Femtosecond Cr⁴⁺: forsterite laser for applications in telecommunications and biophotonics

In this thesis, the development of a femtosecond Cr⁴⁺:forsterite solid-state laser is described where the mode-locking procedure was initiated using two novel saturable absorbers. One was a GaInNAs quantum-well device and the other a quantum-dot-based saturable absorber. These devices had not previously been exploited for the generation of femtosecond pulses from a solid-state laser but in the course of this project, successful mode-locked laser operation in the femtosecond domain was demonstrated for both devices. When the GaInNAs device was incorporated in the Cr⁴⁺:forsterite laser, transform-limited pulses with durations as short as 62fs were obtained. The performance of this femtosecond laser was significantly superior to that for previous quantum-well based saturable absorbers in the 1300nm spectral region. The dynamics of the device were investigated with the aim of refining subsequent devices and to explore the potential to grow future devices for use at longer wavelengths. At the outset of my research work quantum-dot based saturable absorbers had not be used for the mode locking of solid-state lasers in the femtosecond regime. The work presented in this thesis showed that quantum-dot structures could be exploited very effectively for this purpose. This was initially achieved with the quantum-dot element being inclined at an off-normal incidence within the cavity but experimental assessment together with further development of the device allowed for implementation at normal incidence. Reliable operation of the femtosecond laser was demonstrated very convincingly where transform-limited pulses of 160fs duration were generated. Having developed practical femtosecond Cr⁴⁺:forsterite lasers, the final part of the project research was directed towards exemplar applications for a laser operating in the 1300nm spectral region. These were biophotonics experiments in which assessments of both deep tissue penetration and two-photon chromosome cutting were undertaken. This work confirmed the suitability of the 1300nm laser radiation for propagation through substantial thicknesses of biological tissue (~15cm). The demonstration of highly localised two-photon cutting of Muntjac deer chromosomes also represented a novel result because single-photon absorption could be avoided effectively and the temporal broadening of the femtosecond pulses in the delivery optics arising from group velocity dispersion around 1300nm was minimal

Modelling the Positional and Orientation Sensitivity of Inductively Coupled Sensors for Industrial IoT Applications

As the Internet of Things (IoT) sector continually expands there is a growing abstraction between physical objects and the data associated with them. At the same time, emerging Industrial-IoT applications rely upon diverse and robust hardware sensing interfaces in order to deliver high quality data. In this paper, the fundamental limitations associated with inductive proximity sensing interfaces are considered in terms of positional and orientation sensitivity and a triaxial approach is proposed that enables arbitrary source-sensor positioning. A matrix transformation model based on the field coupling equations is applied to a number of candidate configurations assessed according their relative source-sensor coverage and graphical visualization of coupling quality. Particular attention is paid to the recombination of tri-sensor outputs involving direct-summation, rectifysummation, best-coil and root-mean-square methods. Of these, the rectify-summation method was observed to provide favorable performance, exceeding 70% coverage for practical cases, thus far exceeding that of traditional co-planar arrangements

Understanding the management of doctoral studies in Australia as risk management

This paper discusses and analyses theoretical explanations of risk and risk management in terms of the management of doctoral studies. It deals with the ways in which Government policy, together with contemporary approaches to the bureaucratisation of risk management and the development and imposition of rationalities of risk, are shaping the practices of universities concerning the selection, supervision, support and assessment of doctoral candidates. In particular, the impact of the Research Training Scheme on doctoral studies is discussed as a particular context in which the institutionalisation of risk management occurs.<br /

An automated workflow for patient-specific quality control of contour propagation

Contour propagation is an essential component of adaptive radiotherapy, but current contour propagation algorithms are not yet sufficiently accurate to be used without manual supervision. Manual review of propagated contours is time-consuming, making routine implementation of real-time adaptive radiotherapy unrealistic. Automated methods of monitoring the performance of contour propagation algorithms are therefore required. We have developed an automated workflow for patient-specific quality control of contour propagation and validated it on a cohort of head and neck patients, on which parotids were outlined by two observers. Two types of error were simulated-mislabelling of contours and introducing noise in the scans before propagation. The ability of the workflow to correctly predict the occurrence of errors was tested, taking both sets of observer contours as ground truth, using receiver operator characteristic analysis. The area under the curve was 0.90 and 0.85 for the observers, indicating good ability to predict the occurrence of errors. This tool could potentially be used to identify propagated contours that are likely to be incorrect, acting as a flag for manual review of these contours. This would make contour propagation more efficient, facilitating the routine implementation of adaptive radiotherap

Optimising use of 4D-CT phase information for radiomics analysis in lung cancer patients treated with stereotactic body radiotherapy

From IOP Publishing via Jisc Publications RouterHistory: received 2021-03-17, oa-requested 2021-04-07, accepted 2021-04-21, epub 2021-05-24, open-access 2021-05-24, ppub 2021-06-07Publication status: PublishedFunder: Cancer Research UK; doi: https://doi.org/10.13039/501100000289; Grant(s): C147/A25254Abstract: Purpose. 4D-CT is routine imaging for lung cancer patients treated with stereotactic body radiotherapy. No studies have investigated optimal 4D phase selection for radiomics. We aim to determine how phase data should be used to identify prognostic biomarkers for distant failure, and test whether stability assessment is required. A phase selection approach will be developed to aid studies with different 4D protocols and account for patient differences. Methods. 186 features were extracted from the tumour and peritumour on all phases for 258 patients. Feature values were selected from phase features using four methods: (A) mean across phases, (B) median across phases, (C) 50% phase, and (D) the most stable phase (closest in value to two neighbours), coined personalised selection. Four levels of stability assessment were also analysed, with inclusion of: (1) all features, (2) stable features across all phases, (3) stable features across phase and neighbour phases, and (4) features averaged over neighbour phases. Clinical-radiomics models were built for twelve combinations of feature type and assessment method. Model performance was assessed by concordance index (c-index) and fraction of new information from radiomic features. Results. The most stable phase spanned the whole range but was most often near exhale. All radiomic signatures provided new information for distant failure prediction. The personalised model had the highest c-index (0.77), and 58% of new information was provided by radiomic features when no stability assessment was performed. Conclusion. The most stable phase varies per-patient and selecting this improves model performance compared to standard methods. We advise the single most stable phase should be determined by minimising feature differences to neighbour phases. Stability assessment over all phases decreases performance by excessively removing features. Instead, averaging of neighbour phases should be used when stability is of concern. The models suggest that higher peritumoural intensity predicts distant failure

Early prediction of tumour-response to radiotherapy in NSCLC patients

From IOP Publishing via Jisc Publications RouterHistory: received 2021-03-19, oa-requested 2021-08-13, rev-recd 2021-09-23, accepted 2021-10-13, epub 2021-11-05, open-access 2021-11-05, ppub 2021-11-21Publication status: PublishedAbstract: Objective. In this study we developed an automatic method to predict tumour volume and shape in weeks 3 and 4 of radiotherapy (RT), using cone-beam computed tomography (CBCT) scans acquired up to week 2, allowing identification of large tumour changes. Approach. 240 non-small cell lung cancer (NSCLC) patients, treated with 55 Gy in 20 fractions, were collected. CBCTs were rigidly registered to the planning CT. Intensity values were extracted in each voxel of the planning target volume across all CBCT images from days 1, 2, 3, 7 and 14. For each patient and in each voxel, four regression models were fitted to voxel intensity; applying linear, Gaussian, quadratic and cubic methods. These models predicted the intensity value for each voxel in weeks 3 and 4, and the tumour volume found by thresholding. Each model was evaluated by computing the root mean square error in pixel value and structural similarity index metric (SSIM) for all patients. Finally, the sensitivity and specificity to predict a 30% change in volume were calculated for each model. Main results. The linear, Gaussian, quadratic and cubic models achieved a comparable similarity score, the average SSIM for all patients was 0.94, 0.94, 0.90, 0.83 in week 3, respectively. At week 3, a sensitivity of 84%, 53%, 90% and 88%, and specificity of 99%, 100%, 91% and 42% were observed for the linear, Gaussian, quadratic and cubic models respectively. Overall, the linear model performed best at predicting those patients that will benefit from RT adaptation. The linear model identified 21% and 23% of patients in our cohort with more than 30% tumour volume reduction to benefit from treatment adaptation in weeks 3 and 4 respectively. Significance. We have shown that it is feasible to predict the shape and volume of NSCLC tumours from routine CBCTs and effectively identify patients who will respond to treatment early

Novel methodology to assess the effect of contouring variation on treatment outcome

From Wiley via Jisc Publications RouterHistory: received 2020-11-25, accepted 2021-03-22, rev-recd 2021-03-22, pub-electronic 2021-04-24, pub-print 2021-06Article version: VoRPublication status: PublishedFunder: NIHR Manchester Biomedical Research Centre. Prostate Cancer UK; Grant(s): RIA15‐ST2‐031Funder: Cancer Research UK via funding to the Cancer Research Manchester Centre; Grant(s): C147/A25254Funder: Cancer Research UK; Grant(s): C8225/A21133Purpose: Contouring variation is one of the largest systematic uncertainties in radiotherapy, yet its effect on clinical outcome has never been analyzed quantitatively. We propose a novel, robust methodology to locally quantify target contour variation in a large patient cohort and find where this variation correlates with treatment outcome. We demonstrate its use on biochemical recurrence for prostate cancer patients. Method: We propose to compare each patient’s target contours to a consistent and unbiased reference. This reference was created by auto‐contouring each patient’s target using an externally trained deep learning algorithm. Local contour deviation measured from the reference to the manual contour was projected to a common frame of reference, creating contour deviation maps for each patient. By stacking the contour deviation maps, time to event was modeled pixel‐wise using a multivariate Cox proportional hazards model (CPHM). Hazard ratio (HR) maps for each covariate were created, and regions of significance found using cluster‐based permutation testing on the z‐statistics. This methodology was applied to clinical target volume (CTV) contours, containing only the prostate gland, from 232 intermediate‐ and high‐risk prostate cancer patients. The reference contours were created using ADMIRE® v3.4 (Elekta AB, Sweden). Local contour deviations were computed in a spherical coordinate frame, where differences between reference and clinical contours were projected in a 2D map corresponding to sampling across the coronal and transverse angles every 3°. Time to biochemical recurrence was modeled using the pixel‐wise CPHM analysis accounting for contour deviation, patient age, Gleason score, and treated CTV volume. Results: We successfully applied the proposed methodology to a large patient cohort containing data from 232 patients. In this patient cohort, our analysis highlighted regions where the contour variation was related to biochemical recurrence, producing expected and unexpected results: (a) the interface between prostate–bladder and prostate–seminal vesicle interfaces where increase in the manual contour relative to the reference was related to a reduction of risk of biochemical recurrence by 4–8% per mm and (b) the prostate's right, anterior and posterior regions where an increase in the manual contour relative to the reference contours was related to an increase in risk of biochemical recurrence by 8–24% per mm. Conclusion: We proposed and successfully applied a novel methodology to explore the correlation between contour variation and treatment outcome. We analyzed the effect of contour deviation of the prostate CTV on biochemical recurrence for a cohort of more than 200 prostate cancer patients while taking basic clinical variables into account. Applying this methodology to a larger dataset including additional clinically important covariates and externally validating it can more robustly identify regions where contour variation directly relates to treatment outcome. For example, in the prostate case we use to demonstrate our novel methodology, external validation will help confirm or reject the counter‐intuitive results (larger contours resulting in higher risk). Ultimately, the results of this methodology could inform contouring protocols based on actual patient outcomes