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.

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.

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.

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.

With multi-pinhole collimation systems, high resolutions can be reached with both SPECT and PET. Using tracers with higher-energy gamma-photons increases the effects of pinhole edge penetration, decreasing the resolution of the system. This research aims to design a collimator using 3x3 twisted pinhole clusters to be used in the VECTor system. This new cluster design should allow high-energy tracers such as 89-Zr to be imaged. The sensitivity of this new design was compared at 511 keV and 909 keV with the sensitivity of the VECTor collimator by simulating the sensitivity using GATE. A collimator design is characterised by its degree of multiplexing and detector coverage. These values are aimed to be the same as for the VECTor collimator to ensure a fair comparison at the simulation stage. Several design approaches have been tested. The final design had the clusters placed in 5 rows on the collimator. The three inner rows had 21 clusters, the two outer rows had 15 clusters. The inner radius of the collimator was increased to prevent pinholes from intersecting. The pinhole diameters of the final design were modified such that the resolution of the system was the same as the VECTor resolution for both 511 keV and 909 keV photons. To prevent the pinholes from intersecting, either the inner radius of the collimator had to be further increased, or the pinholes had to be smaller. It was chosen to test both options, resulting in a total of three collimator designs to be simulated. A scan lasting 1 hour was simulated with a source the size of the VECTor CFOV. This was done with 511~keV photons using 2 MBq/mL 18-F, and with 909 keV photons using 2 MBq/mL 89-Zr with their respective designs. These simulations resulted in a 1.77 times higher sensitivity to direct photons for the 511 keV collimator, and either a 2.04 or a 2.69 times higher sensitivity to direct photons for the 909 keV photons. Additionally, the total sensitivity of the 511 keV collimator did not significantly change, and the total sensitivity of the 909 keV collimator designs increased with either 8% or 40%. It is concluded that the implementation of 3x3 twisted pinhole clusters in a collimator to be used in the VECTor system has significant benefits over the current collimator when used with high-energy gamma-photons.

Computed Tomography (CT) imaging is an important step in treatment planning for proton therapy. The conversion from Hounsfield Unit (HU) to stopping power ratio (SPR) accounts for more than half of the 3.5% error that is considered in treatment planning. The aim of the study is to investigate the efficacy of photon-counting CT (PCCT) for predicting the SPR in proton therapy. Its performance will be compared to single-energy CT (SECT) and dual-energy CT (DECT). Fresh tissue samples—steak, ground beef, and bone marrow—were secured in 3D-printed holders to minimize air inclusion and maintain tissue stability during measurements. These samples were secured in a head and a body configuration of a phantom and CT scans were conducted using standard clinical protocols. SPR values were derived from the CT data using automated DirectSPR software for the DECT and PCCT scans, and converted from HUs to SPR using a Hounsfield look up table (HLUT) for the SECT scans. Results were validated against actual SPR measurements obtained via proton beam measurements. Two measurement series have been performed. For the validation of the first measurement series, proton beam energies of 150 and 175 MeV both resulted in an SPR value of 1.00 for the ground beef sample. For the steak sample, SPR values of 1.02 and 1.03 were found. The CT-based SPR predictions resulted in errors up to 1.5% for ground beef and 3.5% for steak. For the validation of the second measurement series, SPR values for ground beef (0.89-0.92), steak (0.96-0.98), and bone marrow (0.87-0.91) were obtained. The percentage error between the CT-based SPR predictions and the validation measurements showed no substantial differences between the CT-modalities, except for SECT providing lower SPRs for the bone marrow samples than DECT and PCCT. The validation measurements of the second series resulted in lower SPR values than found in literature and the first series, indicating possible methodological inconsistencies. Factors such as sample heterogeneities and measurement errors might have contributed to these inconsistencies, but do not account for these inconsistencies completely. These results highlight the need for further research with standardized phantoms and animal tissue samples to evaluate PCCT’s true potential in combination with DirectSPR prediction. From this study it cannot be concluded that PCCT demon- strates an advantage over SECT and DECT as CT-modality for the HU-SPR conversion in proton therapy treatment planning

Motivation: Effective management of liver tumors through thermal ablation requires precise monitoring of the ablation zone to ensure successful treatment outcomes. Computed tomography (CT) thermometry offers a promising non-invasive solution to monitor if tumor cells have been heated to the lethal temperature threshold. However, achieving reproducible, precise, and accurate temperature measurements remains a challenge, particularly due to metal artifacts introduced by the ablation equipment. Purpose: This study investigates the applicability of spectral CT thermometry in monitoring liver microwave ablation. It compares the reproducibility, precision and accuracy of CT thermometry on attenuation value images, with CT thermometry on physical density maps using spectral CT. Furthermore, it identifies the optimal metal artifact reduction (MAR) method — among O-MAR, deep learning-MAR, spectral CT, or a combination — to reduce needle artifacts and improve CT thermometry precision. Materials and Methods: Four liver-mimicking gel phantoms embedded with temperature sensors underwent a 10-minute, 60W microwave ablation imaged by dual-layer spectral CT using a Philips CT7500 scanner. Each scan was processed to reconstruct standard 120 kVp images alongside physical density maps, which were derived from virtual monochromatic imaging (70 - 150 keV) and effective atomic number maps. During each procedure, 23 CT scans were acquired to monitor attenuation and physical density values in proximity of the ablation antenna over time. Attenuation-based and physical density-based thermometry models were tested for reproducibility (coefficient of variation) over three repetitions; a fourth repetition focused on accuracy (Bland-Altman analysis). MAR techniques were applied to a single repetition to evaluate temperature precision in artifact-corrupted slices. Results: The correlation between CT value and temperature was highly linear with an R-squared value exceeding 96% for both attenuation and physical density-based thermometry. Model parameters for attenuation-based and physical density-based thermometry were -0.38 HU/ºC and 0.00039 ºC-1, with coefficients of variation of 0.023 and 0.067, respectively, indicating a high reproducibility. CT thermometry precision increased with distance from the ablation antenna, the use of attenuation maps and deep learning-MAR. Physical density maps generated at 150 keV alone and in combination with O-MAR and deep learning-MAR reduced needle artifacts by 73% on average (p=0.003) compared to attenuation images. Bland-Altman analysis reveals limits-of-agreement of -7.7°C to 5.3°C and -9.5°C to 8.1°C for attenuation and physical density-based thermometry, respectively. Conclusion: Spectral CT has the potential to make CT thermometry more universally applicable. This study demonstrates the effectiveness of spectral CT thermometry for non-invasive temperature monitoring during liver microwave ablation. It shows that using spectral physical density maps at 150 keV, alongside deep learning-MAR and O-MAR, enhances temperature accuracy and minimizes metal artifacts. However, standardizing thermometry parameters across different patient conditions remains a challenge. Future enhancements in photon counting CT and deep learning technologies could further refine this method, ultimately reducing the risk of local tumor recurrence.

Motivation: Effective management of liver tumors through thermal ablation requires precise monitoring of the ablation zone to ensure successful treatment outcomes. Computed tomography (CT) thermometry offers a promising non-invasive solution to monitor if tumor cells have been heated to the lethal temperature threshold. However, achieving reproducible, precise, and accurate temperature measurements remains a challenge, particularly due to metal artifacts introduced by the ablation equipment. Purpose: This study investigates the applicability of spectral CT thermometry in monitoring liver microwave ablation. It compares the reproducibility, precision and accuracy of CT thermometry on attenuation value images, with CT thermometry on physical density maps using spectral CT. Furthermore, it identifies the optimal metal artifact reduction (MAR) method — among O-MAR, deep learning-MAR, spectral CT, or a combination — to reduce needle artifacts and improve CT thermometry precision. Materials and Methods: Four liver-mimicking gel phantoms embedded with temperature sensors underwent a 10-minute, 60W microwave ablation imaged by dual-layer spectral CT using a Philips CT7500 scanner. Each scan was processed to reconstruct standard 120 kVp images alongside physical density maps, which were derived from virtual monochromatic imaging (70 - 150 keV) and effective atomic number maps. During each procedure, 23 CT scans were acquired to monitor attenuation and physical density values in proximity of the ablation antenna over time. Attenuation-based and physical density-based thermometry models were tested for reproducibility (coefficient of variation) over three repetitions; a fourth repetition focused on accuracy (Bland-Altman analysis). MAR techniques were applied to a single repetition to evaluate temperature precision in artifact-corrupted slices. Results: The correlation between CT value and temperature was highly linear with an R-squared value exceeding 96% for both attenuation and physical density-based thermometry. Model parameters for attenuation-based and physical density-based thermometry were -0.38 HU/ºC and 0.00039 ºC-1, with coefficients of variation of 0.023 and 0.067, respectively, indicating a high reproducibility. CT thermometry precision increased with distance from the ablation antenna, the use of attenuation maps and deep learning-MAR. Physical density maps generated at 150 keV alone and in combination with O-MAR and deep learning-MAR reduced needle artifacts by 73% on average (p=0.003) compared to attenuation images. Bland-Altman analysis reveals limits-of-agreement of -7.7°C to 5.3°C and -9.5°C to 8.1°C for attenuation and physical density-based thermometry, respectively. Conclusion: Spectral CT has the potential to make CT thermometry more universally applicable. This study demonstrates the effectiveness of spectral CT thermometry for non-invasive temperature monitoring during liver microwave ablation. It shows that using spectral physical density maps at 150 keV, alongside deep learning-MAR and O-MAR, enhances temperature accuracy and minimizes metal artifacts. However, standardizing thermometry parameters across different patient conditions remains a challenge. Future enhancements in photon counting CT and deep learning technologies could further refine this method, ultimately reducing the risk of local tumor recurrence.

Motivation: Effective management of liver tumors through thermal ablation requires precise monitoring of the ablation zone to ensure successful treatment outcomes. Computed tomography (CT) thermometry offers a promising non-invasive solution to monitor if tumor cells have been heated to the lethal temperature threshold. However, achieving reproducible, precise, and accurate temperature measurements remains a challenge, particularly due to metal artifacts introduced by the ablation equipment. Purpose: This study investigates the applicability of spectral CT thermometry in monitoring liver microwave ablation. It compares the reproducibility, precision and accuracy of CT thermometry on attenuation value images, with CT thermometry on physical density maps using spectral CT. Furthermore, it identifies the optimal metal artifact reduction (MAR) method — among O-MAR, deep learning-MAR, spectral CT, or a combination — to reduce needle artifacts and improve CT thermometry precision. Materials and Methods: Four liver-mimicking gel phantoms embedded with temperature sensors underwent a 10-minute, 60W microwave ablation imaged by dual-layer spectral CT using a Philips CT7500 scanner. Each scan was processed to reconstruct standard 120 kVp images alongside physical density maps, which were derived from virtual monochromatic imaging (70 - 150 keV) and effective atomic number maps. During each procedure, 23 CT scans were acquired to monitor attenuation and physical density values in proximity of the ablation antenna over time. Attenuation-based and physical density-based thermometry models were tested for reproducibility (coefficient of variation) over three repetitions; a fourth repetition focused on accuracy (Bland-Altman analysis). MAR techniques were applied to a single repetition to evaluate temperature precision in artifact-corrupted slices. Results: The correlation between CT value and temperature was highly linear with an R-squared value exceeding 96% for both attenuation and physical density-based thermometry. Model parameters for attenuation-based and physical density-based thermometry were -0.38 HU/ºC and 0.00039 ºC-1, with coefficients of variation of 0.023 and 0.067, respectively, indicating a high reproducibility. CT thermometry precision increased with distance from the ablation antenna, the use of attenuation maps and deep learning-MAR. Physical density maps generated at 150 keV alone and in combination with O-MAR and deep learning-MAR reduced needle artifacts by 73% on average (p=0.003) compared to attenuation images. Bland-Altman analysis reveals limits-of-agreement of -7.7°C to 5.3°C and -9.5°C to 8.1°C for attenuation and physical density-based thermometry, respectively. Conclusion: Spectral CT has the potential to make CT thermometry more universally applicable. This study demonstrates the effectiveness of spectral CT thermometry for non-invasive temperature monitoring during liver microwave ablation. It shows that using spectral physical density maps at 150 keV, alongside deep learning-MAR and O-MAR, enhances temperature accuracy and minimizes metal artifacts. However, standardizing thermometry parameters across different patient conditions remains a challenge. Future enhancements in photon counting CT and deep learning technologies could further refine this method, ultimately reducing the risk of local tumor recurrence.

Photon-counting detectors (PCD) for medical X-ray computed tomography (CT) are designed to measure the number of X-ray photons incident on a detector pixel as well as the energy of the individual X-rays. They are expected to yield improvements in image quality for a given radiation dose, and to offer opportunities for spectral imaging beyond dual-energy techniques. However, the fluence rate incident on the detector can exceed 108 mm-2 s-1 in CT, so that the detector pulses generated by the X-rays likely pile up on each other, which distorts the measurement. The semiconductors CdTe and Cd1-xZnxTe (CZT, x ≈ 0.1-0.2) are commonly considered efficient X-ray absorbers that provide sufficient rate capability (fast pulses in the order of 101 ns and a high pixel density ≥ 4 mm-2) and energy resolution (8-20% FWHM at 60 keV). In such detectors, an X-ray is converted into electron-hole pairs, which travel to (pixelated) electrodes, on which they induce a current pulse. However, the cost-effective synthesis of material of sufficient quality to make this a stable and reliable detection process appears to remain an issue. Thus, the aim of this thesis is to explore the photon-counting performance, e.g., the rate capability and energy resolution, of an alternative detector concept based on a scintillator, which converts an X-ray into a light pulse, and a silicon photomultiplier (SiPM), which detects the light. Since such a detector relies on light rather than charge transport, it may enable cost-effective manufacturing of stable and reliable PCDs... @en

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).

Background Dynamic SPECT scanning provides a non-invasive way to image the time-dependent distribution of radio-labelled tracers inside living tissue. Beside human medicine, dynamic SPECT also finds its applications in pre-clinical research on small animals. In pre-clinical research, multi-pinhole collimators are used to enable high-resolution sub-millimeter imaging. Conventional iterative reconstruction methods, such as Maximum Likelihood Expectation Maximisation (MLEM) perform poorly in reconstructing the noisy and low-count scans in dynamic SPECT. This limits the temporal resolution that can be achieved. Method The reconstruction of noisy, low-count time-frames can be aided by incorporating information from earlier and later time-frames. Wang and Qi (2015) published the paper 'PET Image Reconstruction Using Kernel Method', with a proposed method entitled Kernelised Expectation Maximisation (KEM) for dynamic PET, a method that uses principles from Machine Learning, such as Support Vector Machines and the 'kernel trick' to incorporate prior information in the reconstruction algorithm. This method is highly adjustable due to a number of input parameters of the method. In this paper, KEM is implemented for dynamic multi-pinhole SPECT. The effects of the KEM parameters are explored in computer simulations. Two different dynamic phantoms are used, one of the striata in a mouse brain which were adapted from a paper by Vastenhouw et al. (2007) and one of the hepatobiliary system adapted from Vaissier et al. (2012). The results of KEM are benchmarked against conventional MLEM with a Gaussian post-filter.  Results In high-count simulations, the MLEM reconstructions had a lower mean-squared-error than the KEM image, while the signal-to-noise ratio of KEM was better than MLEM. The images produced after 200 iterations were indistinguishable, however. In the low-count regime, KEM was shown to be more resistant to noise than MLEM. Varying the input parameters of KEM gave rise to differences in performance, such as (over-)smoothing effects and a different level of noise-suppresion in the reconstructed image.  Conclusions In the simulations used in this paper, KEM was shown to outperform conventional MLEM with a Gaussian post-filter from low-count projections. The optimal input parameters of the KEM algorithm, however, need to be found ad hoc by searching the parameter space. Further research should look into finding a set of rules or guidelines for finding the optimal parameters.

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).

The need of image (frame) acquisitions within short time intervals is of major importance for preclinical SPECT imaging. The short frame times enable higher temporal resolution which is required in bio-distribution and pharmacokinetic studies where fast dynamic imaging is performed. The present study evaluates and compares the performance of two different preclinical multipinhole SPECT systems (NanoScan, VECTor) for short frames acquisitions. Prior to the systems comparison, the comparison and selection of the best performing fast imaging mode provided by NanoScan system (Mediso) was performed. The fast imaging modes of this system provide acquisitions with 1,2 and 3 detector position around the animal bed. This comparison was performed by using uniform phantoms (syringes) and the rods of the NEMA NU4IQ phantom (frames: 6-30s). The down-sized version of NU4IQ phantom (SPECTIQ phantom) was used in this study to compare the performance of VECTor (MILabs) and NanoScan when performing acquisitions with short frame times (18s-600s, whole body scans). The quality of the acquired images was assessed in terms of absolute quantification (recovery coefficient), noise levels and visual evaluation. The quantification with the NanoScan was accurate (±5%) regardless of times frames duration and activity concentrations when imaging large structures. The increase in number of detector positions yielded images with lower noise levels. In the case of small structures, acquisition with 3 detector positions (Semi-3 mode) appeared to provide more accurate activity recovery compared to acquisitions with 1 (stationary mode) and 2 detector positions (Semi-2 mode). Especially in the case of the 2mm diameter rod of the NU4IQ phantom, the Semi-3 mode appears to provide significantly more accurate activity recovery (30s frame). The systems comparison showed activity recovery with up to 5% deviation from the dose calibrator measurement when imaging the uniform region of SPECTIQ phantom (d = 21mm). Both systems could recover the three largest rods (d = 1.5, 1.0, 0.75mm) for the longest frames used(180,360,600s). None of the systems could recover the two smallest rods of the phantom (d=0.5,0.35mm). As the frame time decreased, both systems could recover less number of rods. VECTor appeared to provide higher activity recovery than NanoScan for the three largest rods of the phantom. However, as the frame time decreased the differences became less significant. Furthermore, VECTor provided and 22.2% and 46.6% less spillover in airand water-filled phantom regions (after reaching convergence) than NanoScan did. The performances of two preclinical SPECT systems (NanoScan, VECTor) for short time acquisitionswere compared. The conducted experiments showed that the systems perform equally when conducting short frames imaging. Furthermore, the fast imaging mode of NanoScan employing three detector positions showed better performance than the other two fast imaging modes provided by this system.

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.