Flow visualisation and quantification using high frame rate ultrasound imaging and microbubble contrast agents

Non-invasive techniques capable of visualising and quantifying blood flow in-vivo are highly desirable in studying a wide range of cardiovascular diseases. Although existing ultrasound imaging techniques have been widely used clinically to visualise and quantify blood flow, they have various limitations in terms of field of view, temporal and spatial resolution, imaging sensitivity, and beam-flow angle dependence. In this thesis, our aim is to develop flow quantification tools capable of non-invasively measuring the flow velocity, wall shear stress (WSS) as well as intraluminal mixing. Firstly, a high frame-rate ultrasound imaging velocimetry (UIV) system was developed based on tracking the speckle patterns of microbubbble contrast agents in contrast-enhanced ultrasound image sequences acquired from a plane wave imaging system. Initial evaluation of the system demonstrated the potential of the new system as a flow velocity mapping tool capable of tracking fast and dynamic flow and we improved our flow velocity measurement technique by introducing an incoherent ensemble correlation approach in the UIV tracking algorithm. Such a modified UIV technique avoids the motion artifact which could potentially affect the velocity measurement as compounded plane wave images are not coherently summed during the compounded plane wave image formation. Ultrasound flow simulations were conducted to fully evaluate our new modified-UIV technique. Together with some in-vitro experiments on physiologically relevant flow phantoms, we demonstrated the capability of our system to provide robust, angle independent, sensitive, and accurate two-dimensional velocity measurements. Secondly, as studies have revealed strong correlation between WSS and the initiation and development of atherosclerosis, we extended our UIV technique to the derive spatio-temporal wall shear rate from the velocity flow profile. The performance of the system to provide wall shear stress distributions was initially evaluated in simulation and demonstrated in-vitro using physiologically relevant flow phantoms. Thirdly, a novel approach which uses the high frame rate system and controlled microbubble destruction for flow visualisation and intraluminal mixing quantification was also proposed. Three different model vessel geometries: straight, planar curved and helical, with known effects on the flow field and mixing were evaluated against computational fluid dynamics (CFD) results. The findings indicated the technique is not only capable of visualising the secondary flows, but also able to quantify the degree of mixing in the different configurations. Finally, real time processing of the image formation and flow quantification technique were explored due to the large amount of data generated from the high frame rate ultrasound system. Initial development of a graphic processing unit (GPU) accelerated plane wave UIV system was demonstrated with the potential for real time measurements.Open Acces

Fast acoustic wave sparsely activated localization microscopy:Ultrasound super-resolution using plane-wave activation of nanodroplets

Localization-based ultrasound super-resolution imaging using microbubble contrast agents and phase-change nano-droplets has been developed to visualize microvascular structures beyond the diffraction limit. However, the long data acquisition time makes the clinical translation more challenging. In this study, fast acoustic wave sparsely activated localization microscopy (fast-AWSALM) was developed to achieve super-resolved frames with sub-second temporal resolution, by using low-boiling-point octafluoropropane nanodroplets and high frame rate plane waves for activation, destruction, as well as imaging. Fast-AWSALM was demonstrated on an in vitro microvascular phantom to super-resolve structures that could not be resolved by conventional B-mode imaging. The effects of the temperature and mechanical index on fast-AWSALM was investigated. Experimental results show that sub-wavelength micro-structures as small as 190 lm were resolvable in 200 ms with plane-wave transmission at a center frequency of 3.5 MHz and a pulse repetition frequency of 5000 Hz. This is about a 3.5 fold reduction in point spread function full-width-half-maximum compared to that measured in conventional B-mode, and two orders of magnitude faster than the recently reported AWSALM under a non-flow/very slow flow situations and other localization based methods. Just as in AWSALM, fast-AWSALM does not require flow, as is required by current microbubble based ultrasound super resolution techniques. In conclusion, this study shows the promise of fast-AWSALM, a super-resolution ultrasound technique using nanodroplets, which can generate super-resolution images in milli-seconds and does not require flow

Using Deep Learning-based Features Extracted from CT scans to Predict Outcomes in COVID-19 Patients

The COVID-19 pandemic has had a considerable impact on day-to-day life. Tackling the disease by providing the necessary resources to the affected is of paramount importance. However, estimation of the required resources is not a trivial task given the number of factors which determine the requirement. This issue can be addressed by predicting the probability that an infected patient requires Intensive Care Unit (ICU) support and the importance of each of the factors that influence it. Moreover, to assist the doctors in determining the patients at high risk of fatality, the probability of death is also calculated. For determining both the patient outcomes (ICU admission and death), a novel methodology is proposed by combining multi-modal features, extracted from Computed Tomography (CT) scans and Electronic Health Record (EHR) data. Deep learning models are leveraged to extract quantitative features from CT scans. These features combined with those directly read from the EHR database are fed into machine learning models to eventually output the probabilities of patient outcomes. This work demonstrates both the ability to apply a broad set of deep learning methods for general quantification of Chest CT scans and the ability to link these quantitative metrics to patient outcomes. The effectiveness of the proposed method is shown by testing it on an internally curated dataset, achieving a mean area under Receiver operating characteristic curve (AUC) of 0.77 on ICU admission prediction and a mean AUC of 0.73 on death prediction using the best performing classifiers