Towards EPID-based 3D in vivo dosimetry for modern radiation therapy

Modern radiotherapy techniques, such as Intensity Modulated Radiation Therapy (IMRT) and Volumetric Modulated Arc Therapy (VMAT), can deliver highly conformal dose distributions, with steep dose gradients between the target and organs at risk. This increases the demands on proper quality assurance and dose verification before (pre-treatment) and during (in vivo) patient irradiation. This project proposes a methodology for EPID-based in vivo dosimetry, combining the accuracy of Monte Carlo (MC) methods for dose simulation in patient geometry, with the time-efficiency of deep neural networks. The Deep Dose Estimation (DDE) network, originally developed for dose estimation in radiological computed tomography (CT) exams, has been extended and trained to predict 3D dose distributions due to IMRT fields, inside a patient, with accuracy comparable to MC methods. The DDE uses as input a patient CT image and an approximated dose distribution, called first order dose approximation (FOD), reconstructed from simulated EPID signals. The network was trained to map this two-channel input to an accurate dose distribution (ADD) inside the same patient CT, simulated using MC methods. The FODs are simplified 3D dose distributions produced as backprojections of the simulated EPID signals, accounting for magnification and inverse square law corrections, and attenuation through the virtual patient model. The FODs do not account for several effects, such as the build-up, beam hardening and scattering within the patient, all of which were properly considered in the ADDs. Hence, the methodology relies strongly on the MC model used to produce both the ADD and the transmitted EPID signals. A reliable MC model of the linac considered in this work was constructed and extensively validated. The patient-dependent part of the linac head, namely the multi-leaf collimator (MLC) system, was produced based entirely on information available in the literature. A virtual model of the EPID was also included in the patient-dependent part, to simultaneously record the transmitted signal through the virtual patient. The patient-independent part, i.e. the static parts of the linac head, was constructed based on confidential information provided by the vendor, and used to produce phase space (PhSp) files. These PhSp files were subsequently used as primary particle generators to simulate the ADDs and EPID signals. An alternative methodology for optimization of existing IAEA PhSp files was developed as a side project, which can be used to model the patient-independent part of the linac head when confidential vendor information is not available. The ADDs for clinical prostate IMRT fields, and respective transmitted EPID signals, were simulated inside 83 pelvic CTs, with gantry at 0�. In total, 581 different ADD-FOD sets were produced, with seven different fields per patient CT. The network was trained using the data sets of 67 patients (training set). The data of the remaining 16 patients were used for validation (test set). An additional dataset with eight fields simulated with gantry at 90� (lateral set) was used for evaluating the performance of the trained DDE for other irradiation directions. The quality of the DDE-predicted dose distributions (DDEP) on the test and lateral sets was quantified in terms of the gamma analysis with respect to the ADD (3%, 2 mm criteria). To evaluate the improvement obtained with the DDE, the same evaluation was performed for FODs and respective ADDs. The gamma passing rates between FODs and ADDs were as low as 46%, while for DDEPs the passing rates were above 97% for all fields on the test set. For the fields in the lateral set, the DDE was able to improve the passing rates from 88% to above 95%. The high passing rates for DDEPs indicate that the DDE was able to convert the FODs into ADDs, properly accounting for all missing effects. Moreover, once trained, the DDE can predict the dose inside a patient CT within 0.6 s per field (using a GPU), in contrast to 14 h needed for MC simulations (using a CPU-cluster). The dose delivered to a patient due to an entire prostate treatment session can therefore be predicted in less than one minute. With the proposed methodology, 3D in vivo dose distributions due to clinical patient irradiation can be obtained within seconds, potentially paving the way towards a clinically viable, real-time EPID-based in vivo dosimetry

Feasibility of magnetic resonance imaging-based radiation therapy for brain tumour treatment

Purpose : The increasing use of MRI alongside CT images has brought about growing interest in trying to determine radiation attenuation information based on MR images only. The primary aim of this thesis is, therefore, to determine what head tissue compartments need to have separate HU values in order to obtain sufficient RT planning accuracy. This can serve as input for an MR-based classification thus enabling pseudo-CT generation in an MR-only RT workflow. Methods: To achieve this target, flattened (stratified) CT images (fCT) were generated and compared to the original CT images. Mean (ME) and mean absolute (MAE) errors were used for the fCT quality assessment, as was dose comparisons. 70 CT-based RT plans were generated and the dose distributions compared to those obtained when using the different fCT versions in place of the original CT images. The dose agreement was assessed using DVH and 1%/1mm gamma analysis. Results: The lowest MAE of 59.63 HU was calculated for an fCT8 version. DVH analysis showed low differences in the range between 3% (water-filled fCT) and 0.05% depending on the tissue stratification of the fCT version. 1%/1mm gamma analysis correctly identified plans where insufficiently fine-grained tissue classification was the main reason for dose discrepancy. The best RT planning accuracy was obtained for the fCT5 with segmented air cavities, fat, water-rich tissue, spongy, and compact bone, and for the fCT8 where also the brain tissue was stratified. Conclusions: The small differences in dose accuracy between CT and fCT images shows the feasibility of using MR-only RT planning for the brain. Nonetheless, other aspects of the MR-only workflow, such as patient positioning, as well as the impact of e.g. the surgical incisions in the skull should be subject to further research

Strategies for adaptive radiotherapy: towards clinically efficient workflows

Adaptive radiotherapy (ART) aims to adapt the treatment plan to account for inter-fraction anatomical variations, based on online acquired images. However, ART workflows are not –yet– routinely used in clinical practice, primarily due to the dramatic increase of the workload required and the inadequate understanding of optimal methods to maximise clinical benefit. This thesis reports on investigations of procedures for the automation of the ART process and the identification of optimal adaptation methodologies. Investigated auto-segmentation algorithms were found insufficient for an automated workflow, while a hybrid deformable image registration (DIR), incorporating both intensity based and feature-based components, revealed the most accurate and robust performance. An evaluation method was proposed for interfraction treatment monitoring through dose accumulation following DIR. The robustness of several treatment methods to observable anatomical changes were investigated, highlighting cases whereby substantial dosimetric consequences may arise. Offline ART workflows were explored, specifically investigating the effects of treatment monitoring frequency, adaptation method (simple re-plan or re-optimisation addressing cumulative dose), and adaptation timing. Contrary to simple re-planning, re-optimisation demonstrated its ability to compensate for under-/over-dose, however, non-uniform dose distributions and hot-spots may be generated. Therefore established planning techniques are applicable for re-planning while advanced approaches are required for treatment re-optimisation accounting for radiobiological consequences

Magnetic resonance imaging to improve structural localisation in radiotherapy planning

The purpose of this thesis is to develop the role of magnetic resonance imaging (MRI) in the radiotherapy (RT) planning process. This began by assessing a prototype inline three-dimensional distortion correction algorithm. A number of quality assurance tests were conducted using different test objects and the 3D distortion correction algorithm was compared with the standard two-dimensional version available for clinical use on the MRI system. Scanning patients using MRI in the RT position within an immobilisation mask can be problematic, since the multi-channel head coils typically used in diagnostic imaging, are not compatible with the immobilisation mask. To assess the image quality which can be obtained with MR imaging in the RT position, various MRI quality assurance phantoms were positioned within an immobilisation mask and a series of image quality tests were performed on four imaging coils compatible with the immobilisation mask. It was shown that only the 4-channel cardiac coil delivered comparable image quality to a multi-channel head coil. An investigation was performed to demonstrate how MRI patient position protocols influence registration quality in patients with prostate cancer undergoing radical RT. The consequences for target volume definition and dose coverage with RT planning were also assessed. Twenty patients with prostate cancer underwent a computed tomography (CT) scan in the RT position, a diagnostic MRI scan and an MRI scan in the RT position. The CT datasets were independently registered with the two MRI set-ups and the quality of registration was compared. This study demonstrated that registering CT and MR images in the RT position provides a statistically significant improvement in registration quality, target definition and target volume dose coverage for patients with prostate cancer. A similar study was performed on twenty-two patients with oropharyngeal cancer undergoing radical RT. It was shown that when patients with oropharyngeal cancer undergo an MRI in the RT position there are significant improvements in CT-MR image registration, target definition and target volume dose coverage

High Performance Optical Computed Tomography for Accurate Three-Dimensional Radiation Dosimetry

Optical computed tomography (CT) imaging of radiochromic gel dosimeters is a method for truly three-dimensional radiation dosimetry. Although optical CT dosimetry is not widely used currently due to previous concerns with speed and accuracy, the complexity of modern radiotherapy is increasing the need for a true 3D dosimeter. This thesis reports technical improvements that bring the performance of optical CT to a clinically useful level. New scanner designs and improved scanning and reconstruction techniques are described. First, we designed and implemented a new light source for a cone-beam optical CT system which reduced the scatter to primary contribution in CT projection images of gel dosimeters from approximately 25% to approximately 4%. This design, which has been commercially implemented, enables accurate and fast dosimetry. Second, we designed and constructed a new, single-ray, single-detector parallel-beam optical CT scanner. This system was able to very accurately image both absorbing and scattering objects in large volumes (15 cm diameter), agreeing within ∼1% with independent measurements. It has become a reference standard for evaluation of optical CT geometries and dosimeter formulations. Third, we implemented and characterized an iterative reconstruction algorithm for optical CT imaging of gel dosimeters. This improved image quality in optical CT by suppressing the effects of noise and artifacts by a factor of up to 5. Fourth, we applied a fiducial-based ray path measurement scheme, combined with an iterative reconstruction algorithm, to enable optical CT reconstruction in the case of refractive index mismatch between different media in the scanner’s imaged volume. This improved the practicality of optical CT, as time-consuming mixing of liquids can be avoided. Finally, we applied the new laser scanner to the difficult dosimetry task of small-field measurement. We were able to obtain beam profiles and depth dose curves for 4 fields (3x3 cm2 and below) using one 15 cm diameter dosimeter, within 2 hours. Our gel dosimetry depth-dose curves agreed within ∼1.5% with Monte Carlo simulations. In conclusion, the developments reported here have brought optical CT dosimetry to a clinically useful level. Our techniques will be used to assist future research in gel dosimetry and radiotherapy treatment techniques

New concepts for beam angle selection in IMRT treatment planning : From heuristics to combinatorial optimization

This thesis investigates beam ensemble selection strategies in intensity-modulated radiation therapy treatment planning. Beam ensemble selection strategies are applied to find the very beam ensembles that meet the treatments' objectives at the best possible rate. (1) A formal description of the beam ensemble selection problem is presented and the characteristics of the search space is discussed with a focus on its non-convexity and exponential complexity. (2) We review existing approaches to beam ensemble selection and provide a comprehensive overview of the field. (3) Conceptual advancements of beam ensemble selection strategies relying on score functions and geometric considerations are introduced. For photons, we demonstrate a clear benefit regarding organ at risk sparing for asymmetric patient geometries as regularly observed within the abdomen or skull. For protons, phantom studies yield plausible beam configurations. The measures taken to guarantee robustness regarding potential uncertainties are promising but require refinements. (4) The simultaneous optimization of beamlet weights and beam orientations is investigated at a very high precision. We apply different metaheuristics for the combinatorial optimization of beam ensembles and confirm the beneficial performance of genetic algorithms in this context. Both heuristic selection and combinatorial optimization of beam ensembles may yield extensive benefits for complicated planning cases. In the future it will be critical to transfer automated beam ensemble selection to the clinic for the benefit of the patient