Data-driven quantitative photoacoustic tomography

Spatial information about the 3D distribution of blood oxygen saturation (sO2) in vivo is of clinical interest as it encodes important physiological information about tissue health/pathology. Photoacoustic tomography (PAT) is a biomedical imaging modality that, in principle, can be used to acquire this information. Images are formed by illuminating the sample with a laser pulse where, after multiple scattering events, the optical energy is absorbed. A subsequent rise in temperature induces an increase in pressure (the photoacoustic initial pressure p0) that propagates to the sample surface as an acoustic wave. These acoustic waves are detected as pressure time series by sensor arrays and used to reconstruct images of sample’s p0 distribution. This encodes information about the sample’s absorption distribution, and can be used to estimate sO2. However, an ill-posed nonlinear inverse problem stands in the way of acquiring estimates in vivo. Current approaches to solving this problem fall short of being widely and successfully applied to in vivo tissues due to their reliance on simplifying assumptions about the tissue, prior knowledge of its optical properties, or the formulation of a forward model accurately describing image acquisition with a specific imaging system. Here, we investigate the use of data-driven approaches (deep convolutional networks) to solve this problem. Networks only require a dataset of examples to learn a mapping from PAT data to images of the sO2 distribution. We show the results of training a 3D convolutional network to estimate the 3D sO2 distribution within model tissues from 3D multiwavelength simulated images. However, acquiring a realistic training set to enable successful in vivo application is non-trivial given the challenges associated with estimating ground truth sO2 distributions and the current limitations of simulating training data. We suggest/test several methods to 1) acquire more realistic training data or 2) improve network performance in the absence of adequate quantities of realistic training data. For 1) we describe how training data may be acquired from an organ perfusion system and outline a possible design. Separately, we describe how training data may be generated synthetically using a variant of generative adversarial networks called ambientGANs. For 2), we show how the accuracy of networks trained with limited training data can be improved with self-training. We also demonstrate how the domain gap between training and test sets can be minimised with unsupervised domain adaption to improve quantification accuracy. Overall, this thesis clarifies the advantages of data-driven approaches, and suggests concrete steps towards overcoming the challenges with in vivo application

Novel approaches to radiotherapy treatment scheduling

Radiotherapy represents an important phase of treatment for a large number of cancer patients. It is essential that resources used to deliver this treatment are used efficiently. This thesis approaches the problem of scheduling treatments in a radiotherapy centre. Data about the daily intake of patients are collected and analysed. Several approaches are presented to create a schedule every day. The first presented are constructive approaches, developed due to their simplicity and low computational requirements. The approaches vary the preferred treatment start, machine utilisation reservation levels, and the frequency and number of days in advance with which schedules are created. An Integer Linear Programming (ILP) model is also presented for the problem and used in combination with approaches similar to the ones above. A generalisation of the constructive utilisation threshold approach is developed in order to vary the threshold level for each day according to how far it is from the current day. In addition, the model is evaluated for different sizes of the problem by increasing the rate of patient arrivals per day and the number of machines available. Different machine allocation policies are also evaluated. An exact method is introduced for finding a set of solutions representing the whole Pareto frontier for integer programming problems. It is combined with two robust approaches: the first considers known patients before they are ready to be scheduled, while the second considers sets of predicted patients who might arrive in the near future. A rescheduling approach is also suggested and implemented. A comparison is made amongst the best results from each group of approaches to identify the advantages and disadvantages of each. The robust approaches are found to be the best alternative of the set

Fine Art Pattern Extraction and Recognition

This is a reprint of articles from the Special Issue published online in the open access journal Journal of Imaging (ISSN 2313-433X) (available at: https://www.mdpi.com/journal/jimaging/special issues/faper2020)

Smart Sensors for Healthcare and Medical Applications

This book focuses on new sensing technologies, measurement techniques, and their applications in medicine and healthcare. Specifically, the book briefly describes the potential of smart sensors in the aforementioned applications, collecting 24 articles selected and published in the Special Issue “Smart Sensors for Healthcare and Medical Applications”. We proposed this topic, being aware of the pivotal role that smart sensors can play in the improvement of healthcare services in both acute and chronic conditions as well as in prevention for a healthy life and active aging. The articles selected in this book cover a variety of topics related to the design, validation, and application of smart sensors to healthcare

Three Grains, Two Photons and a Colourful Diamond

Exploring how light interacts with physical systems is an elegant and powerful way to unravel processes occurring at different scales, from bulk materials to single atoms. In this thesis, we employ several microscopy and spectroscopy techniques to investigate the local optoelectronic properties of metal halide perovskites, from thin films to large crystals, as well as to elucidate the physics of single-photon emitters in diamond. First, the influence of the grain size on the low-temperature phase transition in methylammonium lead iodide perovskite polycrystalline thin films is assessed by means of temperature-dependent macro- and micro-photoluminescence measurements coupled with complementary X-ray diffraction and absorption measurements. The results suggest that local strain plays a role in inhibiting thel low-temperature tetragonal-to-orthorhombic phase transition, and in the extreme case of very small grains, can almost entirely suppress it. We then unveil buried charge-carrier recombination pathways in both thin film and micro-crystal methylammonium lead halide perovskite structures through 3D photoluminescence tomography acquired using two-photon confocal microscopy. These measurements reveal that light-induced passivation approaches are primarily surface-sensitive and that even nominal single crystals still contain heterogeneous defects that impact charge-carrier recombination. We build on the two-photon mapping by developing a technique to monitor the carrier diffusion at different depths in a semiconductor by monitoring the photoluminescence as a function of distance from the two-photon-excitation spot. The technique was applied to methylammonium lead bromide crystals, revealing a spatial heterogeneity in diffusion that is not captured in macroscopic diffusion measurements. We outline a model to explain the observations by distinguishing the influences of carrier diffusion and photon reabsorption at different depths in the sample. Finally, a series of optical studies on the Silicon-Vacancy (SiV) colour-centre in diamond are reported. Coherent Population Trapping (CPT) experiments performed using electrically actuated diamond micro-cantilevers show that the ground state splitting, and therefore the strength of the electron-photon coupling limiting the coherence time in this system, depends upon mechanical strain. A route to the all-optical control of such single electron spins in diamond is then outlined. The thesis overall introduces a number of powerful techniques to shed light on the intimate relationships between carrier recombination, defects, strain and other physical properties of novel light absorbing and light emitting materials.The Cambridge Trust EPSR

Experimental Search for Signatures of Zonal Flow Physics in a Large Spherical Tokamak using Beam Emission Spectroscopy

Cross-field turbulent transport in magnetically confined plasmas is a highly effective loss mechanism of the heat and mass of fusion fuels and principally responsible for degrading the quality of confinement. Recently, an inter-dependence of turbulence and flows has been demonstrated in conventional tokamaks, including zonal flows that take energy directly out of turbulence. Data, in particular an experiment in a spherical tokamak, is scarce. This project uses a Beam Emission Spectroscopy (BES) diagnostic on the MAST spherical tokamak to measure local fluctuation of plasma density. We develop correlation and spectral analysis techniques to facilitate investigation of flow structure across the plasma profile. I first developed a velocimetry routine using cross-correlation time delay estimation (CCTDE) optimising the length of correlation functions of fluctuating MAST BES data to achieve high time resolution for zonal flow detection. Tests using surrogate data were included to improve the accuracy and reliability of the code, achieving up to a 98% successful measurement rate in broadband data. The new code’s performance was benchmarked using three realistic examples of spherical tokamak physics that previously had made velocimetry unusable. Each had an imposed mode mimicking zonal flow shown to be observable and measurable in my velocity spectra. Results from this spectroscopy identify four classes of spectra observed in real BES data. Spectra with the clearest coherent peaks are shown to exist concurrently with a long-lasting magnetic mode. A systematic test of zonal flow physics at shot times that produce velocity spectra with coherent peaks was developed. Automating and weighting scores for those tests created a framework for GAM detection; adaptable as expectations of GAM physics change. In this project it enabled the first search of a substantial MAST data set of 63 shots in which BES observed the plasma edge. No examples matched all expected physics. The observed spectrum patterns match GAM theory that accounts for safety factor and plasma rotation, but not elongation; the modal average extent of matches covers four columns (about 8 cm plasma radius) of the detector. A scan of 209 shots at L-H transition times found 5 cases with good matches to expected zonal flow physics but relatively weak velocity modes