Combination of contrast with stress echocardiography: A practical guide to methods and interpretation

Contrast echocardiography has an established role for enhancement of the right heart Doppler signals, the detection of intra-cardiac shunts, and most recently for left ventricular cavity opacification (LVO). The use of intravenously administered micro-bubbles to traverse the myocardial microcirculation in order to outline myocardial viability and perfusion has been the source of research studies for a number of years. Despite the enthusiasm of investigators, myocardial contrast echocardiography (MCE) has not attained routine clinical use and LV opacification during stress has been less widely adopted than the data would support. The purpose of this review is to facilitate an understanding of the involved imaging technologies that have made this technique more feasible for clinical practice, and to guide its introduction into the practice of the non-expert user

Brain Tumour Classification Using Deep Features from Structural MRI

C

Multimodal Signal Processing for Diagnosis of Cardiorespiratory Disorders

This thesis addresses the use of multimodal signal processing to develop algorithms for the automated processing of two cardiorespiratory disorders. The aim of the first application of this thesis was to reduce false alarm rate in an intensive care unit. The goal was to detect five critical arrhythmias using processing of multimodal signals including photoplethysmography, arterial blood pressure, Lead II and augmented right arm electrocardiogram (ECG). A hierarchical approach was used to process the signals as well as a custom signal processing technique for each arrhythmia type. Sleep disorders are a prevalent health issue, currently costly and inconvenient to diagnose, as they normally require an overnight hospital stay by the patient. In the second application of this project, we designed automated signal processing algorithms for the diagnosis of sleep apnoea with a main focus on the ECG signal processing. We estimated the ECG-derived respiratory (EDR) signal using different methods: QRS-complex area, principal component analysis (PCA) and kernel PCA. We proposed two algorithms (segmented PCA and approximated PCA) for EDR estimation to enable applying the PCA method to overnight recordings and rectify the computational issues and memory requirement. We compared the EDR information against the chest respiratory effort signals. The performance was evaluated using three automated machine learning algorithms of linear discriminant analysis (LDA), extreme learning machine (ELM) and support vector machine (SVM) on two databases: the MIT PhysioNet database and the St. Vincent’s database. The results showed that the QRS area method for EDR estimation combined with the LDA classifier was the highest performing method and the EDR signals contain respiratory information useful for discriminating sleep apnoea. As a final step, heart rate variability (HRV) and cardiopulmonary coupling (CPC) features were extracted and combined with the EDR features and temporal optimisation techniques were applied. The cross-validation results of the minute-by-minute apnoea classification achieved an accuracy of 89%, a sensitivity of 90%, a specificity of 88%, and an AUC of 0.95 which is comparable to the best results reported in the literature

Flow pattern analysis for magnetic resonance velocity imaging

Blood flow in the heart is highly complex. Although blood flow patterns have been investigated by both computational modelling and invasive/non-invasive imaging techniques, their evolution and intrinsic connection with cardiovascular disease has yet to be explored. Magnetic resonance (MR) velocity imaging provides a comprehensive distribution of multi-directional in vivo flow distribution so that detailed quantitative analysis of flow patterns is now possible. However, direct visualisation or quantification of vector fields is of little clinical use, especially for inter-subject or serial comparison of changes in flow patterns due to the progression of the disease or in response to therapeutic measures. In order to achieve a comprehensive and integrated description of flow in health and disease, it is necessary to characterise and model both normal and abnormal flows and their effects. To accommodate the diversity of flow patterns in relation to morphological and functional changes, we have described in this thesis an approach of detecting salient topological features prior to analytical assessment of dynamical indices of the flow patterns. To improve the accuracy of quantitative analysis of the evolution of topological flow features, it is essential to restore the original flow fields so that critical points associated with salient flow features can be more reliably detected. We propose a novel framework for the restoration, abstraction, extraction and tracking of flow features such that their dynamic indices can be accurately tracked and quantified. The restoration method is formulated as a constrained optimisation problem to remove the effects of noise and to improve the consistency of the MR velocity data. A computational scheme is derived from the First Order Lagrangian Method for solving the optimisation problem. After restoration, flow abstraction is applied to partition the entire flow field into clusters, each of which is represented by a local linear expansion of its velocity components. This process not only greatly reduces the amount of data required to encode the velocity distribution but also permits an analytical representation of the flow field from which critical points associated with salient flow features can be accurately extracted. After the critical points are extracted, phase portrait theory can be applied to separate them into attracting/repelling focuses, attracting/repelling nodes, planar vortex, or saddle. In this thesis, we have focused on vortical flow features formed in diastole. To track the movement of the vortices within a cardiac cycle, a tracking algorithm based on relaxation labelling is employed. The constraints and parameters used in the tracking algorithm are designed using the characteristics of the vortices. The proposed framework is validated with both simulated and in vivo data acquired from patients with sequential MR examination following myocardial infarction. The main contribution of the thesis is in the new vector field restoration and flow feature abstraction method proposed. They allow the accurate tracking and quantification of dynamic indices associated with salient features so that inter- and intra-subject comparisons can be more easily made. This provides further insight into the evolution of blood flow patterns and permits the establishment of links between blood flow patterns and localised genesis and progression of cardiovascular disease.Open acces

Analysis of first pass myocardial perfusion imaging with magnetic resonance

Early diagnosis and localisation of myocardial perfusion defects is an important step in the treatment of coronary artery disease. Thus far, coronary angiography is the conventional standard investigation for patients with known or suspected coronary artery disease and it provides information about the presence and location of coronary stenoses. In recent years, the development of myocardial perfusion CMR has extended the role of MR in the evaluation of ischaemic heart disease beyond the situations where there have already been gross myocardial changes such as acute infarction or scarring. The ability to non-invasively evaluate cardiac perfusion abnormalities before pathologic effects occur, or as follow-up to therapy, is important to the management of patients with coronary artery disease. Whilst limited multi-slice 2D CMR perfusion studies are gaining increased clinical usage for quantifying gross ischaemic burden, research is now directed towards complete 3D coverage of the myocardium for accurate localisation of the extent of possible defects. In 3D myocardial perfusion imaging, a complete volumetric data set has to be acquired for each cardiac cycle in order to study the first pass of the contrast bolus. This normally requires a relatively large acquisition window within each cardiac cycle to ensure a comprehensive coverage of the myocardium and reasonably high resolution of the images. With multi-slice imaging, long axis cardiac motion during this large acquisition window can cause the myocardium imaged in different cross- sections to be mis-registered, i.e., some part of the myocardium may be imaged more than twice whereas other parts may be missed out completely. This type of mis-registration is difficult to correct for by using post-processing techniques. The purpose of this thesis is to investigate techniques for tracking through plane motion during 3D myocardial perfusion imaging, and a novel technique for extracting intrinsic relationships between 3D cardiac deformation due to respiration and multiple ID real-time measurable surface intensity traces is developed. Despite the fact that these surface intensity traces can be strongly coupled with each other but poorly correlated with respiratory induced cardiac deformation, we demonstrate how they can be used to accurately predict cardiac motion through the extraction of latent variables of both the input and output of the model. The proposed method allows cross-modality reconstruction of patient specific models for dense motion field prediction, which after initial modelling can be use in real-time prospective motion tracking or correction. In CMR, new imaging sequences have significantly reduced the acquisition window whilst maintaining the desired spatial resolution. Further improvements in perfusion imaging will require the application of parallel imaging techniques or making full use of the information content of the ¿-space data. With this thesis, we have proposed RR-UNFOLD and RR-RIGR for significantly reducing the amount of data that is required to reconstruct the perfusion image series. The methods use prospective diaphragmatic navigator echoes to ensure UNFOLD and RIGR are carried out on a series of images that are spatially registered. An adaptive real-time re-binning algorithm is developed for the creation of static image sub-series related to different levels of respiratory motion. Issues concerning temporal smoothing of tracer kinetic signals and residual motion artefact are discussed, and we have provided a critical comparison of the relative merit and potential pitfalls of the two techniques. In addition to the technical and theoretical descriptions of the new methods developed, we have also provided in this thesis a detailed literature review of the current state-of-the-art in myocardial perfusion imaging and some of the key technical challenges involved. Issues concerning the basic background of myocardial ischaemia and its functional significance are discussed. Practical solutions to motion tracking during imaging, predictive motion modelling, tracer kinetic modelling, RR-UNFOLD and RR-RIGR are discussed, all with validation using patient and normal subject data to demonstrate both the strength and potential clinical value of the proposed techniques.Open acces

Development of whole-heart myocardial perfusion magnetic resonance imaging

Myocardial perfusion imaging is of huge importance for the detection of coronary artery disease (CAD), one of the leading causes of morbidity and mortality worldwide, as it can provide non-invasive detection at the early stages of the disease. Magnetic resonance imaging (MRI) can assess myocardial perfusion by capturing the rst-pass perfusion (FPP) of a gadolinium-based contrast agent (GBCA), which is now a well-established technique and compares well with other modalities. However, current MRI methods are restricted by their limited coverage of the left ventricle. Interest has therefore grown in 3D volumetric \whole-heart" FPP by MRI, although many challenges currently limit this. For this thesis, myocardial perfusion assessment in general, and 3D whole-heart FPP in particular, were reviewed in depth, alongside MRI techniques important for achieving 3D FPP. From this, a 3D `stack-of-stars' (SOS) FPP sequence was developed with the aim of addressing some current limitations. These included the breath-hold requirement during GBCA rst-pass, long 3D shot durations corrupted by cardiac motion, and a propensity for artefacts in FPP. Parallel imaging and compressed sensing were investigated for accelerating whole-heart FPP, with modi cations presented to potentially improve robustness to free-breathing. Novel sequences were developed that were capable of individually improving some current sequence limits, including spatial resolution and signal-to-noise ratio, although with some sacri ces. A nal 3D SOS FPP technique was developed and tested at stress during free-breathing examinations of CAD patients and healthy volunteers. This enabled the rst known detection of an inducible perfusion defect with a free-breathing, compressed sensing, 3D FPP sequence; however, further investigation into the diagnostic performance is required. Simulations were performed to analyse potential artefacts in 3D FPP, as well as to examine ways towards further optimisation of 3D SOS FPP. The nal chapter discusses some limitations of the work and proposes opportunities for further investigation.Open Acces

Signal acquisition and analysis of ambulatory electromyographic recordings for the assessment of sleep bruxism : A scoping review

Background Ambulatory electromyographic (EMG) devices are increasingly being used in sleep bruxism studies. EMG signal acquisition, analysis and scoring methods vary between studies. This may impact comparability of studies and the assessment of sleep bruxism in patients. Objectives (a) To provide an overview of EMG signal acquisition and analysis methods of recordings from limited-channel ambulatory EMG devices for the assessment of sleep bruxism; and (b) to provide an overview of outcome measures used in sleep bruxism literature utilising such devices. Method A scoping review of the literature was performed. Online databases PubMed and Semantics Scholar were searched for studies published in English until 7 October 2020. Data on five categories were extracted: recording hardware, recording logistics, signal acquisition, signal analysis and sleep bruxism outcomes. Results Seventy-eight studies were included, published between 1977 and 2020. Recording hardware was generally well described. Reports of participant instructions in device handling and of dealing with failed recordings were often lacking. Basic elements of signal acquisition, for example amplifications factors, impedance and bandpass settings, and signal analysis, for example rectification, signal processing and additional filtering, were underreported. Extensive variability was found for thresholds used to characterise sleep bruxism events. Sleep bruxism outcomes varied, but typically represented frequency, duration and/or intensity of masticatory muscle activity (MMA). Conclusion Adequate and standardised reporting of recording procedures is highly recommended. In future studies utilising ambulatory EMG devices, the focus may need to shift from the concept of scoring sleep bruxism events to that of scoring the whole spectrum of MMA.Peer reviewe

The design and evaluation of discrete wearable medical devices for vital signs monitoring

The observation, recording and appraisal of an individual’s vital signs, namely temperature, heart rate, blood pressure, respiratory rate and blood oxygen saturation (SpO2), are key components in the assessment of their health and wellbeing. Measurements provide valuable diagnostic data, facilitating clinical diagnosis, management and monitoring. Respiratory rate sensing is perhaps the most under-utilised of all the vital signs, being routinely assessed by observation or estimated algorithmically from respiratory-induced beat-to-beat variation in heart rate. Moreover there is an unmet need for wearable devices that can measure all or most of the vital signs. This project therefore aims to a) develop a device that can measure respiratory rate and b) develop a wearable device that can measure all or most of the vital signs. An accelerometer-based clavicular respiratory motion sensor was developed and compared with a similar thoracic motion sensor and reference using exhalatory flow. Pilot study results established that the clavicle sensor accurately tracked the reference in monitoring respiratory rate and outperformed the thoracic device. An Ear-worn Patient Monitoring System (EPMS) was also developed, providing a discrete telemonitoring device capable of rapidly measuring tympanic temperature, heart rate, SpO2 and activity level. The results of a comparative pilot study against reference instruments revealed that heart rate matched the reference for accuracy, while temperature under read (< 1°C) and SpO2 was inconsistent with poor correlation. In conclusion, both of the prototype devices require further development. The respiratory sensor would benefit from product engineering and larger scale testing to fully exploit the technology, but could find use in both hospital and community-based The design and evaluation of discrete wearable medical devices for vital signs monitoring DG Pitts ii Cranfield University monitoring. The EPMS has potential for clinical and community use, having demonstrated its capability of rapidly capturing and wirelessly transmitting vital signs readings. Further development is nevertheless required to improve the thermometer probe and resolve outstanding issues with SpO2 readings