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

Background and purpose: One of the main challenges in external beam radiotherapy treatment of locally advanced cervical cancer patients is dealing with bladder and rectum filling. Organ filling causes inter­fraction motion of the uterus, requiring large treatment planning volumes, or a plan library. Current assessment of tumor position is mainly done by visual inspection of a Cone Beam Computed Tomography (CBCT) scan. Eventually, this can lead to inter-­ and intra-­observer variability when choosing the best treatment plan from the plan library based on bladder filling. The incoming introduction of auto­contouring tools to obtain automatically-­generated (AG) contours of the bladder and the rectum on CBCT scans, allows the easier identification of these organs at risk and consequently, faster localization of the tumor region. However, to rely on these AG contours in the decision of plan selection, it is necessary to know if they have been reliably segmented. The goal of this project is to develop a strategy based on quantitative image features, to evaluate the quality of the AG contours to know if they are suitable for plan selection assessment.
Materials & Methods: 140 LACC patients from Erasmus MC were included. For each patient, bladder and rectum contours were obtained from each of the CBCT scans done throughout the treatment (five fractions (CBCT scans) per patient). These contours were automatically­generated using a deep learning­-based auto­segmentation algorithm. Gold­-standard contours were manually delineated in some CBCT scans, but the rest of the automatically­generated contours did not have the corresponding ground-­truth contour, hence they were labeled with a score between 1 (bad quality) and 5 (good quality). For consistency, gold­-standard contours were included in the dataset with the class label 5. The contours were relabeled to have a binary classification problem, and those with label 3 were removed. Each contour volume was divided into three subregions: core region, inner and outer shell. This contour data was used for a comparison study between two supervised machine learning (ML) methodologies:
Random forest (RF) networks and Logistic Regression (LR). For both strategies, feature extraction and selection were implemented. In RF methodology, a prior step of dimensionality reduction using principal component analysis (PCA) was performed. In LR, univariate feature selection followed by a multivariate logistic regression analysis was done. Before implementing the classifiers, the dataset
was split into a training set and a test set. The ML models were trained using the training set, and they were tested on new unseen data. Predictions on the test data were obtained and used for evaluation of the model's performance using evaluation metrics: accuracy, sensitivity, specificity, confusion matrix, ROC curve, and AUC.
Results: The RF classifier performed on the bladder test data with an AUC value of 0.87, while for the LR model, the value obtained was 0.77. The trained RF model identified the accurate and inaccurate bladder contours with a sensitivity of 94% and a specificity of 54%. The trained LR model resulted in a sensitivity of 91% and a specificity of 42%. In the case of the rectum, the RF classifier performance is indicated with the AUC value of 0.89, while the LR model obtained a value of 0.84. In the case of sensitivity and specificity, the RF model got 96% and 38%, and the LR classifier 95% and 38%, respectively.
Conclusion: Random forest classifiers give the best results in terms of performance and classification skills for the OARs considered, especially for the bladder. It has been demonstrated that quantitative image features, paired with the corresponding contour class label, can be used for deriving statistical relationships from the data. This allows the identification of contouring errors and classifying the contours based on their quality. With the increasing automation of different steps in the radiotherapy treatment workflows, the automatic contour QA tool developed would be a key step in the process to ensure a faster, more feasible, and consistent plan selection. The tool could act as a support tool for radiotherapy technicians when choosing the plan from the plan library that best fits the daily anatomy of the patient.

In the field of radiation oncology, proton therapy is a relatively new technique. It shows a great advantage over conventional radiation therapy in the depth-dose relation, which results in the possibility to deliver dose far more concentrated at a specific depth. This in turn has the potential to spare the healthy tissue surrounding a tumour from receiving a very high dose. However, this depth-dose relation has the downside that it is very sensitive to small uncertainties in geometry and tissue composition, which are present in heterogeneous tissues such as lung tissue.
Because of this sensitivity, it is essential that the dose delivery can be verified properly for these heterogeneous tissues, which requires highly accurate quality assurance. To improve this quality assurance, highly anthropomorphic phantoms could offer a solution. The currently commercially available phantoms however lack the high level of detail necessary, as these are produced using casting techniques. Thus a new production technique should be considered to create phantoms of greater heterogeneity and at a higher level of detail. A possible solution to this problem is to apply additive manufacturing, since this manufacturing technique can supposedly address both these issues.
An important issue with the application of additive manufacturing is the lack of knowledge on the accuracy of 3D-printers. Next to the unknown accuracy, there are other challenges concerning additive manufacturing, such as the layered creation of objects and the material that is to be printed over an air cavity and the support structures this is associated with.
The goal of this research is to explore the possibility to apply additive manufacturing in the creation of a phantom with a high level of detail and heterogeneity and the effect of the heterogeneous object on the quality of the Bragg peak. More specifically, simulations are performed on porous materials to quantify the degeneration of the Bragg peak. Also, a literature study on additive manufacturing will be performed, combined with the use of commercially available tabletop 3D-printers. Together, the capacity of the printer to create a high level of heterogeneity and the simulations performed on these heterogeneous structures, should give an indication on the feasibility to create a 3D-printed phantom for lung proton therapy.

At the Antoni van Leeuwenhoek Hospital/Dutch Cancer Institute (NKI-AvL) in Amsterdam, inter-fractional anatomical changes during the course of radiotherapy are monitored using cone beam CT scans, taken prior to irradiation. These scans are assessed visually, and the fractions are scored according to a 'traffic light protocol'. Based on the magnitude of change, a green, yellow, orange or red colour, in increasing order of severity, is assigned to the fraction. The goal of this work was to ascertain if the colour of the traffic lights, which were assumed to be indicators of anatomical change, correlate to changes in dosimetry for H\&N VMAT treatments, as well as lung IMRT treatments. The in-vivo EPID dose was reconstructed in the patient for each fraction, using a back-projection algorithm that is used clinically at the NKI. Calibrated CBCTs of each fraction were obtained using DIR or anti-scatter grid methods researched at the NKI, which were then imported to a TPS to obtain the fraction dose. These two modes of dosimetry were compared against each other, as well as against the traffic light colours for H\&N treatments. For lung treatments, due to unavailability of CBCT based dose data, only EPID dosimetry was used; two different models of the back-projection algorithm were compared in this case. γ index and DVH metrics were used to express deviation in the dose distributions. Deviations over successive fractions for 18 H\&N treatments were studied. The traffic light protocol correlated poorly with CBCT based dose and EPID reconstructed dose (ρ = 0.33 and 0.35 respectively). The CBCT and EPID dose correlated with each other quite strongly (ρ = 0.72), however the EPID dose was more sensitive in its fluctuations. Deviations for 98 IMRT lung fractions were studied. The traffic light protocol correlated even more poorly with the EPID reconstructed dose than in the H\&N study (ρ = 0.18). The calculated transmission model of the EPID was found to exaggerate the deviations in comparison to the measured transmission model. Since VMAT innately uses the calculated transmission model, this explains the sensitivity of the EPID results seen in the H\&N study. We have shown that the traffic light protocol does not correlate with dosimetric changes, due to differences in assessment criteria. 15 out of 18 H\&N treatments showed moderate (ρ ≥ 0.4), if not strong, correlations between deviations of EPID reconstructed dose and CBCT based dose, strengthening the EPID's applicability for in-vivo dosimetry.

Introduction: High Dose Rate (HDR) Brachytherapy is a radiotherapy modality that involves temporarily introducing a highly radioactive source into the target volume with the use of an applicator. With respect to HDR brachytherapy for prostate cancer, an 192Iridium source is driven into the target volume through catheters implanted into the prostate. The dose delivered to a point in the prostate depends on the time the source dwells at a given position. Treatment planning for brachytherapy involve the optimization of dwell times and dwell positions. The aim of the treatment plan is to deliver the prescribed dose to the target volume, the prostate, while minimizing the dose to the organs at risk (OAR), namely the urethra, bladder and rectum. In current clinical practice, the process of treatment planning involves the manual manipulation of the parameters of an optimizer until the desired dose distribution is achieved. This implies that the plan quality depends on the experience of the planner, and there is variation in plan quality between planners. The aim of this project was to develop an automated treatment planning system that would able to generate clinically acceptable plans with minimal human intervention. The brachytherapy treatment planning module is named B-iCycle and may be integrated in the future with the treatment planning software suite, called Erasmus-iCycle, developed at the Erasmus MC.
Materials and methods: At the core of the treatment planning system (TPS) is a precise and fast dose engine that is able to simulate the dose to be delivered. In this project, we employ the TG-43 dose calculation formalism as it is the most widely implemented method in dose engines for brachytherapy treatment planning systems. The dose engine is then verified against the dose engine of the clinical treatment planning system. B-iCycle uses the 2-phase ϵ-constraint (2pϵc) algorithm to optimize the dwell times and positions. The 2pϵc algorithm requires a ‘wish-list’, which encapsulates the treatment protocol as goals and constraints for each critical structure. For this project three treatment protocols were chosen, four fractions of 9.5 Gy, single fraction of 19 Gy and single fraction of 20 Gy, and wish-lists were generated for each protocol. Three patient groups with different catheter geometries were selected. Treatment plans were generated for each patient and compared against the plans that were generated, for the same patients, in the clinic. The treatment plans that were generated in B-iCycle were then exported to the clinical treatment planning system (Oncentra from Elekta) to obtain the dose characteristics. The plans were compared based on the dose characteristics and the Conformity Index (COIN). The plans were also verified by a radiation oncologist.
Results: The TG-43 dose engine was successfully verified against the clinical dose engine. The Gamma analysis showed that only 0.68% of the voxels failed the gamma analysis and these voxels were located within the catheters therefore they can be ignored as no tissue lies at these positions. With regard to plans that were generated, the physician confirmed that the clinically acceptable B-iCycle plans are very comparable to the clinical plans. The B-iCycle plans are better at minimizing the dose to the urethra. When comparing B-iCycle plans to the clinical plans using COIN, B-iCycle was found to be better than the clinical procedure. B-iCycle can generate a treatment plan in approximately 10 seconds, which is much faster than the clinical procedure, which averages at 10 minutes. It is also able to avoid the issue of treatment planner variability and is able to generate consistent, high quality treatment plans.

Proton therapy is a relatively new technique in the field of radiation oncology. The advantage of using protons can be illustrated by the depth-dose relation, which results in a more concentrated dose at a specific depth and thus potentially less dose in the surrounding healthy tissue compared to conventional photon therapy. However, this depth-dose relation also makes the dose delivery very sensitive to small geometric uncertainties. Because of this sensitivity, high accuracy of the quality assurance (QA) is essential.
QA can be used to test robustness of the system when dealing with small geometric uncertainties such as air gaps. Most of the QA protocols use phantoms to test the clinical treatment protocol or the complete treatment chain. To accurately simulate a patient, the phantom should resemble the human anatomy as well as tissue composition its interaction properties with ionizing radiation. In order to be optimally test the accuracy of the treatment system, the phantom should include small air gaps, density gradients in soft and bone tissue-substitutes and millimeter-scale structures. Currently phantoms are produced using casting techniques, which limits the possibilities to include small features or density gradients. This causes the phantoms available to lack the level of detail required for proton therapy QA.
One important source of errors in current treatment planning is the usage of a mono energy CT-scan of the patient for treatment planning, which measures the photon attenuation and converts this to Hounsfield Units (HU). For proton therapy, the HU is converted to the proton stopping power ratio (SPR) of the tissue compared to water, using a HU-SPR conversion model. Since there is no one-to-one relation between HU and SPR, this inevitably leads to errors, for example when tissues with the same HU have a slightly different SPR. Therefore dual-energy CT (DECT) has been proposed as a replacement for conventional mono energetic CT in proton treatment planning. The information acquired by the DECT is used in the Bethe formula to calculate the SPR directly, making the HU-SPR calibration curve obsolete, thus improving the accuracy of treatment planning.
The goal of this study is to explore the possibilities of designing an anthropomorphic phantom with small geometric features using a 3D-printer. By using a 3D-printer we can print the structure of the phantom at the millimeter-scale. Multiple materials can be mixed while printing, making it easier to adjust material properties such as density. Since DECT could improve proton treatment planning, we will aim to use materials that will be compatible with DECT-based HU-SPR conversion methods, so that these materials will be treated similar to that of human tissue by the treatment system. This way, the phantom should have the same SPR as human tissue, as well as covering small geometric details.

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.

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