acceptable computational costs. Cost-accuracy analysis showed that 10 000 fully fractionated scenarios and 5 included breathing signals were statistically adequately for PRE, resulting in a total computation time of < 1 day per patient. For all patients, we found a probability of adequate clinical target volume dose (CTV D99.8% > 0.95 Dprescribed) larger than the criterium of 90%, with a population mean 99.56% (minimum – maximum: 98.75% – 99.96%). If fractionation and/or repainting are not considered, ≥ 20 breathing signals could be required for statistically adequate PRE.
Conclusion: We established and validated a pipeline for extending the PCE methodology for PRE to account for breathing motion. Our selected PCE models were as accurate as previously reported models. However, the number of included breathing signals required for statistically adequate PRE could vary depending on the IMPT treatment planning protocol. Our results show that the protocol used to create the included IMPT plans leads to safe, but conservative treatment plans. A lower prescription dose level could be used to significantly reduce the heart dose and NTCP, while still ensuring adequate dose to the CTV.
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As humanity prepares to return to the Moon, understanding its complex radiation environment becomes increasingly critical. Radiation hardness assurance is essential to ensure that the payload remains operational and reliable under prolonged exposure to harsh space conditions. This thesis investigates the performance of the Floating Gate Dosimeter (FGDOS) as part of the Lunar Zebro nano-rover payload developed at TU Delft. The sensor’s response was evaluated through a series of irradiation campaigns: low-dose gamma radiation at the Reactor Institute Delft (RID), proton irradiation at HollandPTC, and mixed-field exposure at CERN’s CHARM facility. While the FGDOS demonstrated linear dose-response behavior and potential for spaceborne dosimetry, several limitations were identified, including reduced sensitivity, temperature dependence, and a firmware-related recharging issue.

A preliminary investigation into the use of a boron carbide layer showed a measurable increase in neutron sensitivity, suggesting its relevance for shielding strategies in mixed-field environments. Although the current payload is not yet flight-ready, it provides a robust foundation for further development. Future work should focus on high-flux gamma testing and refinement of both hardware and firmware to improve measurement reliability and system resilience in the lunar radiation environment. ... Read more

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. ... Read more

Dose calculations in brachytherapy, a form of cancer treatment in which ionising radiation is used to kill cancer cells, are commonly performed using the TG-43 formalism. In this approach, dosimetry is performed in a large water medium and subsequently superimposed onto the patient geometry. This simplification neglects patient-specific factors such as tissue heterogeneities, applicators materials, and finite patient dimensions, limiting its accuracy in clinical practice. With increasing computational capabilities, more advanced physics-based techniques, termed model based dose calculation algorithms (MBDCAs), are being developed that do not have these limitations.

In this work, a deterministic solver originally developed for external beam radiotherapy (EBRT) is adapted for brachytherapy dose calculations. Deterministic methods solve the Linear Boltzmann transport equation (LBTE), which governs radiation transport, through discretisation in space, angle, and energy. The required modifications for application in brachytherapy are discussed, and several limitations of the solver were identified. In particular, the use of a fixed voxel-grid is shown to be unsuitable for brachytherapy, where dose calculations for brachytherapy span a large backscatter volume (at least 5 cm beyond the region of interest) while also requiring a fine grid to resolve to resolve steep dose gradients near the source and to model the sub-millimetre features of brachytherapy sources.

A series of increasingly complex test cases was developed to evaluate the solver. We started with homogenous water mediums, and introduced heterogeneities and high-Z shielding materials later. The results were compared against Monte Carlo (MC) reference data. Overall, good agreement between was observed between the deterministic and MC method. For both low- and high-energy photon sources, larger deviations were observed within the source (dose underestimation), very close to the source (dose overestimation), and near the boundaries of heterogeneities (dose overestimations). For low-energy photon sources, dose was underestimated through the entire medium, primarily due to the increased importance of photon attenuation.

These findings demonstrate that the developed deterministic solver shows good potential for brachytherapy applications. However, the use of a fixed voxel grid currently limits its suitability for clinical applicability. The development of an adaptive mesh capability is therefore required to enable dose calculations for clinical cases. ... Read more

External beam radiotherapy (EBRT) is a method for treating cancer in which the tumor is targeted by beams of radiation originating from the patient’s exterior. The two main particles employed for EBRT are photons and protons, with electrons and carbon ions also being in use. Both photons and protons are capable of achieving adequate tumor coverage, but protons can theoretically achieve lower doses in the surrounding tissues (at the expense of increased economical costs). Regardless of the chosen modality, the radiotherapy (RT) workflow is similar. It consists of determining the patient anatomy via imaging, usually via computed tomography (CT) scans, contouring (delineating) the organs at risk (OARs) and the target, creating a treatment plan, performing quality assurance (QA) and delivering the plan safely. In classical (also called non-adaptive) RT this workflow is performed once and the treatment is delivered over several (around 30) daily sessions (also called fractions).

Theoretically, the best radiotherapy treatment is the one in which the tumor is completely eradicated, while the surrounding tissue is not irradiated at all. Given that this is physically impossible, due to the nature of photon and proton propagation and interaction with matter, the next best result is maximal tumor coverage and minimal radiation damage to OARs. As the patient anatomy changes on different time scales ranging from weeks (e.g., weight loss, tumor shrinkage) to days (e.g., day to day variations of cavity fillings or neck pose changes) to seconds (due to for example breathing and slight movements) it becomes apparent that the offline approach to RT is suboptimal. To improve on this, the radiotherapy workflow must be adjusted such that imaging, delineation and treatment planning are performed several times over the course of the treatment, resulting in adaptive radiotherapy (ART). ART results in better targeting of the tumor and lower OAR doses. If adaptation is performed without the patient on the treatment table, the process is called offline adaptation. The next time-scale is online, which refers to a daily adaptation regime where the patient remains online (on the treatment table) after imaging. In such a workflow, on a given day the patient is imaged and within a short time (from tens of seconds to several minutes) the complete offline workflow (contouring, treatment planning, quality assurance, safe delivery) is performed. The time between imaging and delivery should be as short as possible, in order to minimize inter-fractional and patient set-up errors and to maximize clinical output. The ideal scenario would be real-time adaptation, in which all the steps of the radiotherapy workflow (including imaging and irradiation adaptations) are performed in real-time… ... Read more

Proton therapy has gained popularity for its ability to precisely deposit energy at a specific depth, allowing for higher tumor doses while minimizing radiation exposure to surrounding healthy tissue. This advantage is particularly relevant for head and neck squamous cell carcinoma (HNSCC), the seventh most common cancer globally, where radiotherapy is challenging due to the tumor’s location near critical organs. Despite advances in conventional photon-based radiotherapy, treatments still expose nearby critical organs, often resulting in severe side effects that significantly impact patients’ quality of life. However, robust clinical evidence supporting the superiority of proton therapy for HNSCC remains limited, partly due to the tumor’s heterogeneity and variations in treatment response driven by intrinsic biological factors and the tumor microenvironment. Furthermore, differences in energy deposition at the molecular level between protons and photons may influence DNA repair mechanisms, potentially affecting treatment efficacy in tumors with DNA repair deficiencies. Understanding these variations in biological effects is essential for improving HNSCC patient selection for proton and photon therapy and optimizing personalized radiotherapy strategies.... ... Read more

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.
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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. ... Read more

In this thesis, Boltzmann's H-theorem is studied and applied to prove a general convergence to equilibrium for the adiabatic piston paradox, governed by a specially-derived kinetic Fokker-Planck equation. We review general results in kinetic theory on the Boltzmann collision operator, and rates of convergence to equilibrium. Furthermore, we turn our attention to simulations of the piston paradox, and apply the algorithm of Sigureirsson et al. for particle-particle collision dynamics to the piston setting, determining empirically the optimal parameters. ... Read more

In this thesis, a predictive non-intrusive reduced order model of a high-temperature gas-cooled reactor has been created. We first set out to create a representative multi-physics model coupling the neutronics and thermodynamics in the HTGR. To this end we first made a representative model of neutronics and thermodynamics separately in the HTGR. These representative models were used to train and test our reduced order models (ROMs). It has been found that the representative model converges spatially and temporally to the expected values of analytical and pseudo analytical solutions for the neutronics, the temperature, and the coupled model. However, the convergence in the spatial domain was found to be slower than the mathematical expectation from the implemented finite volume method, converging at a rate of Δx1.5, instead of Δx2. The temporal convergence rate was Δt as is expected from the implemented first order backward differentiation formula. In literature review, we have found that Operator Inference provides a way to construct ROMs that are both predictive and non-intrusive. We used Operator Inference to create three ROMs. A ROM of the temperature model, a ROM of the neutronics model, and lastly a ROM of the coupled model. The temperature ROM created with 12 modi and 1500 snapshots was found to be accurate to the representative model for any sort of power input, achieving a root mean square error below 10−5 compared to the representative model, while predicting 3000 seconds longer than the training at Δt = 1 s. for 1000 random power inputs not included in training. The neutronics ROM created with 7 modi and 3960 snapshots retrieved accurate results, with a root mean square error around 10−4 compared to the representative model if we used a homogeneous temperature in the reactor. If we defined more temperature zones in the reactor we used a different interpolation technique. For this we found the results to be of lesser quality with an average root mean square error closer to 10−2 for a temperature profile far from a steady state temperature profile, while the results were of greater quality with an average root mean square error of 10−5 for temperature profiles close to a steady state temperature profile. Finally, we developed two methods of creating a reduced order model of the coupled model. First, we created a sequential model, where the outputs of the individual temperature ROM and neutronics ROM fed into each other. Second, we created a merged model, in which both the temperature and neutronics were determined by our reduced order model in one single model. A loss of forced cooling was simulated by changing the heat transfer coefficient at the coolant channel boundary. We trained the ROMs for 50 seconds with Δt = 10−3 s, with 5 different heat transfer coefficient equidistant between 0 and 0.03 Wcm−2K−1. It has been found that the merged model was robust and provided accurate results, it could predict to 200 seconds estimating for a heat transfer coefficient of 0.00375 Wcm−2K−1. with an average root mean square error in time for both the neutron flux profile and the temperature profile of less than 10−2. The sequential model was unstable, with root mean square errors orders of magnitude higher than the merged model. ... Read more

Introduction and purpose: Long-term xerostomia and dysphagia affect the quality of life (QoL) of head and neck (HN) cancer patients treated with radiotherapy (RT). However, xerostomia is subjectively measured with questionnaires, which has several limitations. It is of interest to investigate whether quantitative magnetic resonance imaging (qMRI) techniques can capture radiation-induced damage to organs in the HN area. This study uses diffusion-weighted imaging (DWI) to measure the apparent diffusion coefficient (ADC), mDIXON Quant to measure the fat fraction (FF), and T2-mapping to map T2 values. All three qMRI parameters are expected to increase with dose and, therefore, differ for different patient-reported xerostomia scores. In addition, recent literature emphasizes the relevance of the stem cell rich (SCR) region in the parotid glands, but the delineation methods are not patient-specific and incorporate a large margin to account for differences between patients.

Methods: 26 healthy controls (HC) and 54 HNC patients treated with RT have been included. MRI acquisition was performed 6 months to 3 years after the last RT fraction. 19 patients were scanned in a test-retest fashion to calculate the repeatability coefficient (RC). In addition, planning CT with dose distributions were available for all but two patients. Pearson’s correlation with a simple linear regression was performed to investigate the relationship between qMRI values and dose. A linear mixed-effects model (LMM) analysis was performed to assess the effect of subject characteristics on qMRI values. The fixed-effects estimates of this LMM were used to correct for subject characteristics in the correlation analysis. Cohorts were defined based on patient-reported scores for xerostomia (score 1 or score ≥ 2) and sticky saliva (score 1 or score ≥ 2). A one-way ANOVA with Tukey’s HSD post-hoc test was performed to test for differences in qMRI histogram parameters between HC and xerostomia cohorts, and between HC and sticky saliva cohorts. The hybrid and biomechanical deformable image registration (DIR) were performed for one patient to map the parotid glands from T2-weighted TSE to planning CT and similarity metrics were calculated. The best performing DIR algorithm was used in the delineation process of the SCR region, to delineate the SCR region on MR sialography imaging and map the delineations from MRI to planning CT. Volume and qMRI values were extracted.

Results: The difference in RC between HCs as reported in literature and patients ranged from -0.10 to 0.02 · 103 mm2/s for ADC, -0.4 to 1.6 % for FF, and -2.6 to 2.0 ms for T2, showing sufficient and comparable repeatability. Overall, ADC positively correlated with the dose for several relevant organs, while T2 only positively correlated with the dose of the submandibular glands and the superior pharyngeal constrictor muscle. FF did not show a positive correlation with dose. The LMM analysis showed that age affects the FF and T2 values of several organs related to xerostomia or dysphagia. BMI was also shown to affect the FF and T2 values of the submandibular glands. The fixed-effects estimates were not sufficient to correct for the effect of subject characteristics on qMRI values. Comparison of HC and patient cohorts showed differences in ADC, FF, and T2 for the parotid and submandibular glands, which can partly be explained by the effect of age. For the submandibular glands, the xerostomia cohorts also differed in ADC and T2 values, which can be due to the effects of dose, since the mean dose of the glands differed for the two patient cohorts. This effect was not observed for the sticky saliva cohorts. The hybrid DIR outperformed the biomechanical DIR and was therefore used in the delineation process of the SCR region. It was feasible to delineate the SCR region based on MR sialography imaging and map the delineation to qMRI maps and planning CT.

Conclusion: The findings of this study show the promise of ADC and T2 values in quantifying radiation-induced toxicity long-term after treatment, providing a basis for an improved understanding of long-term toxicities in head and neck cancer patients treated with RT. ... Read more

Over the next couple of decades, the world will face the challenge of drastically reducing carbon emissions. Innovative Generation-IV nuclear reactor designs can play an important role in driving this energy transition. One of these designs is the Molten Salt Fast Reactor (MSFR), characterized by a fast neutron spectrum and the use of a liquid fuel. Because of the liquid fuel, melting and solidification phenomena need to be considered. To this end, this thesis presents a combined experimental and numerical investigation of melting and solidification phenomena in the MSFR. The experimental part was primarily motivated by the lack of suitable experimental data for the transient development of an ice-layer in internal flow, which is a relevant case for the analysis of accident scenarios in a MSFR where solidification may pose a risk. The main focus of the numerical part was to improve the computational efficiency of current state-of-the-art melting and solidification models.

As part of the experimental investigation, a new experimental facility (ESPRESSO) was designed and built. The ESPRESSO facility consists of a water tunnel capable of reaching both laminar and turbulent flow rates, in which ice is grown from a cold plate at the bottom of a square channel. The ESPRESSO facility was designed to have well-described experimental boundary conditions, through careful consideration of the inflow and cold-plate specifications. Subsequently, experimental data was generated for the transient development of an ice layer in laminar internal flow using particle image velocimetry (PIV), which may be used for numerical validation. The onset of ice formation was found to coincide with a sudden increase of the cold-plate temperature, which was therefore used to identify the zero time instant in our experiments. This was attributed to subcooling effects prior to nucleation, of which evidence was obtained using laser induced fluorescence (LIF) temperature measurements.

In addition, non-intrusive temperature measurements have been performed for the transient development of an ice layer in laminar channel flow using LIF, which is so far only the second application of LIF as a non-intrusive temperature measurement technique in solid-liquid phase change experiments. The LIF method presented in this thesis is a novel approach for solid-liquid phase change experiments because of the use of a two color (instead of a one color) technique, the use of a post-processing algorithm to remove top to bottom striations and reduce other measurement noise, and a detailed analysis of the uncertainty in the temperature fields. Good results were obtained for sufficiently large temperature differences of approximately C with an uncertainty of σ=0.3-0.5 °C, however further improvements are needed to remove artefacts as a result of laser light scattering from the solid-liquid interface, and to obtain a sufficiently high accuracy for numerical validation purposes, especially for smaller temperature differences.

The numerical work performed as part of this thesis aims to address the need for more efficient melting and solidification models, which can accurately capture the solid-liquid interface and resolve the recirculation zones in the fluid region at a lower computational cost. To this end, an energy-conservative DG-FEM approach based on the `linearized enthalpy melting/solidification model' was developed and validated. Although certain solid-liquid phase change problems with strong gradients in the flowfield can benefit from the use of the higher order DG-FEM method, overall a suboptimal O(h) mesh convergence rate was obtained due to an inaccurate numerical solution of the discontinuities at the solid-liquid interface. Therefore, further development of the DG-FEM solid-liquid phase change solver is needed to fully benefit from the arbitrarily high order of accuracy of the hierarchical polynomial basis function set.

Very promising results were obtained with a parallel finite volume adaptive mesh refinement method for solid-liquid phase. Cells were refined based on the maximum difference in the liquid fraction over the cell faces and the estimated numerical discretization error in the flow and temperature fields, using the cell residual method. With this approach, a very good agreement was obtained between the adaptive mesh results and the reference solutions on a uniformly refined grid with significantly less degrees of freedom. This demonstrates the potential of the proposed finite volume adaptive mesh refinement approach as a more computationally efficient numerical method for solid-liquid phase change problems.

The final part of this thesis details a five-stage benchmark for modelling phase change in molten salt reactors, modelled after the MS(F)R freeze-valve design. With each stage, an additional layer of complexity is added, which enabled the identification of potential sources of discrepancy between different numerical modelling approaches. Results were obtained with three different codes: STAR-CCM+, OpenFOAM and DGFlows (inhouse DG-FEM based code for computational fluid dynamics). The results from the benchmark showed an overall good agreement between the three codes, although some discrepancies were observed when adding conjugate heat transfer effects. Therefore, we recommend some caution when coupling different solid-liquid phase change and conjugate heat transfer modelling approaches.

To summarize, this thesis presents new experimental data for the transient ice-growth in laminar internal flow, driven by a general lack hereof. In addition, this thesis illustrates the potential of LIF as a non-intrusive temperature measurement technique for solid-liquid phase change experiments. Two new numerical methods were developed and validated for solid-liquid phase change problems, and especially the finite volume adaptive mesh refinement approach showed promising results in terms of enhanced computational efficiency. On a final note: solid-liquid phase change is a vast and ongoing field of research. We believe this thesis is a substantial addition to the field, yet there are still a lot of opportunities for future work. Some suggestions are given in the concluding chapter.
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With FLASH proton radiotherapy, healthy tissue is spared more compared to conventional proton therapy. The FLASH effect is present at high fractional doses of more than 8 Gy, at ultra-high mean dose rates of more than 40 Gy per second and at dose delivery times of less than 200 milliseconds. However, particle accelerator intrinsic uncertainties can influence the FLASH effect negatively. FLASH proton therapy is affected by pencil beam positioning errors and by proton beam current fluctuations. In this work, these two types of uncertainties were added to a Gaussian two-dimensional analytical dose model. The impact of pencil beam placement errors was evaluated on dose deposition and related to clinically used metrics. These are the V95, V107, D2 and D98. The constraint on the V95 was violated for 5\% of patients first. For all evaluated FLASH dose fields, the allowed Gaussian pencil beam placement error is less than 1.0-mm in standard deviation. A trade-off between target coverage and the level of FLASH effect was found. Proton beam current fluctuations were coupled to the pencil beam scanning dose rate (PBSDR98), the minimum dose rate for 98\% of the target volume. For all evaluated FLASH fields the permitted beam current fluctuation was 6.8\%. This is considered more achievable than the limit on pencil beam placement errors. Proton FLASH radiotherapy is feasible, but proton beam characterisation is required to quantify for which patients this is the case.

The PBSDR is sensitive to added uncertainties and contradicts our current understanding of the FLASH effect. In this thesis, a more robust metric is developed which depends on the radiolytic oxygen depletion hypothesis for the FLASH effect. This metric is called the FER_eff. The oxygen concentration over time in a cell is modelled with an ordinary differential equation (ODE) involving an oxygen depletion and re-oxygenation term. The FER_eff can be calculated with the oxygen concentration over time for a treatment plan. Currently, The onset of the FLASH effect lies between a prescribed dose of 8 Gy and 16 Gy. This is in line with the FLASH dose threshold. The shortcomings of the PBSDR are dealt with.

A three-dimensional uncertainty analysis can be done with a semi-analytical dose engine from the TUD. With this engine, changes in dose deposition are coupled to Hounsfield unit (HU) perturbations without the necessity of a full dose re-computation. To study the machine uncertainties, an EMC FLASH treatment plan formed with iCycle was recreated with the TUD dose engine. The iCycle optimisation results contain all the required information for a dose calculation. The gamma pass rates between the iCycle dose and engine dose were satisfactory for the target but not good enough for an organ-at-risk (OAR) yet. Similarity can be improved by adjusting the dose recreation procedure. When the iCycle dose and engine dose are comparable, a trustworthy clinical uncertainty analysis can be done. The idea is to simulate field and individual pencil beam placement errors by shifting the original patient CT to a virtual CT. First, it should be studied if the computed dose response is valid for a combination of CT shifts and HU perturbations. In a later stage, pencil beam placement errors should be coupled to the clinically used ICRU metrics and constraints. The impact of beam current fluctuations can also be studied in the future. ... Read more

Introduction: Head and neck cancer (HNC) is a common and diverse group of tumors located in the region from the nasopharynx down to the upper part of the esophagus. Radiotherapy plays a crucial role in HNC treatment. In this thesis the particular focus is on external photon beam radiotherapy. The research is conducted in collaboration with the radiotherapy department of the Leiden University Medical Center (LUMC).
Theoretical background: The quality of a radiation treatment plan significantly affects patient outcomes in radiotherapy. Excessive radiation to organs at risk (OARs) can lead to complications, while insufficient dose to the tumor may increase the risk of recurrence. Automating the treatment planning process has gained attention in recent years, aiming to improve plan consistency, quality, and planning time. This study focuses on the optimization of treatment planning using RayStation’s deep learning autoplanning (DLAP) for patients with HNC.
Method: The study consists of three patient cohorts. The first and largest cohort includes 43 oropharynx patients. The second cohort includes eleven hypopharynx patients and eight larynx patients. The third cohort consists of nine unilateral oropharynx patients. Dosimetric analysis and normal tissue complication probability (NTCP) analysis are performed for both the clinical and DLAP plans in all cohorts. Dosimetric analysis uses parameters from dose volume histograms (DVH), while the NTCP analysis follows the Dutch National Indication Protocol for Proton Therapy for HNC.
Results: An initial sub-study determines the optimal tuning for the DLAP model, which is not only used in this study but also chosen for clinical implementation at the LUMC. In the following comparison study, all patient cohorts demonstrate higher Planning Target Volume (PTV) coverage in the DLAP compared to the clinical plans. The OAR dosimetric parameters show varying results, with DLAP generally demonstrating similar or better sparing of the OARs in oropharynx, hypopharynx, and larynx tumors. However, DLAP show a significantly higher dose in the brain stem core for larynx patients. Furthermore, an increased dose is observed in the mandible across all patient cohorts. Unilateral oropharynx patients treated with DLAP show a significant increase in dose to several OARs, particularly the contra-lateral salivary glands and swallowing muscles. The NTCP analysis does not reveal notable improvements or worsening across all patient cohorts.
Conclusion: DLAP demonstrates promising results for oropharynx patients, raising the question if further improvements in PTV coverage to achieve lower doses in the OARs are necessary. Although the patient cohorts for hypopharynx and larynx are small, the study’s findings indicate the potential for generating adequate treatment plans in these HNC regions. Furthermore, the results also highlight the need for further investigation in unilateral oropharynx cases, as DLAP did not sufficiently spare the contra-lateral side. The divergence in treatment technique suggests waiting for a specifically trained and designed DLAP for unilateral oropharynx patients. ... Read more