The maximum-independent-set problem is a fundamental graph problem with applications in situations where one wants to select as many mutually compatible objects as possible, such as non-conflicting tasks or choices. This problem is computationally difficult to solve exactly on large graphs. This motivates the use of heuristic algorithms. However, heuristic performance can depend strongly on the structure of the input graph, so it is not enough to ask which algorithm performs best on average. It is also important to understand when and why a heuristic fails.

This Bachelor End Project studies heuristic algorithms for the maximum-independent-set problem from a structural point of view. We compare three methods: a minimum-degree Greedy algorithm, Lotka–Volterra dynamics, and simulated annealing, using an exact solver as a benchmark on small graphs. The goal is not only to compare solution sizes, but also to identify the graph structures and parameter choices that cause each method to perform poorly.

The results show that the algorithms fail for different structural reasons. Greedy can perform poorly when the minimum-degree rule is locally attractive but globally misleading. In the tested bad examples, Greedy selects vertices that look good by degree, but this choice blocks access to a much larger independent set. Lotka–Volterra dynamics can converge to maximal independent sets that are not maximum. This behavior is influenced by the initial condition and becomes more important when the graph contains many competing maximal independent sets. Simulated annealing is affected by the structure of the energy landscape. In particular, the experiments suggest that failure is not caused only by a single large energy barrier, but also by the presence of many competing trap states.

The parameter experiments support these interpretations. Increasing the Lotka–Volterra competition parameter τ strengthens early suppression between neighboring vertices but often leads to smaller independent sets. For simulated annealing, increasing the penalty parameter α makes edge conflicts more expensive and helps the algorithm reach feasible states earlier, but it can also reduce exploration through temporary conflict states. Longer cooling schedules improve performance, especially on larger or more crowded instances, but require more computation.

Overall, the thesis shows that the performance of maximum-independent-set heuristics cannot be explained by graph size alone. The relevant difficulty depends on graph structure, the number of competing maximal independent sets, the initial condition, and the algorithm parameters. This gives a more diagnostic view of heuristic performance: instead of treating Greedy, Lotka–Volterra, and simulated annealing as black-box methods, the thesis identifies structural warning signs that indicate when each method is likely to struggle. ... 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

This thesis investigates the optical properties of monodisperse spherical silica-based coatings through experimental measurements and Monte Carlo ray tracing simulations. The aim is to compare these experiments and simulations to identify potential improvements. The study focuses on quantifying diffusivity and transmittance, related to Hortiscatter (HS) and Hemispherical Light Transmission (HLT), respectively, which are essential for optimizing greenhouse light conditions to enhance plant growth.

For HLT, simulations and measurements aligned more closely for low-scattering coatings, especially with smaller particles (160 nm and 400 nm), often showing less than 1% difference. However, for high-scattering coatings with larger particles, the difference was much larger, reaching up to 9%. This discrepancy highlights the possible importance of surface scattering and potential measurement setup limitations, such as the beam size being too small.

Significant discrepancies were observed between measured and simulated HS values, with measured values consistently higher by 5% to 80%, likely due to simulations not accounting for surface roughness. Smaller particles showed similar wavelength dependence of HS between simulation and measurement, while larger particles (5 μm) indicated the presence of complex scattering interactions that simulations did not account for.

Future work should refine simulation models to include surface roughness and obtain precise input parameters, such as refractive indices, to improve accuracy. Enhancing experimental setups, for example by reducing the signal-to-noise ratio, will also ensure more reliable measurements. This research offers valuable insights into optimizing greenhouse coatings, demonstrating the potential of theoretical models to predict practical outcomes under specific conditions. Continued advancements in simulation and experimental methodologies are crucial for effective greenhouse design to enhance plant growth. ... 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

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

Magnetic resonance fingerprinting is an MRI-based technique that allows for fast, simultaneous quantitative mapping of multiple tissue parameters. Multi-component MRF (MC-MRF) additionally allows for the mapping of multiple tissue components per voxel. Currently, most MC-MRF implementations ignore any nonlinear effects on signals resulting from multi-component systems, such as the effects introduced by magnetisation transfer (MT). Here, we investigate the effects of free pool to free pool magnetisation transfer on the accuracy of MC-MRF, with a focus on the application of this technique for myelin water fraction (MWF) imaging. Assessment of the different MC-MRF techniques is done through application of these algorithms to two-component numerical phantoms, where the signals from two interacting components were simulated using the EPG-X framework. Results show that MT has a negative effect on the accuracy of the acquired parameter estimates for all estimated parameters, resulting in biases and incorrect parameter estimates. Several adjusted methods for MC-MRF including magnetisation transfer were proposed and tested. Although theoretically improvements were expected, the used sequences showed to be inadequate for accurate MT estimates. More research into these techniques is still required to improve their performance and accuracy. A technique left mainly unexplored here is sequence optimisation to minimise the effects of magnetisation transfer on the resulting signals. A quick exploration, however, showed that this might be a viable approach for future research as well. ... Read more

In this report, a transformer based deep learning proton dose calculation algorithm called Dose Transformer Algorithm (DoTA) is described. This model learns to predict proton dose distributions by being trained with Monte Carlo generated data. Monte Carlo is the golden standard of proton dose calculation because it is very accurate, but it has relatively long computation times. In current proton therapy treatment programmes, Monte Carlo algorithms are the most commonly used models to perform dose calculation. The goal of the DoTA model is to predict proton dose distributions with Monte Carlo accuracy in the fraction of the computation time of Monte Carlo algorithms to speed up the dose calculation process in proton therapy treatment. The DoTA model can take patient geometry, random proton beam energy and random proton beam shape (2D Gaussian with different major and minor axes) as input. The addition of the random proton beam shape input is discussed in this report, together with a detailed explanation of the DoTA model. The DoTA model is trained with data from 9 lung cancer patients and 9 head & neck cancer patients. Being used on an Intel(R) Core(TM) i7-8565U CPU, the DoTA model managed to produce results with a gamma pass rate of 98.45 ± 2.60 % with an average computation time of 0.3 seconds. The gamma pass rate determines how similar the DoTA predicted dose is to the reference (Monte Carlo generated) dose. Compared to the average computation time of the Monte Carlo algorithm that was used to generate the training data, which is 20 seconds on the same CPU, we can conclude that the DoTA model has the potential to greatly improve dose calculation times in proton therapy treatment, especially when used on a system with greater processing power. Because the DoTA model is able to deliver accurate results in a small amount of time, it has the potential to be used for real-time dose calculation. Real-time dose calculation could account for small changes in patient geometry during treatment, which increases the accuracy of the treatment and minimizes side effects. The DoTA model can also be used for other radiotherapy types like phonon therapy and electron therapy (in that case it needs to be trained with
different data). ... Read more

Radiometals re-emerge as a promising alternative in nuclear imaging. Cyclotron production of such radiometals using liquid targets solve critical impurity problems seen with current conventional production methods. Purification employing liquid-liquid extraction in microfluidic environments has recently been investigated. Fluid behaviour in microfluidic environments are deeply laminar, and one of the dominant factors in mass transfer in such environments is diffusion. For optimal design, numerical models are being developed to theoretically describe mass transfer in a microfluidic environment. To achieve this, diffusion coefficients of radiometals in their respective liquid target solutions need to be known. Exploiting the laminar flow properties in microfluidics, microfluidic devices have been employed in determining diffusion coefficients. This study presents a simplified 2D theoretical description of mass transfer in single phase flows in microfluidic channels. It has been attempted to verify the proposed model by determining the diffusion coefficient with methylene blue in an aqueous solution for direct comparison with literature. The method was tested for microfluidic devices of varying geometries, different flow rates and alternative setups, and yielded overestimations of a factor of two regarding literature. Consistently finding this overestimation, along with the discovery of several small mistakes in work presented in literature, does not render the method inaccurate. Further research in what range microfluidic devices remain an appropriate tool for determining diffusion coefficients is required. Diffusion coefficients for 68Ga in target solutions of varying concentrations of zinc nitrate, dissolved in aqueous solutions of varying concentrations of nitric acid were investigated. The influence on concentration of nitric acid was minimal, while the diffusion coefficient was found to be inversely proportional to the viscosity of the target solution, in correspondence with the general behaviour of several empirical correlation relations for finding the diffusion coefficient. ... Read more

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. ... Read more

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.
... Read more

Purpose: Pre- and post-treatment MRI based Diffusion Weighted Imaging (DWI) has shown to be an effective predictor and indicator of treatment response in cancer. However, clinical applications of the Apparent Diffusion Coefficient (ADC) requires sufficient precision; this can be assessed with repeatability studies. Therefore, the purpose of this study was to assess the repeatability of ADC measurements on an MR-Linac for patients with locally advanced rectal cancer (LARC). Additionally, the relative change in ADC during treatment was compared against the repeatability.
Methods: For 17 patients, DWI was performed once on 3T MRI during pre-treatment, and twice on an 1.5T MR-Linac during each treatment fraction. Manual delineations of the Gross Tumour Volume (GTV) were created by the author. In addition two semi-automatic delineation methods were implemented, which used; a registration pipeline to propagate T2 delineations; a geometric distortion correction algorithm to correct for DWI susceptibility artefacts. Based on visual inspection the most accurate and consistent delineations were used to calculate the ADC; Bland-Altman repeatability coefficient (RC), within subject coefficient of variation (wCV), and the intraclass correlation coefficient (ICC). Lastly, the relative change in mean and median tumour ADC was compared against the RC.
Results: Manual delineations were determined to have the highest agreement with ADC tumour location and were used in subsequent calculations. The mean and median tumour ADC wCV was 7.6% (CI95:5.5-9.2%) and 8.2% (CI95:5.9-9.9%) respectively, which corresponds with a RC of 21.0% (CI95:15.1-25.6%) and 22.7% (CI95:16.3-27.4%). The reliability of the measured data was good, with an ICC of 0.81 (CI95:0.72-0.87). In 6 out of 17 patients the relative change in ADC exceeded the RC at some point during treatment, which suggest a potential for future clinical applications.
Conclusion: Despite the low precision of rectal cancer ADC measurements, daily DWI imaging on MR-Linac has been shown to be viable for clinical applications in a subset of patients whom show significant ADC changes during treatment. Further studies are required to determine whether the measured ADC repeatability allows for clinically relevant observations. ... Read more

High dose-rate (HDR) brachytheraphy (BT) is radiation therapy (RT) which temporarily inserts a highly radioactive source into the planning target volume (PTV) with the use of an afterloader, applicator, transfer tubes, needles and planning software. The dose delivered to a point in the tumour depends on the type and activity of the source, the distance to the source and the time the source dwells at a predetermined dwell position. Current therapies have proven to be capable of tumour control. However, in contrast with other RTs there is no suitable integrated dose verification method available for HDR BT which does not disturb the clinical workflow. The focus of this research is on the dose verification for the most commonly used form of HDR BT. The one with the Ir-192 source. An optical in vivo dosimetry (OIVD) sensor assembly consists of a capsuled scintillator coupled into an optical fibre, which connects to a photodiode capable of processing the light signal generated in the scintillator. These systems are currently only available in experimental set-ups and not jet integrated with the afterloader, making them clinically unusable. Furthermore, the system needs to be coupled with an optical fibre of 100-300μm, give a linear output signal to remodel the delivered dose, be non-hygroscopic, be suitable at treatment temperatures, and have low production costs. By adding a OIVD sensor to the current clinical BT workflow, better treatment can be achieved. This sensor assembly needs to be made with a suitable scintillator and shape to have a high coupling efficiency in the OIVD sensor system to give reliable dose verification. Both a literature study and simulations are executed as part of my master thesis to provide a context for the project and to find the best-suited shape and scintillator, This report provides an overview of the current status of knowledge of OIVD from the available experiments and clinical studies derived from the published data, so knowledge for choosing the scintillator material, fibre diameter and the coupling geometry can be obtained. Special attention is given to identifying why scintillators excite light of a specific wavelength and how to achieve an efficient light coupling. The inorganic scintillator ZnSe : O has favourable scintillation characteristics and is has proven itself to be helpful in a OIVD sensor for Ir-192 BT with its high intensity, good energy resolution, slight afterglow and low production cost. The experimental part of this research is the verification of the OIVD sensor assembly with simulations executed within the COMSOL Multiphysics®ray-tracing package, which compares multiple similar OIVD sensor assembly heads. The OIVD model is inspired by the experimental set-up from E.Andersen and simulates for multiple configurations on a range of parameters (5). The simulations of the OIVD sensor assembly can conclude the following: 1. The most favourable scintillator is ZnSe :O, this scintillator has the highest light yield and low absorption. 2. With a fibre diameter of 200μ m, sufficient light yield reaches the detector to verify the dose delivery. 3. Couple the fibres off the centre from the central axis of the scintillator will negatively influence the coupling efficiency. The outcome of this research contributes to the design of a OIVD sensor assembly for HDR BT for with Ir-192 . These simulations lead to an optimal design that will fit the current afterloader system. Next to this, the OIVD sensor assembly head can be made with simple manufacturing steps so that it can be produced at a minimal cost. The combination of the COMSOL model with the scintillation properties from the literature shows that a sensor assembly of a ball coupled ZnsSe :O scintillation is has a higher light throughput than the prototype from E.Andersen (5), this shows that it is favourable to verify the dose delivery of Ir-192 HDR BT (5). ... Read more

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. ... Read more

One of the main elements for creating an optimal operating room schedule is an accurate surgery duration estimation. Currently, this estimation is only done preoperatively. However, multiple factors during the surgery itself could influence the duration, for example bleeding. Literature showed the possibility of intraoperatively estimating a surgery duration based on surgical progress. However, one of the main concerns was the usability of such a system in the operating room workflow. Therefore, this research was focused on two parts: (1) the creation of an automatic intraoperative remaining surgery duration estimation system and (2) the evaluation of such a system for the operating room workflow. Two types of surgeries were used for the estimation, the Laparoscopic Cholecystectomy and the Total Laparoscopic Hysterectomy. The estimation was created using multiple statistical regressor methods, such as linear regression and random forest, and progress-based methods based on the nearest-neighbors algorithm and Dynamic Time Warping method. The evaluation was done on two levels: the system level based on the error of the estimation, and the operating room workflow based on surgical data from 2016 to 2019 and interviews with the operating room program coordinators. Results showed that an intraoperative remaining surgery duration estimation system based on surgical phases was able to re-estimate the duration with an error of about 10 minutes, an acceptable error for the operating room workflow. Moreover, the third quarter of the surgery showed to be the essential part where an accurate estimation is needed. Furthermore, an automatic system showed additional benefits such as being unbiased, continuous, and reducing unnecessary disturbance in the operating rooms. Overall, this research showed that an intraoperative remaining surgery duration system based on surgical phases is promising for the operating room workflow. Future research is needed to understand how to implement such a system in the operating room workflow. ... Read more

Pencil beam scanning is becoming a more common treatment modality. However, its ability to deal with moving targets is known to be limited, as beam motion and target motion can reinforce each other, deteriorating the planned dose distribution in what is called the interplay effect. Literature concerning breathing motion usually investigates regular patterns. This work aims to investigate the magnitude of the interplay effect when considering irregular breathing signals. In silico calculations of dose distributions were made in the treatment planning system RayStation (version 7.99), using an XCAT phantom with 50 CT phases to model moving patient anatomy. An interplay calculator was included in RayStation, allowing calculation of disturbed doses based on a treatment plan and an irradiation time model for a proton therapy accelerator. The target investigated was a spherical liver tumour with 5cm diameter, irradiated with two beams delivering a prescription dose of 63 Gy. Plans without and with 5x layered repainting were created. Clinically realistic regular breathing patterns were generated to establish a baseline, after which irregularities were introduced. The basic form for all patterns was a sin^4 signal, with regular signal amplitudes ranging from 6 to 18 mm, period ranging from 3 to 4 s and phase between 0 and 2π rad. Considered irregularities were baseline shifts up to 34 mm, changing amplitudes between 6 and 18 mm, changing periods between 1.6 and 5.2 s and combinations. Evaluation was done by looking at dose homogeneity HI_5 and the fraction of the CTV volume that received a dose outside of the clinical limits of 95% and 107%, V_107/95. For the regular patterns, both a systematic and a randomised analysis were carried out. For irregular patterns, only a systematic analysis was carried out. The mean HI_5 was found to be 31% for regular patterns; the means of all irregular patterns stay below this, even though the size of the irregularities for some breathing patterns was very large. The mean V_107/95 was found to be 0.7 for regular patterns. Irregularities did not cause further deterioration. Five times layered repainting causes a statistically significant decrease in magnitude of the interplay effect across all breathing patterns by 50-80%, but is approximately 50% less effective against baseline shift than against other types of breathing. Interplay effect size correlates strongly with amplitude, but this correlation can be obscured because period and phase introduce very large variance. The interplay effect in general is large for investigated target size, prescription dose, beam configuration and machine performance. It can cause up to 100% of the CTV to receive a clinically unacceptable dose and lead to large inhomogeneities. Irregular breathing was not found to be notably worse. Repainting is effective, even against irregular breathing, but baseline shifts can undermine its effectiveness. Separately considering breathing irregularities for tumours similar to that investigated here is deemed of low importance; it is more important to properly model the magnitude of the interplay effect using an accurate, individualised breathing pattern. ... Read more