153 research outputs found

    Novel mesh generation method for accurate image-based computational modelling of blood vessels

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    Coronary atherosclerosis:biomechanics and imaging

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    Coronary atherosclerosis:biomechanics and imaging

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    Zein-based smart coatings for drug-eluting stents: investigations via static and microfluidic approaches

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    Coronary heart disease is currently responsible for a significant percentage of global mortality in developed and developing nations alike. This occurrence takes place despite the advancement in medical technology and improved treatment options, such as stenting procedures. Due to complications with restenosis and stent thrombosis that are associated with current commercial stents, there has been a growing interest in stent research and development in order to eradicate the causes of such clinical events. The selection of an antioxidant, non-thrombogenic coating has been a major obstacle to the development of drug-eluting stents (DES), and, to date, a truly biocompatible stent platform remains elusive. Moreover, there is a need to assess stent coatings within an in vitro platform prior to in vivo and clinical studies in order to minimize adverse effects. Even if considerable progress has been made over the last two decades in the development of flow chambers to monitor and study thrombus formation outside of the circulation, blood-material interactions are still little investigated under static and dynamic modes. In order to avoid some of the drawbacks of synthetic polymers, such as their undesirable degradation products, long-lasting presence, or potential biocompatibility issues, the aim of this PhD thesis was to investigate zein as a green and abundant plant-derived protein as a coating material for DES applications. This study aimed to understand the potential uses of zein as a controlled release matrix for drug delivery systems, in addition to developing a microfluidic platform to assess the behavior and hemocompatibility of the proposed plant-based stent coatings under flow conditions

    A method to improve the computational efficiency of the Chan-Vese model for the segmentation of ultrasound images

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    Purpose Advanced image segmentation techniques like the Chan-Vese (CV) models transform the segmentation problem into a minimization problem which is then solved using the gradient descent (GD) optimization algorithm. This study explores whether the computational efficiency of CV can be improved when GD is replaced by a different optimization method. Methods Two GD variants from the literature (Nesterov accelerated, Barzilai-Borwein) and a newly developed hybrid variant of GD were used to improve the computational efficiency of CV by making GD insensitive to local minima. One more variant of GD from the literature (projected GD) was used to address the issue of maintaining the constraint on boundary evolution in CV which also increases computational cost. A novel modified projected GD (Barzilai-Borwein projected GD) was also used to overcome both problems at the same time. The effect of optimization method selection on processing time and the quality of the output was assessed for 25 musculoskeletal ultrasound images (five anatomical areas). Results The Barzilai-Borwein projected GD method was able to significantly reduce computational time (average(±std.dev.) reduction 95.82 % (±3.60 %)) with the least structural distortion in the delineated output relative to the conventional GD (average(±std.dev.) structural similarity index: 0.91(±0.06)). Conclusion The use of an appropriate optimization method can substantially improve the computational efficiency of CV models. This can open the way for real-time delimitation of anatomical structures to aid the interpretation of clinical ultrasound. Further research on the effect of the optimization method on the accuracy of segmentation is needed

    Aortic dissection: simulation tools for disease management and understanding

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    Aortic dissection is a severe cardiovascular pathology in which a tear in the intimal layer of the aortic wall allows blood to flow between the vessel wall layers, forming a 'false lumen'. In type-B aortic dissections, those involving only the descending aorta, the decision to medically manage or surgically intervene is not clear and is highly dependent on the patient. In addition to clinical imaging data, clinicians would benefit greatly from additional physiological data to inform their decision-making process. Computational fluid dynamics methods show promise for providing data on haemodynamic parameters in cardiovascular diseases, which cannot otherwise be predicted or safely measured. The assumptions made in the development of such models have a considerable impact on the accuracy of the results, and thus require careful investigation. Application of appropriate boundary conditions is a challenging but critical component of such models. In the present study, imaging data and invasive pressure measurements from a patient with a type-B aortic dissection were used to assist numerical modelling of the haemodynamics in a dissected aorta. A technique for tuning parameters for coupled Windkessel models was developed and evaluated. Two virtual treatments were modelled and analysed using the developed dynamic boundary conditions. Finally, the influence of wall motion was considered, of which the intimal flap that separates the false lumen from the true lumen, is of particular interest. The present results indicate that dynamic boundary conditions are necessary in order to achieve physiologically meaningful flows and pressures at the boundaries, and hence within the dissected aorta. Additionally, wall motion is of particular importance in the closed regions of the false lumen, wherein rigid wall simulations fail to capture the motion of the fluid due to the elasticity of the vessel wall and intimal flap
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