198 research outputs found

    Coronary Artery Segmentation and Motion Modelling

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    Conventional coronary artery bypass surgery requires invasive sternotomy and the use of a cardiopulmonary bypass, which leads to long recovery period and has high infectious potential. Totally endoscopic coronary artery bypass (TECAB) surgery based on image guided robotic surgical approaches have been developed to allow the clinicians to conduct the bypass surgery off-pump with only three pin holes incisions in the chest cavity, through which two robotic arms and one stereo endoscopic camera are inserted. However, the restricted field of view of the stereo endoscopic images leads to possible vessel misidentification and coronary artery mis-localization. This results in 20-30% conversion rates from TECAB surgery to the conventional approach. We have constructed patient-specific 3D + time coronary artery and left ventricle motion models from preoperative 4D Computed Tomography Angiography (CTA) scans. Through temporally and spatially aligning this model with the intraoperative endoscopic views of the patient's beating heart, this work assists the surgeon to identify and locate the correct coronaries during the TECAB precedures. Thus this work has the prospect of reducing the conversion rate from TECAB to conventional coronary bypass procedures. This thesis mainly focus on designing segmentation and motion tracking methods of the coronary arteries in order to build pre-operative patient-specific motion models. Various vessel centreline extraction and lumen segmentation algorithms are presented, including intensity based approaches, geometric model matching method and morphology-based method. A probabilistic atlas of the coronary arteries is formed from a group of subjects to facilitate the vascular segmentation and registration procedures. Non-rigid registration framework based on a free-form deformation model and multi-level multi-channel large deformation diffeomorphic metric mapping are proposed to track the coronary motion. The methods are applied to 4D CTA images acquired from various groups of patients and quantitatively evaluated

    Assessment of collaterals in acute ischaemic stroke using CT imaging techniques

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    There is growing evidence that the degree of collateral circulation in acute ischaemic stroke, and in particular of leptomeningeal collaterals, is a useful imaging marker that is correlated with various baseline and outcome clinical parameters. However, methods for assessing collaterals on acute ischaemic stroke are poorly standardized at present. In the first part of this master thesis, an in-depth systematic review of methods for assessing collaterals published between 2009 and 2017 is presented. The review shows that although DSA is still used as gold standard, there has been a shift towards CT- and MR- based imaging modalities, which offer equal or higher sensitivity while being at the same time less invasive for the patient. In particular, CT seems to be a good candidate for replacing DSA as gold standard in the future and one scoring method proposed by Tan et al. has been widely adopted in recent studies. However, there has been zero or minimal progress towards a standardized method since previously published reviews. In the second part of this thesis, a retrospective study conducted at the QEUH (Glasgow) to assess the reliability of collaterals on single-phase CTA is presented. CTA does not provide time-resolved information and this may lead to mislabeling of collaterals. The phase of acquisition of the scan should be taken into account when evaluating collaterals. From 4 past clinical trials, we identified paItients with confirmed ICA or MCA occlusion. Three temporal-MIP images were reconstructed from CTP for each patient, each image corresponding to one of arterial, equilibrium and venous phase of contrast enhancement. Collateral scores were measured on both the temporal-MIP images and on single-phase CTA angiography and it was found that there was substantial agreement between the scores if the CTA was acquired in the equilibrium phase but only moderate agreement if the CTA was acquired in the arterial or venous phase. This confirms that the arterial phase, despite being the preferred phase for assessing arterial occlusion and recanalization, is not the best phase for assessing collaterals and that a combination of CTA-CTP or a CTA scan employing a time-resolved protocol should be employed when evaluating collateral status in stroke patients

    Deep learning for large-scale holographic 3D particle localization and two-photon angiography segmentation

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    Digital inline holography (DIH) is a popular imaging technique, in which an unknown 3D object can be estimated from a single 2D intensity measurement, also known as the hologram. One well known application of DIH is 3D particle localization, which has found numerous use cases in the areas of biological sample characterization and optical measurement. Traditional techniques for DIH rely on linear models of light scattering, which only account for single scattering of light and completely ignore the multiple scattering among scatterers. This assumption of linear models becomes inaccurate under high particle densities and refractive index contrasts. Incorporating multiple scattering into the estimation process has shown to improve reconstruction accuracy in numerous imaging modalities. However, existing multiple scattering solvers become computationally prohibitive for large-scale problems comprising of millions of voxels within the scattering volume. This thesis addresses this limitation by introducing computationally efficient frameworks that are able to effectively account for multiple scattering in the reconstruction process for large-scale 3D data. We demonstrate the effectiveness of the proposed schemes on a DIH setup for 3D particle localization and show that incorporating multiple scattering significantly improves the localization performance compared to traditional single scattering based approaches. First, we discuss a scheme in which multiple scattering is computed using the iterative Born approximation by dividing the 3D volume into discrete 2D slices, and computing the scattering among them. This method makes it feasible to compute multiple scattering for large volumes and significantly improves 3D particle localization compared to traditional methods. One limitation of the aforementioned method is that the multiple scattering computations were unable to converge when the sample under consideration was strongly scattering. This limitation stemmed from the method's dependence on the iterative Born approximation, which assumes the samples to be weakly scattering. This challenge is addressed in our following work, where we incorporate an alternative multiple scattering model that is able to effectively account for strongly scattering samples without irregular convergence properties. We demonstrate the improvement of the proposed method over linear scattering models for 3D particle localization, and statistically show that it is able to accurately model the hologram formation process. Following this work, we address an outstanding challenge faced by many imaging applications, related to descattering, or removal of scattering artifacts. While deep neural networks (DNNs) have become the state-of-the-art for descattering in many imaging modalities, generally multiple DNNs have to be trained for this purpose if the range of scattering artifact levels is very broad. This is because for optimal descattering performance, it has been shown that each network has to be specialized for a narrow range of scattering artifact levels. We address this challenge by presenting a novel DNN framework that is able to dynamically adapt its network parameters to the level of scattering artifacts at the input, and demonstrate optimal descattering performance without the need of training multiple DNNs. We demonstrate our technique on a DIH setup for 3D particle localization, and show that even when trained on purely simulated data, the networks is able to demosntrate improved localization on both simulated and experimental data, compared to existing methods. Finally, we consider the problem of 3D segmentation and localization of blood vessels from large-scale two-photon microscopy (2PM) angiograms of the mouse brain. 2PM is a widely adapted imaging modality for 3D neuroimaging. The localization of brain vasculature from 2PM angiograms, and its subsequent mathematical modeling, has broad implications in the fields of disease diagnosis and drug development. Vascular segmentation is generally the first step in the localization process, in which blood vessels are separated from the background. Due to the rapid decay in the 2PM signal quality with increasing imaging depth, the segmentation and localization of blood vessels from 2PM angiograms remains problematic, especially for deep vasculature. In this work, we introduce a high throughput DNN, with a semi-supervised loss function, which not only is able to localize much deeper vasculature compared to existing methods, but also does so with greater accuracy and speed

    Optogenetic Interrogation and Manipulation of Vascular Blood Flow in Cortex

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    Understanding blood flow regulatory mechanisms that correlate the regional blood flow with the level of local neuronal activity in brain is an ongoing research. Discerning different aspects of this coupling is of substantial importance in interpretation of functional imaging results, such as functional magnetic resonance imaging (fMRI), that rely on hemodynamic recordings to detect and image brain neuronal activity. Moreover, this understanding can provide insight into blood flow disorders under different pathophysiological conditions and possible treatments for such disorders. The blood regulatory mechanisms can be studied at two different; however, complementary levels: at the cellular level or at the vascular level. To fully understand the regulatory mechanisms in brain, it is essential to discern details of the coupling mechanism in each level. While, the cellular pathways of the coupling mechanism has been studied extensively in the past few decades, our understanding of the vascular response to brain activity is fairly basic. The main objective of this dissertation is to develop proper methods and instrumentation to interrogate regional cortical vasodynamics in response to local brain stimulation. For this purpose we offer the design of a custom-made OCT scanner and the necessary lens mechanisms to integrate the OCT system, fluorescence imaging, and optogenetic stimulation technologies in a single system. The design uses off-the-shelf components for a cost-effective design. The modular design of the device allows scientists to modify it in accordance with their research needs. With this multi-modal system we are able to monitor blood flow, blood velocity, and lumen diameter of pial vessels, simultaneously. Additionally, the system design provides the possibility of generating arbitrary spatial stimulation light pattern on brain. These abilities enables researchers to capture more diverse datasets and, eventually, obtain a more comprehensive picture of the vasodynamics in the brain. Along with the device we also proposed new biological experiments that are tailored to investigate the spatio-temporal properties of the vascular response to optical neurostimulation of the excitatory neurons. We demonstrate the ability of the proposed methods to investigate the effect of length and amplitude of stimulation on the temporal pattern of response in the blood flow, blood velocity, and diameter of the pial vessels. Moreover, we offer systemic approaches to investigate the spatial characteristics of the response in a vascular network. In these methods we apply arbitrary spatial patterns of optical stimulation to the cortex of transgenic mice and monitor the attributes of surrounding vessels. With this flexibility we were able to image the brain region that is influenced by a pial artery. After characterizing the spatio-temporal properties of the vascular blood flow response to optical neuro-modulation, we demonstrate the design and application of an optogenetic-based closed-loop controller mechanism in the brain. This controller, uses a proportional–integral–derivative (PID) compensator to engineer temporal optogenetic stimulation light pulses and maintain the flow of blood at various user defined levels in a set of selected arteries. Upon tuning the gain values of the PID controller we obtained a near to critically-damped response in the blood flow of selected arterial vessels

    Trends in Cerebrovascular Surgery and Interventions

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    This is an open access proceeding book of 9th European-Japanese Cerebrovascular Congress at Milan 2018. Since many experts from Europe and Japan had very important and fruitful discussion on the management of Cerebrovascular diseases, the proceeding book is very attractive for the physician and scientists of the area

    CT Scanning

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    Since its introduction in 1972, X-ray computed tomography (CT) has evolved into an essential diagnostic imaging tool for a continually increasing variety of clinical applications. The goal of this book was not simply to summarize currently available CT imaging techniques but also to provide clinical perspectives, advances in hybrid technologies, new applications other than medicine and an outlook on future developments. Major experts in this growing field contributed to this book, which is geared to radiologists, orthopedic surgeons, engineers, and clinical and basic researchers. We believe that CT scanning is an effective and essential tools in treatment planning, basic understanding of physiology, and and tackling the ever-increasing challenge of diagnosis in our society

    4D flow cardiovascular magnetic resonance consensus statement

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