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

Automatic analysis of transperineal ultrasound images

This thesis focuses on the automatic image analysis of transperineal ultrasound (TPUS) data, which is used to investigate female pelvic floor problems. These problems have a high prevalence, but the understanding of pelvic floor (dys-)function is limited. TPUS analysis of the pelvic floor is done manually, which is time-consuming and observer dependent. This hinders both the research into interpretation of TPUS data and its clinical use. To overcome these problems we use automatic image analysis. Currently, one of the main methods used, to analyse the TPUS is manually selecting and segmenting the slice of minimal hiatal dimensions (SMHD). In the first chapter of this thesis we show that reliable automatic segmentation of the urogenital hiatus and the puborectalis muscle in the SMHD can be successfully implemented, using deep learning. Furthermore, we show that this can also be used to successfully automate the process of selecting and segmenting the SMHD. 4D TPUS is available in the clinical practice but by the aforementioned method only provides 1D and 2D parameters. Therefore, information stored within TPUS about the volume appearance of the pelvic floor muscles and muscle functionality is not analyzed. In the third chapter of this thesis we propose a reproducible manual 3D segmentation protocol of the puborectalis muscle. The resulting manual segmentations can be used to train active appearance models and convolutional neural networks, these algorithms can be used for reliable automatic 3D segmentation. In the fifth chapter of we show that on this data it is possible to identify all subdivisions of the main pelvic floor muscle group, the levator ani muscles, on new TPUS data. In the last chapter we apply unsupervised deep learning to our data and show that this can be used for classification of the TPUS data. The segmentation results presented in this thesis are an important step to reduce the TPUS analysis time and will therefore ease the study of large populations and clinical TPUS analysis. The 3D identification and segmentation of the levator ani muscle subdivisions helps to identify if they are still intact. This is an important step to better informed clinical decision-making

Computer-Generated Holography for Areal Additive Manufacture

With a market of approximately $10B, additive manufacture (AM) is an exciting next-generation technology with the promise of significant environmental and societal impact. AM promises to help reduce emissions and waste during manufacture while improving sustainability. Widely used in applications from hip implants to jet engines, AM remains the domain of experts due to the material and thermal challenges encountered. AM in metals is dominated by Laser Powder Based Fusion (L-PBF). Powder is spread in layers 10s of microns thick and selectively melted by scanning a small laser spot heat source over the bed. Traditional AM systems have limited ability to manage or compensate for heat generated. The rapidly moving heat source spot results in high thermal cycling and is a major influence on residual stress and distortion. Mechanical limitations in the galvoscanner mean that over or under-heating is common and can lead to voids, boiling and spatter. The scale difference between the part size and the spot size means that predictive modelling is beyond the scope of even today’s best computing clusters. These factors have led to frequent inability to ensure part quality without physical prototyping and destructive testing. This thesis sets out initial research into creating a radically new AM process that uses computer-generated holography (CGH) to produce complex light patterns in a single pulse. Projecting power to the whole layer at once will mean that the thermal properties of the powders before and after writing can be factored into the processed hologram and part design. It will also significantly reduce thermal gradients and melt-pool instability. The fields of additive manufacture and computer-generated holography are introduced in Chapter 1. Chapters 2 and 3 then provide more detail on CGH and AM modelling respectively. The first deliverable, a reusable software package capable of generating holograms, is presented in Chapter 4. Algorithms developed for the project are introduced in Chapter 4.3. The first project demonstrator, an AM machine capable of printing in resins using holographic projection is discussed in Section 6.2. This shows performance comparable to modern 3D printing machines and highlights the applicability of computer-generated holography to areal processes. Section 6.3 then discusses the ongoing development of a metal powder demonstrator. As this PhD forms the first stage of a larger project, only preliminary work on the powder demonstrator is discussed. Chapter 7 then draws conclusions and outlines the way forward for future research. The thesis appendices then discuss an in-depth discussion of algorithm performances in Appendices A and B. Appendices C and D then discuss digressions into the implementation. Appendices E and F present a laser induced damage threshold (LIDT) measurement system developed. Finally, Appendices G and H provide more detail on the software developed and Appendix I gives links to additional project resources.EP/T008369/1; EP/L016567/1; EP/V055003/