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.

Targeted Alpha Therapy (TAT) is an interesting technique in tumour treatments, especially in the treatment of local tumours or the treatments of solid tumours while minimizing unwanted radiation to surrounding tissue. The use of Ac-225 in TAT is especially interesting as in the decay of Ac-225 four alpha particles are emitted. The recoil effect will however disrupt the daughter nuclide from the targetting molecule, allowing the daughter nuclide to drift away from the tumour cells to healthy tissue. In order to study the redistribution of radionuclides due to the recoil effect imaging tools are required. An existing technique is the classic alpha camera, this technique is based on the scintillation of alpha particles. Emitted photons by the scintillating layer are registered by a charge coupled device. This technique allows for a spatial resolution of up to 35 μm but is limited by the uptake of other radiation forms (photon, beta). Another alpha imaging technique is called the Timepix, this is a hybrid pixelated semiconducting sensor that consists of an array of 55 μm square pixels. The semiconducting material (silicon) converts ionizing radiation into charge carriers that are collected at pixel sites. In this research the limitations of the Timepix chip with regard to spatial & energy resolution in alpha particle imaging are investigated. The spatial resolution was investigated by the use of fine edged alpha absorbing objects, a small pitched collimators to ensure a monochromatic beam of alpha particles, and an alpha emitting source (Am-241). The Timepix chip registered the incoming alpha particles. By binning the counted alpha particles in small inter- vals a step function was obtained, from which a Gaussian function was obtained. The Full Width at Halve Maximum (FWHM) is a measure of (spatial) resolution of the Gaussian function. Results showed a spatial resolution of 9.8 μm obtained by a collimator with pitch of 6 μm. The obtained spatial resolution shows that sub-pixel resolution in alpha particle imaging is possible. The energy resolution of the Timepix chip was investigated by the use of a collimator to ensure a monochro- matic beam of alpha particles, several Mylar foils to slow down the alpha particles and an alpha emitting source (Am-241). The use of the time over threshold mode (TOT) indicates the amount of time a signal is above threshold, which is a direct indication of the particles energy. Output of the chip is Gaussian shaped, from which the energy resolution was obtained. The obtained energy resolution of the Timepix chip was 0.8 MeV, which allows for identification of most daughter nuclides from the decay chain of Ac-225. This energy resolution does not allow for the identification of the disintegration of Fr-221 (6.64 MeV) and Ac-225 (5.94 MeV).

A time of flight MIEZE spectrometer, which employs radio frequency spin flippers with square pole shoes and a magnetic yoke is presented. These flippers can achieve higher fields than conventional resonant RF flippers, which employ an air core. High fields are crucial for the construction of a high resolution and compact MIEZE spectrometer. Setups using conventional and novel flippers are constructed for comparison and a variety of experiments to characterize MIEZE instruments. Evidence is presented which indicates that high field flippers are capable of generating a 100kHz MIEZE signal comparable to that obtained with a conventional setup. Furthermore the need for a fast and thin detector is demonstrated. In addition the shape of the MIEZE focal spot is determined to be Gaussian. Finally the importance of stable timing for time of flight MIEZE is demonstrated. This research is relevant for the implementation of MIEZE on the Larmor instrument at ISIS pulsed neutron source in the UK.

The main objective of this research was to determine the feasibility of using the Timepix3 detector, a novel hybrid-pixel detector developed by CERN, as a tool for imaging ex-vivo slices of tumour tissue treated with 225Ac Targeted Alpha Therapy. Targeted Alpha Therapy using 225Ac-PSMA shows promise as a therapeutic method, as it allows direct targeting of the tumour and precise irradiation of cancer cells while sparing healthy tissue due to the short range of α-particles (typically 50-100μm). However, due to the recoil effect, the daughters of 225Ac can break loose from the targeting vector, potentially diffusing away from the tumour site and harming healthy tissue resulting in unwanted side effects. To investigate this, several experiments were conducted using the Timepix3 detector, capable of energy, spatial and time-resolved measurements. Before experiments could be conducted, a data processing tool was developed and an optimal bias voltage of 30V was determined. Subsequently, the Timepix was calibrated using a γ and α calibration, which yielded energy resolutions of 4.27±0.06% and 4.96±0.06% compared to 6.35±0.04% without calibration when measuring α-particles from 225Ac locally. The Timepix3 was then tested using different collimators for whole surface measurements. For a plastic collimator (L/D=2.5, ⌀=1mm) the energy resolution improved from 15.7±0.3% to 11.9% and 9.6±0.1%. For a lead-glass collimator (L/D=50, ⌀=24.8μm) the energy resolution improved to 13.5% and 12.6±0.4%, here the uncalibrated resolution could not be determined. Finally, the spatial resolution of the Timepix was determined using the plastic collimator, which was 310±10μm. Based on the findings, the Timepix3 detector is not suitable for the proposed application, an imaging tool to determine nuclide distribution in 225Ac-PSMA treated tissue samples, using this particular setup. However, using a different collimator with a more suitable L/D ratio should definitely be capable as this improves spatial and energy resolution further. Furthermore, the Timepix3 detector was used in a clinical test to directly measure and determine the nuclide contents of the radio-pharmaceutical 225Ac-PSMA separated by high-performance liquid chromatography. The results indicated an initially lower amount of 213Po in the sample which grew over time This indicates that the HPLC is capable of separating individual nuclides. This result also demonstrated the potential use of the Timepix3 detector for these types of applications. In experiments where the Timepix3 was placed inside a 70MeV and 120MeV proton beam to test its feasibility as a beam verification tool, the results were less successful due to the high intensity of the proton beam. However, it was concluded that it was still possible to measure scattered protons and secondary products. 

Nano-carriers have the potential to be an enormous game-changer in medicinal drug delivery systems. The polymeric nano-carriers used in this study are a product of the self-assembly of amphiphilic block copolymers, a complicated process which must be understood completely to finely tune the desired morphology for drug delivery. The goal of this thesis is to gain a better understanding of the self-assembly process of amphiphilic block copolymers. Specifically, it will focus on the ’opaque phase’ observed for poly(1,2-butadiene)-b-poly(ethylene oxide) (PBd-PEO) block copolymers, which seems to occur in the early stages of the self-assembly process. A nano-precipitation method has been developed at the TU Delft, which induces selfassembly and brings forward the opaque phase. The used block copolymer has a hydrophobic PBd block and a hydrophilic PEO block. This block copolymer dissolves well in acetone, but upon water (H2O) addition, it starts to self-assemble into spherical aggregates, useful for drug delivery. At small volumes of H2O, the opaque phase appears and disappears as more H2O is added. In this thesis, multiple samples have been prepared with the so-called Inverse Nanoprecipitation method and different experimental parameters among which the volume percentage of H2O present in the sample, have been varied. The samples have been studied using Visual Inspection, Dynamic Light Scattering and Spin Echo Small Angle Neutron Scattering. The experiments show that the time intervals between H2O addition do not affect the formation of aggregates, but rather the ‘when’ of adding the H2O. If this is added to the acetone before the block copolymer is dissolved, it affects the self-assembly process. A visual experiment showed that the opaque phase occurred 1.2±0.1 vol% H2O earlier than in previous research, which might be a result of the lower room temperature during this thesis. Another significant result might be that the addition of acetone-D6 or D2O affects the self-assembly process, which must be considered for future SESANS measurements. Lastly, during the opaque phase a strong temperature sensitivity is observed (which was already found in previous research at TU Delft, by E. Remmelts and further researched by R. Baaijens), high light scattering intensities are detected with DLS and for SESANS measurements the scattered neutron intensities were low. These observations all strongly point to a theory called ‘pre-micellization’, which gives a better understanding of the opaque phase.