1,859 research outputs found

    Coronary motion modelling for CTA to X-ray angiography registration

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    Coronary motion modelling for CTA to X-ray angiography registration

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    A coronaria CT új alkalmazási lehetőségei

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    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

    In Vitro and Computational Analyses of Blood Flow at Aortoiliac Bifurcation for Patients with Atherosclerotic Plaque Treated with Endovascular Procedures

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    This research has developed an appropriate approach allowing for more accurate assessment of haemodynamic changes following implantation of endovascular stent graft to treat patients with occlusive aortoiliac disease. Two different endovascular techniques involving the use of different types of stent grafts were analysed and compared with regard to haemodynamics associated with these techniques. Results improved understanding of the flow characteristics of these endovascular techniques

    Medical image registration by neural networks: a regression-based registration approach

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    This thesis focuses on the development and evaluation of a registration-by-regression approach for the 3D/2D registration of coronary Computed Tomography Angiography (CTA) and X-ray angiography. This regression-based method relates image features of 2D projection images to the transformation parameters of the 3D image by a nonlinear regression. It treats registration as a regression problem, as an alternative for the traditional iterative approach that often comes with high computational costs and limited capture range. First we presented a survey of the methods with a regression-based registration approach for medical applications, as well as a summary of their main characteristics (Chapter 2). Second, we studied the registration methodology, addressing the input features and the choice of regression model (Chapter 3 and Chapter 4). For that purpose, we evaluated different options using simulated X-ray images generated from coronary artery tree models derived from 3D CTA scans. We also compared the registration-by-regression results with a method based on iterative optimization. Different image features of 2D projections and seven regression techniques were considered. The regression approach for simulated X-rays was shown to be slightly less accurate, but much more robust than the method based on an iterative optimization approach. Neural Networks obtained accurate results and showed to be robust to large initial misalignment. Third, we evaluated the registration-by-regression method using clinical data, integrating the 3D preoperative CTA of the coronary arteries with intraoperative 2D X-ray angiography images (Chapter 5). For the evaluation of the image registration, a gold standard registration was established using an exhaustive search followed by a multi-observer visual scoring procedure. The influence of preprocessing options for the simulated images and the real X-rays was studied. Several image features were also compared. The coronary registration–by-regression results were not satisfactory, resembling manual initialization accuracy. Therefore, the proposed method for this concrete problem and in its current configuration is not sufficiently accurate to be used in the clinical practice. The framework developed enables us to better understand the dependency of the proposed method on the differences between simulated and real images. The main difficulty lies in the substantial differences in appearance between the images used for training (simulated X-rays from 3D coronary models) and the actual images obtained during the intervention (real X-ray angiography). We suggest alternative solutions and recommend to evaluate the registration-by-regression approach in other applications where training data is available that has similar appearance to the eventual test data

    Advances in Cardiac Computed Tomography

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    Coronary cardiac computed tomography (CCTA) has seen rapid improvements in technology including hardware and postprocessing techniques that have contributed to its rapid growth and enabled it to remain in the forefront on diagnostic imaging. Important technological advances include wider detectors for greater coverage with less gantry rotation times, dual-source computed tomography (CT) with improved temporal resolution, dual-energy CT where simultaneous imaging at different energies to increase the contrast difference between different tissues enhances diagnostic accuracy, and emergence of spectral CT to enhance atherosclerotic imaging through nanoparticle technology. Software advances include iterative reconstruction methodologies to reduce noise and radiation doses, plaque imaging and quantification tools to assess plaque morphology and stenosis severity. Processing advances using computational fluid dynamics now enables the determination of fractional flow reserve (FFR). Another important advancement in CCTA physiologic imaging is CCTA perfusion imaging to detect ischemia and compares favorably with myocardial perfusion imaging and coronary angiographic stenosis. Finally, large registry studies and single-center studies have now been published assessing the incremental value of coronary calcium score, CT plaque severity of disease and have demonstrated that the CCTA carries strong prognostic value over and above traditional risk assessment in predicting adverse outcomes
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