The use of non-ionizing radiation in the magnetic resonance imaging (MRI) made it a safer diagnostic technique in comparison to the X-ray imaging method. MRI can also produce soft tissue images with a very high contrast without contrast agent which is another advantage that made MRI an important imaging technique for studying the mechanism of the deep brain stimulation (DBS) and for targeting the desired regions in the brain that should be stimulated. For these and other advantages, the number of MRI examinations have increased hugely around the world including Sweden. Despite the consideration that MRI is a safe modality, it is not free from risks and hazards. The radiofrequency (RF)-induced heating of the tissues and the metallic implants is one of the safety concerns in MRI which certainly includes patients with DBS implant.The purpose of this project, motivated by the dangers accompanying the RF-induced heating in implantable DBS lead, is to investigate the effect of the exposure to the RF fields in MRI on these leads. After temperature measurements were made, the observations were focused on the amount of temperature increase, and the time required for the temperature to increase and then to decrease to its initial value. Some factors that could affect the lead temperature were studied and that includes the effect of the lead configuration, the lead surrounding medium, the exposure level, and the orientation of the lead coil with respect to the RF field.The result showed that the temperature of the lead (placed in air) increased more but slower when the lead was formed as a coil than when it was randomly configured. It was also showed that the lead coil temperature rise was higher and faster when the coil was placed in air than when it was immersed in deionized water or in saline. The lead coil temperature rise was higher, but slower when the coil was immersed in saline compared to deionized water. Also, exposure level affected the temperature rise such that the higher exposure level showed higher and faster temperature rise of the lead coil. When the lead coil was placed in air and oriented perpendicular to the strongest magnetic field component, its temperature increased higher and faster. On the other hand, the results when the lead coil was immersed in deionized water or in saline showed a deviation from when it was placed in air such that two magnetic field components had the same effect on the lead coil temperature. The time required for the temperature to decrease to its initial value, after the end of the exposure, depended on the magnitude of the RF magnetic flux density, orientation of the lead coil with respect to the RF magnetic field, and the lead surrounding medium. The stronger RF magnetic field is, the longer time is for the temperature to decrease. Consequently, when the lead coil was directed perpendicularly to the strongest component of the RF magnetic field, it took longer time for the temperature to decrease. The time for the temperature to decrease was longer when the lead coil was immersed in water (deionized or saline) than when the lead coil was placed in air. It also took longer time for the temperature to decrease when the lead coil was immersed in saline than in deionized water.

<p dir="ltr">Introduction: Radiotherapy is a treatment modality that is prescribed for more than 50% of cancer patients around the globe. Through decades of clinical application, RT has witnessed considerable advancements achieving significant tumor control with minimal damage to healthy tissues. Recently, a paradigm shift has recognized RT's potential to induce anti-tumor immune responses, where patients receiving radiation to the primary tumor also resolved lesions outside the treatment field. This out-of-field response also referred to as an abscopal effect, is believed to promote immunogenic cell death (ICD) initiated by the radiation-induced DNA damage and subsequent activation of the cGAS-STING-IFNβ pathways. However, clinical realization of an abscopal effect remains rare. We <i><u>hypothesize</u></i> by selectively irradiating cancer cells with high metastatic potential within a solid tumor (intra-tumor radiotherapy treatment planning) with high metastatic potential, a more efficient anti-tumor response can be achieved while minimizing inflammatory responses from surrounding tumor and normal tissues, obfuscating a potential adaptive immune response, thus help in overcoming the rarity observed in the clinical practice. To achieve this <i><u>objective</u></i>, radiotherapy treatment plans targeting hypoxic regions (known to harbor a metastatic phenotype) within a solid tumor and optimally activating IFNβ will be investigated.</p><p dir="ltr">Methods: Hypoxic conditions within tumor microenvironments significantly reduce DNA damage, conferring a radioresistant phenotype that leads to RT failure. To address the inherent radioresistance and immunosuppression of hypoxic tumors, high linear energy transfer (LET) modalities are used. Our research aims to enhance the specificity and efficiency of ICD, particularly in highly metastatic (hypoxic) regions within the tumor, by employing heavy charged particle (HCP) beams to optimize DSB induction. Empirical mathematical models have been developed to predict the dose-response of IFNb based on in vitro data and Monte Carlo methods of DSB-induction. These methods are used in maximizing type I interferon (IFNβ) production and subsequent immune response while minimizing the inflammatory response and damage to surrounding tissue. Immunogenic treatment plans, iTPS, have been developed to integrate charged particle beam models for proton, helium, and carbon ions and the above-empirical models into FLUKA Monte Carlo simulations and subsequently evaluated in clinical case studies of brain and lung cancer. Next, new biophysical models accounting for tumor hypoxia were developed and integrated into the iTPS, and clinical case studies were reevaluated.</p><p dir="ltr">Results: SA(1): Developed and integrated charged particle beam models into FLUKA MC for both homogeneous and heterogeneous treatment planning. Empirical equations for RBE_{DSB, pO2}, LET, and IFNβ dose-response were incorporated into FLUKA for voxel-based simulations across oxygen levels. SA(2): RBE_DSB-weighted optimization yielded uniform IFNβ production. High LET enabled carbon ion beams to require the lowest doses, achieving superior peak-to-entrance ratios of 15.85 compared to 10.78 and 7.60 for helium and proton beams, respectively. Patient simulations demonstrated carbon ions' superiority, with D_95% values of 7.68 Gy for the brain and 7.60 Gy for lung tumors, excelling in IFNβ production. SA(3): An optimized treatment plan for uniform IFNβ in hypoxia utilizing empirical equations for RBE_DSB across hypoxia levels was created for different charged particles. MCC13 adjustments based on OER_DSB from MCDS were confirmed by measured data in U251 cell lines, showing an OER of 1.5 between normoxia and 1% hypoxia, closely matching MCDS predictions within a 7% discrepancy. Carbon ions achieved optimal IFNβ at 11.02 Gy for brain tumors under 0.1% hypoxia in FLUKA simulations.</p><p dir="ltr">Conclusions: Our results from both homogeneous target and patient cases demonstrate that charged particles have the potential to elicit higher levels of IFNβ at lower doses compared to photon irradiations in different pO₂ levels. High LET irradiation not only ensures a highly localized IFNβ response in the target but also effectively spares surrounding normal tissues, thereby minimizing treatment-related toxicity. This finding underscores the superiority of high LET irradiation in achieving targeted immunogenic effects while enhancing the therapeutic window by reducing damage to normal cells.</p>

A patient specific image planning system (IPS) was developed that can be used to assist in kV imaging technique selection during localization for radiotherapy. The IPS algorithm performs a divergent ray-trace through a three dimensional computed tomography (CT) data set. Energy-specific attenuation through each voxel of the CT data set is calculated and imaging detector response is integrated into the algorithm to determine the absolute values of pixel intensity and image contrast. Phantom testing demonstrated that image contrast resulting from under exposure, over exposure as well as a contrast plateau can be predicted by use of a prospective image planning algorithm. Phantom data suggest the potential for reducing imaging dose by selecting a high kVp without loss of image contrast. In the clinic, image acquisition parameters can be predicted using the IPS that reduce patient dose without loss of useful image contrast.

Le développement de nouvelles modalités de traitement telles que la RCMI et la radiothérapie stéréotaxique s'accompagne d'une utilisation croissante de champs d'irradiation complexes obtenus par superposition de faisceaux de petite taille ayant de multiples angles d'incidence. Ces nouvelles conditions de traitement sont très différentes des conditions de référence sur lesquelles se basent les protocoles dosimétriques internationaux. Ces travaux de thèse se proposent de réaliser des références dosimétriques pour des champs d'irradiation de dimensions inférieures à 10 x 10 cm², à savoir 4 x 4 et 2 x 2 cm². Il s'agit, dans la pratique, de comparer les coefficients d'étalonnage d'une chambre d'ionisation en termes de dose absorbée dans l'eau, pour les faisceaux de photons de 6 MV (avec et sans cône égalisateur) et de 12 MV de l'accélérateur linéaire médical du LNHB. Les références ont été déterminées à partir d'une mesure par calorimétrie graphite. Pour les mesures en champ 2 x 2 cm², un calorimètre disposant d'un absorbeur de petites dimensions a été construit. Par ailleurs, une chambre d'ionisation adaptée à cette taille de champ a été recherchée. Nous avons montré que, pour les faisceaux étudiés, le coefficient d'étalonnage de la chambre d'ionisation de référence est indépendant de la dimension du champ d'irradiation entre 10 x 10 et 2 x 2 cm², aux incertitudes près (environ 0,4 % à un écart-type).

La tomographie par émission de positrons est une technique d'imagerie nucléaire utilisant des noyaux radioactifs. Elle est utilisée dans le domaine clinique et préclinique. Cette dernière nécessite l'utilisation de petits animaux, comme la souris. Comme en imagerie clinique, l'objectif est d'obtenir le meilleur signal avec la meilleure précision spatiale possible. Cependant, un rapport d'échelle homme-souris suggère une résolution inférieure à 1 mm3. Un imageur conventionnel est constitué de modules de détection entourant le patient, orientés radialement. Cette approche lie efficacité et résolution spatiale. Ce travail concerne l'étude de la géométrie axiale. Les éléments de détection sont ici orientés parallèlement à l'objet. Ceci limite la corrélation entre efficacité de détection et résolution spatiale, et ainsi permet d'obtenir une haute résolution et haute sensibilité. La simulation de prototypes a permis d'envisager une résolution spatiale moyenne inférieure au millimètre et une efficacité de 15 ou 40% selon l'extension axiale. Ces résultats permettent de présager de bonnes perspectives en imageries préclinique et clinique.

This work establishes a framework for Monte Carlo simulations of complex, modulated electron fields produced by Varian's TrueBeam medical linear accelerator for investigations into modulated electron radiation therapy (MERT) and combined modulated photon and electron radiation therapy (MPERT). Both MERT and MPERT have shown potential for reduced low dose to normal tissue without compromising target coverage in the external beam radiation therapy of some breast, chest wall, head and neck, and scalp cancers. This reduction in low dose could translate into the reduction of immediate radiation side effects as well as long term morbidities and incidence of secondary cancers. Monte Carlo dose calculations are widely accepted as the gold standard for complex radiation therapy dose modelling, and are used almost exclusively for modelling the complex electron fields involved in MERT and MPERT. The introduction of Varian's newest linear accelerator, the TrueBeam, necessitated the development of new Monte Carlo models in order to further research into the potential role of MERT and MPERT in radiation therapy. This was complicated by the fact that the field independent internal schematics of TrueBeam were kept proprietary, unlike in previous generations of Varian accelerators. Two approaches are presented for performing Monte Carlo simulations of complex electron fields produced by TrueBeam. In the first approach, the dosimetric characteristics of electron fields produced by the TrueBeam were first compared with those produced by an older Varian accelerator, the Clinac 21EX. Differences in depth and profile characteristics of fields produced by the TrueBeam and those produced by the Clianc 21EX were found to be within 3%/3 mm. Given this information, complete accelerator models of the Clinac 21EX, based on its known internal geometry, were then successfully modified in order to simulate 12 and 20 MeV electron fields produced by the TrueBeam to within 2%/2 mm of measured depth and profile curves and to within 3.7% of measured relative output. While the 6 MeV TrueBeam model agreed with measured depth and profile data to within 3%/3 mm, the modified Clinac 21EX model was unable to reproduce trends in relative output as a function of field size with acceptable accuracy. The second approach to modelling TrueBeam electron fields used phase-space source files provided by Varian that were scored below the field-independent portions of the accelerator head geometry. These phase-spaces were first validated for use in MERT and MPERT applications, in which simulations using the phase-space source files were shown to model depth dose curves that agreed with measurement within 2%/2 mm and profile curves that agreed with measurement within 3%/3 mm. Simulated changes in output as a function of field size fell within 2.7%, for the most part. In order to inform the positioning of jaws in MLC-shaped electron field delivery, the change in output as a function of jaw position for fixed MLC-apertures was investigated using the phase-space source files. In order to achieve maximum output and minimize treatment time, a jaw setting between 5 and 10 cm beyond the MLC- field setting is recommended at 6 MeV, while 5 cm or closer is recommended for 12 and 20 MeV with the caveat that output is most sensitive to jaw position when the jaws are very close to the MLC-field periphery. Additionally, output was found to be highly sensitive to jaw model. A change in divergence of the jaw faces from a point on the source plane to a 3x3 mm^2 square in the source plane changed the shape of the output curve dramatically. Finally, electron backscatter from the jaws into the monitor ionization chamber of the TrueBeam was measured and simulated to enable accurate absolute dose calculations. Two approaches were presented for measuring backscatter into the monitor ionization chamber without specialized electronics by turning o the dose and pulse forming network servos. Next, a technique was applied for simulating backscatter factors for the TrueBeam phase-space source models without the exact specifications of the monitor ionization chamber. By using measured backscatter factors, the forward dose component in a virtual chamber was determined and then used to calculate backscatter factors for arbitrary fields to within 0.21%. Backscatter from the jaws was found to contribute up to 2.6% of the overall monitor chamber signal. The measurement techniques employed were not sensitive enough to quantify backscatter from the MLC, however, Monte Carlo simulations predicted this contribution to be 0.3%, at most, verifying that this component can be neglected. / Graduate / 0756 / lloyd.samantha@gmail.com

A fundamental challenge in the treatment planning process of multi-fractional external-beam radiation therapy (EBRT) is the tradeoff between tumor control and normal tissue sparing in the presence of geometric uncertainties (GUs). To accommodate GUs, the conventional way is to use an empirical planning treatment volume (PTV) margin on the treatment target. However, it is difficult to determine a near-optimal PTV margin to ensure specified target coverage with as much normal tissue protection as achievable. Coverage optimized planning (COP) avoids this problem by optimizing dose in possible virtual treatment courses with GU models directly incorporated. A near-optimal dosimetric margin generated by COP was reported to savvily accommodate setup errors of target and normal tissues for prostate cancer treatment. This work further develops COP to account for (1) deformable organ motion and (2) delineation uncertainties for high-risk prostate cancer patients. The clinical value of COP is investigated by comparing with two margin-based planning techniques: (i) optimized margin (OM) technique that iteratively modifies PTV margins according to the evaluated target coverage probability and (ii) fixed margin (FM) technique that uses empirically selected constant PTV margins. Without patient-specific coverage probability estimation, FM plans are always less immune to the degraded effect of the modeled GUs than the COP plans or the OM plans. Empirical PTV margins face more risks of undesirable target coverage probability and/or excessive dose to surrounding OAR. The value of COP relative to OM varies with different GUs. As implemented for deformable organ motions, COP has limited clinical benefit. Due to optimization tradeoffs, COP often results in target coverage probability below the prescribed value while OM achieves better target coverage with comparable normal tissue dose. For delineation uncertainties, the clinical value of COP is potentially significant. Compared to OM, COP successfully maintains acceptable target coverage probability by exploiting the slack of normal tissue dose in low dose regions and maximally limiting high dose to normal tissue within tolerance.

This dissertation describes the application of two principled variance reduction strategies to increase the efficiency for two applications within medical physics. The first, called correlated Monte Carlo (CMC) applies to patient-specific, permanent-seed brachytherapy (PSB) dose calculations. The second, called adjoint-biased forward Monte Carlo (ABFMC), is used to compute cone-beam computed tomography (CBCT) scatter projections. CMC was applied for two PSB cases: a clinical post-implant prostate, and a breast with a simulated lumpectomy cavity. CMC computes the dose difference between the highly correlated dose computing homogeneous and heterogeneous geometries. The particle transport in the heterogeneous geometry assumed a purely homogeneous environment, and altered particle weights accounted for bias. Average gains of 37 to 60 are reported from using CMC, relative to un-correlated Monte Carlo (UMC) calculations, for the prostate and breast CTV’s, respectively. To further increase the efficiency up to 1500 fold above UMC, an approximation called interpolated correlated Monte Carlo (ICMC) was applied. ICMC computes using CMC on a low-resolution (LR) spatial grid followed by interpolation to a high-resolution (HR) voxel grid followed. The interpolated, HR is then summed with a HR, pre-computed, homogeneous dose map. ICMC computes an approximate, but accurate, HR heterogeneous dose distribution from LR MC calculations achieving an average 2% standard deviation within the prostate and breast CTV’s in 1.1 sec and 0.39 sec, respectively. Accuracy for 80% of the voxels using ICMC is within 3% for anatomically realistic geometries. Second, for CBCT scatter projections, ABFMC was implemented via weight windowing using a solution to the adjoint Boltzmann transport equation computed either via the discrete ordinates method (DOM), or a MC implemented forward-adjoint importance generator (FAIG). ABFMC, implemented via DOM or FAIG, was tested for a single elliptical water cylinder using a primary point source (PPS) and a phase-space source (PSS). The best gains were found by using the PSS yielding average efficiency gains of 250 relative to non-weight windowed MC utilizing the PPS. Furthermore, computing 360 projections on a 40 by 30 pixel grid requires only 48 min on a single CPU core allowing clinical use via parallel processing techniques.

To improve relatively poor outcomes for locally-advanced lung cancer patients, many current efforts are dedicated to minimizing uncertainties in radiotherapy. This enables the isotoxic delivery of escalated tumor doses, leading to better local tumor control. The current dissertation specifically addresses inter-fractional uncertainties resulting from patient setup variability. An automatic block-matching registration (BMR) algorithm is implemented and evaluated for the purpose of directly localizing advanced-stage lung tumors during image-guided radiation therapy. In this algorithm, small image sub-volumes, termed “blocks”, are automatically identified on the tumor surface in an initial planning computed tomography (CT) image. Each block is independently and automatically registered to daily images acquired immediately prior to each treatment fraction. To improve the accuracy and robustness of BMR, this algorithm incorporates multi-resolution pyramid registration, regularization with a median filter, and a new multiple-candidate-registrations technique. The result of block-matching is a sparse displacement vector field that models local tissue deformations near the tumor surface. The distribution of displacement vectors is aggregated to obtain the final tumor registration, corresponding to the treatment couch shift for patient setup correction. Compared to existing rigid and deformable registration algorithms, the final BMR algorithm significantly improves the overlap between target volumes from the planning CT and registered daily images. Furthermore, BMR results in the smallest treatment margins for the given study population. However, despite these improvements, large residual target localization errors were noted, indicating that purely rigid couch shifts cannot correct for all sources of inter-fractional variability. Further reductions in treatment uncertainties may require the combination of high-quality target localization and adaptive radiotherapy.

The purpose of this work is to present the development and validation of a novel method for reconstructing time-dependent, or 4D, cone-beam CT (4DCBCT) images. 4DCBCT can have a variety of applications in the radiotherapy of moving targets, such as lung tumors, including treatment planning, dose verification, and real time treatment adaptation. However, in its current incarnation it suffers from poor reconstruction quality and limited temporal resolution that may restrict its efficacy. Our algorithm remedies these issues by deforming a previously acquired high quality reference fan-beam CT (FBCT) to match the projection data in the 4DCBCT data-set, essentially creating a 3D animation of the moving patient anatomy. This approach combines the high image quality of the FBCT with the fine temporal resolution of the raw 4DCBCT projection data-set. Deformation of the reference CT is accomplished via a patient specific motion model. The motion model is constrained spatially using eigenvectors generated by a principal component analysis (PCA) of patient motion data, and is regularized in time using parametric functions of a patient breathing surrogate recorded simultaneously with 4DCBCT acquisition. The parametric motion model is constrained using forward iterative projection matching (FIPM), a scheme which iteratively alters model parameters until digitally reconstructed radiographs (DRRs) cast through the deforming CT optimally match the projections in the raw 4DCBCT data-set. We term our method FIPM-PCA 4DCBCT. In developing our algorithm we proceed through three stages of development. In the first, we establish the mathematical groundwork for the algorithm and perform proof of concept testing on simulated data. In the second, we tune the algorithm for real world use; specifically we improve our DRR algorithm to achieve maximal realism by incorporating physical principles of image formation combined with empirical measurements of system properties. In the third stage we test our algorithm on actual patient data and evaluate its performance against gold standard and ground truth data-sets. In this phase we use our method to track the motion of an implanted fiducial marker and observe agreement with our gold standard data that is typically within a millimeter.