Motion and radiation dose reduction in quantitative CT perfusion imaging of acute stroke

Computed tomography perfusion (CTP) imaging provides vital decision-support for physicians in the diagnosis and treatment planning for acute ischaemic stroke. Serial three-dimensional frames collected over 1-2 minutes during the transit of contrast agent enables visualisation of the integrity of the cerebral vasculature and underpins quantitative haemodynamic modelling to characterise stroke lesions. Notwithstanding the value of CTP imaging for stroke management, there are two areas of fundamental limitation: the increased likelihood of motion-induced corruption of the serial (4D) data compared to conventional 3D neuroimaging CT scans that complete within seconds, and the noise-limiting radiation exposure to patients to ensure that robust haemodynamic modelling can be performed. The overarching aim of this thesis was to develop methods to address these key limitations in CTP imaging, thereby improving the accuracy of image-based stroke analysis and long-term outcomes for patients. Our starting point was to characterise the prevalence, severity, temporal behaviour and dependencies of head movement during CTP imaging studies, and to quantify its clinical impact. Based on this understanding, a predictive model was established to identify patient-specific risk factors for motion. The model implicated stroke severity quantified by the National Institutes of Health Stroke Scale (NIHSS), patient age and time from stroke onset to imaging as the most important factors, all of which can be used pre-emptively to mitigate motion risk in CTP imaging. The results also showed that the accuracy of image interpretation and treatment decision making can potentially be improved for at least a fifth of CTP studies by developing retrospective intra-frame motion correction methods to augment conventional inter-frame motion correction. Although motion correction is well-recognised as an important pre-requisite to haemodynamic modelling in CTP image analysis, only inter-frame alignment is used and the impact of intra-frame corruption caused by continuous motion is ignored. We investigated the Intel RealSense D415 depth sensor, a compact, markerless and consumer-grade optical motion tracking device, for potential use in providing rapid and accurate pose estimates for continuous motion in CTP imaging. Suitability of the device was characterised with respect to thermal stability and jitter, static and dynamic six degree-of-freedom pose accuracy, and adaptability to the clinical setting. A conservative pose accuracy estimate for robotically controlled phantom motion was < 2 mm and < 1°, and for volunteer motion inside a clinical CT scanner was < 3 mm and < 1°. The device therefore shows promise for CTP motion correction but would likely need to be used in a multi-Intel D415 sensor configuration, or used to augment data-driven methods. To simultaneously reduce the radiation dose and the likelihood of motion during a CTP acquisition, we attempted to reduce the scan duration by reducing the number of frames acquired. This was achieved using a novel application of a stochastic adversarial video prediction approach trained to predict late CTP image frames from early frames, thereby avoiding the truncation of the wash-out phase of contrast agent transit. Using this approach to predict the last 18 CTP frames resulted in bolus shape characteristics deviating by < 4 ± 4% compared to the ground-truth. Average volumetric error of the hypo-perfused region was overestimated by 28.36 mL (22%) and the corresponding spatial agreement was 83% (mean dice coefficient). The results showed that predicting the last 18 frames can preserve the majority of clinical content of the images while simultaneously reducing the scan duration and radiation dose by 65% and 54.5%, respectively. The final strategy developed in this thesis was a radiation dose reduction method based on using a 3D generative adversarial network (GAN) to synthesise normal-dose CTP images from low-dose images. The method incorporated pre-processing aimed at leveraging the full spatio-temporal (4D) information of CTP data within a 3D GAN architecture. The quality of GAN-denoised images was assessed via image quality metrics, expert quality rating, and the preservation of the lesion characteristics. The results showed that prioritising temporal information in adapting 4D CTP data to the 3D GAN model resulted in better restoration of tissue haemodynamic information. The average lesion volumetric error reduced significantly by 18 - 29% and dice coefficient improved significantly by 15 - 22% at 50% and 25% of normal radiation dose using the GAN model. In summary, this thesis reports novel quantitative methods to improve our patient-specific understanding of the impact and dependencies of head motion during CTP imaging, the potential use of practical consumer-grade motion tracking devices for comprehensive motion-corrected CTP imaging, and two state-of-the-art deep learning-based approaches for radiation dose reduction in CTP imaging. The proposed methods lay the foundation for improved image-based stroke analysis and optimised CTP imaging workup and radiation dose, thereby providing more robust decision-support for physicians to improve patient outcomes

Optimised methylene-blue detection and quantification using conventional Raman spectroscopy

Theoretical thesis.Bibliography: pages 66-73.1. Introduction -- 2. Background -- 3. Instrumentation, experimental and analysis methods -- 4. Results and discussion -- 5. Conclusions and future work.Raman spectroscopy is a powerful technique based on specific molecular vibrations producing a characteristic "fingerprint spectrum" used for sample analysis. So far, the use of Raman quantitative analysis has not kept pace with its use for qualitative analysis due to some challenges, yet it has a great potential to be developed for measuring the intended property within the sample. Moreover, bulky laboratory Raman spectrometers are very pricey, complex and are designed to be as versatile as possible.The main aim of this work is thus to perform a preliminary study that will enable the future advanced quantitative analysis and design of a much simpler, yet field-transportable system, that will allow the detection and quantfication of minuscule amounts of toxins found in the environment and specific pesticides on the different plants. For that purpose, an existing Raman spectrometer is used to investigate the optimum conditions for the detection and quantification of a specific model-molecule with a well-known Raman spectrum, Methylene Blue (MB). In a systematic study, the influence of instrumental and sample-related parameters on the ability to detect very low concentrations of MB are therefore investigated. In particular, optimum excitation wavelength and power, laser spot size, and sample phase and configuration are found. Moreover, suitable methods for the calculation and minimisation of the limit of detection and quantification (LOD and LOQ) are applied under various experimental conditions. Finally, analytical models are established and the error of prediction are calculated and discussed.The presented results offer clear guidelines for the quantification study, design and development of a field-transportable Raman analysis system, which will be the subject of a future PhD work.Mode of access: World wide web1 online resource (xiii, 73 pages) illustrations (some colour

Improving pulse eddy current and ultrasonic testing stress measurement accuracy using neural network data fusion

Stress and residual stress are two crucial factors which play important roles in mechanical performance of materials, including fatigue and creep, hence measuring them is highly in demand. Pulse eddy current (PEC) and ultrasonic testing (UT) are two non-destructive tests (NDT) which are nominated to measure stresses and residual stresses by numerous scholars. However, both techniques suffer from lack of accuracy and reliability. One technique to tackle these challenges is data fusion, which has numerous approaches. This study introduces a promising one called neural network data fusion, which shows effective performance. First, stresses are simulated in an aluminium alloy 2024 specimen and then PEC and UT signals related to stresses are acquired and processed. Afterward, useful information obtained is fused using artificial neural network procedure and stresses are estimated by fused data. Finally, the accuracy of fused data are compared with PEC and UT information and results show the capability of neural network data fusion to improve stress measurement accuracy

Head movement during cerebral CT perfusion imaging of acute ischaemic stroke : characterisation and correlation with patient baseline features

Purpose: To quantitatively characterise head motion prevalence and severity and to identify patient-based risk factors for motion during cerebral CT perfusion (CTP) imaging of acute ischaemic stroke. Methods: The head motion of 80 stroke patients undergoing CTP imaging was classified retrospectively into four categories of severity. Each motion category was then characterised quantitatively based on the average head movement with respect to the first frame for all studies. Statistical testing and principal component analysis (PCA) were then used to identify and analyse the relationship between motion severity and patient baseline features. Results: 46/80 (58%) of patients showed negligible motion, 19/80 (24%) mild-to-moderate motion, and 15/80 (19%) considerable-to-extreme motion sufficient to affect diagnostic/therapeutic accuracy even with correction. The most prevalent movement was “nodding” with maximal translation/rotation in the sagittal/axial planes. There was a tendency for motion to worsen as scan proceeded and for faster motion to occur in the first 15 s. Statistical analyses showed that greater stroke severity (National Institutes of Health Stroke Scale (NIHSS)), older patient age and shorter time from stroke onset were predictive of increased head movement (p < 0.05 Kruskal-Wallis). Using PCA, the combination of NIHSS and patient age was found to be highly predictive of head movement (p < 0.001). Conclusions: Quantitative methods were developed to characterise CTP studies impacted by motion and to anticipate patients at-risk of motion. NIHSS, age, and time from stroke onset function as good predictors of motion likelihood and could potentially be used pre-emptively in CTP scanning of acute stroke

Efficient radiation dose reduction in whole-brain CT perfusion imaging using a 3D GAN: Performance and clinical feasibility

Dose reduction in cerebral CT perfusion (CTP) imaging is desirable but is accompanied by an increase in noise that can compromise the image quality and the accuracy of image-based haemodynamic modelling used for clinical decision support in acute ischaemic stroke. The few reported methods aimed at denoising low-dose CTP images lack practicality by considering only small sections of the brain or being computationally expensive. Moreover, the prediction of infarct and penumbra size and location - the chief means of decision support for treatment options - from denoised data has not been explored using these approaches. In this work, we present the first application of a 3D generative adversarial network (3D GAN) for predicting normal-dose CTP data from low-dose CTP data. Feasibility of the approach was tested using real data from 30 acute ischaemic stroke patients in conjunction with low dose simulation. The 3D GAN model was applied to 643 voxel patches extracted from two different configurations of the CTP data - frame-based and stacked. The method led to whole-brain denoised data being generated for haemodynamic modelling within 90 s. Accuracy of the method was evaluated using standard image quality metrics and the extent to which the clinical content and lesion characteristics of the denoised CTP data were preserved. Results showed an average improvement of 5.15-5.32 dB PSNR and 0.025-0.033 structural similarity index (SSIM) for CTP images and 2.66-3.95 dB PSNR and 0.036-0.067 SSIM for functional maps at 50% and 25% of normal dose using GAN model in conjunction with a stacked data regime for image synthesis. Consequently, the average lesion volumetric error reduced significantly (p-value \u3c0.05) by 18%-29% and dice coefficient improved significantly by 15%-22%. We conclude that GAN-based denoising is a promising practical approach for reducing radiation dose in CTP studies and improving lesion characterisation

Characterization of the Intel RealSense D415 Stereo Depth Camera for Motion-Corrected CT Imaging

A combination of non-contrast CT (NCCT) and CT Perfusion (CTP) imaging is the most common regime for evaluation of acute ischemic stroke patients. CTP-based image analysis is known to be compromised by patient head motion. However, there is currently no technique to compensate for intra-frame head motion during CTP acquisition. In this work, we investigated the feasibility of using the small form factor Intel RealSense D415 stereo depth camera to obtain accurate head pose estimates for intra-frame motion correction in CTP. First, we quantitatively evaluated head movement in a cohort of 72 acute stroke cases. Then we characterized the performance of the Intel D415 against ground-truth robotic motion and the clinically validated OptiTrack marker-based motion tracking system. The results showed that head motion during CTP imaging of acute stroke of patients is extremely common, with around 50% of patients moving > 5 mm and 1 deg and around 20% moving 10-100 mm and rotating 3-20 deg. The pose accuracy of the Intel for controlled robotic motion was approximately 5 mm and 2 deg. For translations and rotations, respectively. For human head motion using the OptiTrack as ground truth, the accuracy was approximately 4 mm (except for lateral motion) and 1.25 deg, respectively. Although poorer than what is needed clinically, there is a lot of potential to optimize performance and potentially achieve an accuracy consistently around 1 mm and 1 deg