369 research outputs found

    Translating computational modelling tools for clinical practice in congenital heart disease

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    Increasingly large numbers of medical centres worldwide are equipped with the means to acquire 3D images of patients by utilising magnetic resonance (MR) or computed tomography (CT) scanners. The interpretation of patient 3D image data has significant implications on clinical decision-making and treatment planning. In their raw form, MR and CT images have become critical in routine practice. However, in congenital heart disease (CHD), lesions are often anatomically and physiologically complex. In many cases, 3D imaging alone can fail to provide conclusive information for the clinical team. In the past 20-30 years, several image-derived modelling applications have shown major advancements. Tools such as computational fluid dynamics (CFD) and virtual reality (VR) have successfully demonstrated valuable uses in the management of CHD. However, due to current software limitations, these applications have remained largely isolated to research settings, and have yet to become part of clinical practice. The overall aim of this project was to explore new routes for making conventional computational modelling software more accessible for CHD clinics. The first objective was to create an automatic and fast pipeline for performing vascular CFD simulations. By leveraging machine learning, a solution was built using synthetically generated aortic anatomies, and was seen to be able to predict 3D aortic pressure and velocity flow fields with comparable accuracy to conventional CFD. The second objective was to design a virtual reality (VR) application tailored for supporting the surgical planning and teaching of CHD. The solution was a Unity-based application which included numerous specialised tools, such as mesh-editing features and online networking for group learning. Overall, the outcomes of this ongoing project showed strong indications that the integration of VR and CFD into clinical settings is possible, and has potential for extending 3D imaging and supporting the diagnosis, management and teaching of CHD

    Proceedings, MSVSCC 2012

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    Proceedings of the 6th Annual Modeling, Simulation & Visualization Student Capstone Conference held on April 19, 2012 at VMASC in Suffolk, Virginia

    Augmented Reality

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    Augmented Reality (AR) is a natural development from virtual reality (VR), which was developed several decades earlier. AR complements VR in many ways. Due to the advantages of the user being able to see both the real and virtual objects simultaneously, AR is far more intuitive, but it's not completely detached from human factors and other restrictions. AR doesn't consume as much time and effort in the applications because it's not required to construct the entire virtual scene and the environment. In this book, several new and emerging application areas of AR are presented and divided into three sections. The first section contains applications in outdoor and mobile AR, such as construction, restoration, security and surveillance. The second section deals with AR in medical, biological, and human bodies. The third and final section contains a number of new and useful applications in daily living and learning

    Deep Learning in Medical Image Analysis

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    The accelerating power of deep learning in diagnosing diseases will empower physicians and speed up decision making in clinical environments. Applications of modern medical instruments and digitalization of medical care have generated enormous amounts of medical images in recent years. In this big data arena, new deep learning methods and computational models for efficient data processing, analysis, and modeling of the generated data are crucially important for clinical applications and understanding the underlying biological process. This book presents and highlights novel algorithms, architectures, techniques, and applications of deep learning for medical image analysis

    Proceedings, MSVSCC 2011

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    Proceedings of the 5th Annual Modeling, Simulation & Visualization Student Capstone Conference held on April 14, 2011 at VMASC in Suffolk, Virginia. 186 pp

    Computational planning tools in ophthalmology: Intrastromal corneal ring surgery

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    This thesis addresses the problem of the simulation of intrastromal corneal ring segment surgery for the reduction of myopia and astigmatism, as well as the stabilisation of keratoconus (KC). This disease causes high myopia, irregular astigmatism and reduction of the patient's visual acuity to the point of blindness. Therefore there are several techniques to try to stabilise it and, thus, prevent its progression. For mild keratoconus, it is enough to use special spectacles or lenses to try to correct it, but in more advanced cases it would be necessary to use refractive surgery to try to stop the progression of the disease. The most common ones to avoid the cornea transplant (PK) are the cross-linking and the additive surgery of intrastromal rings. The current planning tools are empirical, based on the nomograms of the ring manufactures, and rely on the experience of the surgeon. Unfortunately, deterministic tools able to estimate the postsurgical visual results of this treatment do not exist. Therefore, the aim of the current thesis is to establish a realistic numerical framework to simulate intrastromal ring surgeries and estimate the mechanical and optical postsurgical outcomes. There are different types of rings depending on their angle and cross-section. There are two large groups of rings: segments which have an angle of less than 360º and those that cover the entire circumference. In the first group we find rings of triangular section such as the Keraring (Mediaphacos, BeloHorizonte, Brazil) and the Ferrara (AJL Ophthalmic Ltd, Spain) and rings of hexagonal section like the Intacs (Additional Technology Inc.). In the second group we can find the MyoRing (Dioptex, GmbH.) whose cross-section is the combination of a parabola and a circumference and the Intacs SK whose section is oval. Due to the complexity of the simulation, since multiple variables are involved, such as the type of rings, the model of the corneal material, the contact conditions between them, etc., two methodologies arised which simulated the insertion of the rings. Both are based on generating a hole in the corneal stroma, introducing the ring and closing the hole with the ring inside, establishing contact until the simulation is completed. In the first of the methodologies the hole was generated by introducing a pressure, while the second was used to an auxiliary tool, such as balloon angioplasty to introduce endovascular stents, which is displaced generating enough hole to insert the rings. As with all numerical simulations, they were not exempt of limitations, although with the first of the methodologies only circular cross--section rings were simulated and in some configurations, there was pressure inside the hole, so it was decided to focus on the second. Nevertheless, interesting conclusions were obtained: the greatest correction was obtained by placing the rings with the largest section near the apex, and whether the ring is located near the epithelium, the stresses generated in the stroma can cause the ring to extrude. With the second methodology based on a displacement control, it was possible to simulate most of the cross-sections and very interesting studies were carried out that gave conclusive results. The most important were: i) the most influential parameter is the depth of insertion; ii) considering the physiological depth of the surgery, the greater optical change is provided by the diameter of the ring, and the fine adjusted is reached with the size of the implant cross--section, i.e the diameter of the implant and the size of the cross--section are the key on regulating the refractive correction; iii) the friction between ring and stroma is important to consider it because a prediction of 2 or 3 diopters could be lost; iv) whether the KC progression is stress-driven, only MyoRing can stop its progression; v) when the covered arc of the segments is more than 320º, axisymmetric model could be used instead of tridimensional model, saving computational time; vi) the anisotropy of the model does not play an important role because the rings are much stiffer than corneal tissue; vii) the implants cannot consider such as second limbus since they act as a dynamic pivot that moves along the circadian cycles of intraocular pressure (IOP); viii) preliminary nomograms is built which allow the estimation of the optical outputs according to the size and typology of the ring and optical zone of implantation.Additionally, a characterisation of ring material was carried out by means two complementary methods: uncertainty analysis and iFEM optimisation, concluding that the manufacturing process of the rings could be the cause of the alteration of the material between the raw PMMA and the ring already prepared for its insertion.<br /
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