3,449 research outputs found

    Biomechanics

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    Biomechanics is a vast discipline within the field of Biomedical Engineering. It explores the underlying mechanics of how biological and physiological systems move. It encompasses important clinical applications to address questions related to medicine using engineering mechanics principles. Biomechanics includes interdisciplinary concepts from engineers, physicians, therapists, biologists, physicists, and mathematicians. Through their collaborative efforts, biomechanics research is ever changing and expanding, explaining new mechanisms and principles for dynamic human systems. Biomechanics is used to describe how the human body moves, walks, and breathes, in addition to how it responds to injury and rehabilitation. Advanced biomechanical modeling methods, such as inverse dynamics, finite element analysis, and musculoskeletal modeling are used to simulate and investigate human situations in regard to movement and injury. Biomechanical technologies are progressing to answer contemporary medical questions. The future of biomechanics is dependent on interdisciplinary research efforts and the education of tomorrow’s scientists

    Modelling mitral valvular dynamics–current trend and future directions

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    Dysfunction of mitral valve causes morbidity and premature mortality and remains a leading medical problem worldwide. Computational modelling aims to understand the biomechanics of human mitral valve and could lead to the development of new treatment, prevention and diagnosis of mitral valve diseases. Compared with the aortic valve, the mitral valve has been much less studied owing to its highly complex structure and strong interaction with the blood flow and the ventricles. However, the interest in mitral valve modelling is growing, and the sophistication level is increasing with the advanced development of computational technology and imaging tools. This review summarises the state-of-the-art modelling of the mitral valve, including static and dynamics models, models with fluid-structure interaction, and models with the left ventricle interaction. Challenges and future directions are also discussed

    Development of an Atlas-Based Segmentation of Cranial Nerves Using Shape-Aware Discrete Deformable Models for Neurosurgical Planning and Simulation

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    Twelve pairs of cranial nerves arise from the brain or brainstem and control our sensory functions such as vision, hearing, smell and taste as well as several motor functions to the head and neck including facial expressions and eye movement. Often, these cranial nerves are difficult to detect in MRI data, and thus represent problems in neurosurgery planning and simulation, due to their thin anatomical structure, in the face of low imaging resolution as well as image artifacts. As a result, they may be at risk in neurosurgical procedures around the skull base, which might have dire consequences such as the loss of eyesight or hearing and facial paralysis. Consequently, it is of great importance to clearly delineate cranial nerves in medical images for avoidance in the planning of neurosurgical procedures and for targeting in the treatment of cranial nerve disorders. In this research, we propose to develop a digital atlas methodology that will be used to segment the cranial nerves from patient image data. The atlas will be created from high-resolution MRI data based on a discrete deformable contour model called 1-Simplex mesh. Each of the cranial nerves will be modeled using its centerline and radius information where the centerline is estimated in a semi-automatic approach by finding a shortest path between two user-defined end points. The cranial nerve atlas is then made more robust by integrating a Statistical Shape Model so that the atlas can identify and segment nerves from images characterized by artifacts or low resolution. To the best of our knowledge, no such digital atlas methodology exists for segmenting nerves cranial nerves from MRI data. Therefore, our proposed system has important benefits to the neurosurgical community

    A Novel Method of Anatomical Data Acquisition Using the Perceptron ScanWorks V5 Scanner

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    A drastic reduction in the time available for cadaveric dissection and anatomy teaching in medical and surgical education has increased the requirement to supplement learning with the use of virtual gross anatomy training tools. In light of this, a number of known studies have approached the task of sourcing anatomical data from cadaveric material for end us in creating 3D reconstructions of the human body by producing vast image libraries of anatomical cross sections. However, the processing involved in the conversion of cross sectional images to reconstructions in 3D elicits a number of problems in creating an accurate and adequately detailed end product, suitable for educational. In this paperwe have employed a unique approach in a pilot study acquire anatomical data for end-use in 3D anatomical reconstruction by using topographical 3D laser scanning and high-resolution digital photography of all clinically relevant structures from the lower limb of a male cadaveric specimen. As a result a comprehensive high-resolution dataset, comprising 3D laser scanned data and corresponding colour photography was obtained from all clinically relevant gross anatomical structures associated with the male lower limb. This unique dataset allows a very unique and novel way to capture anatomical data and saves on the laborious processing of image segmentation common to conventional image acquisition used clinically, like CT and MRI scans. From this, it provides a dataset which can then be used across a number of commercial products dependent on the end-users requirements for development of computer training packages in medical and surgical rehearsal

    A Novel Method of Anatomical Data Acquisition Using the Perceptron ScanWorks V5 Scanner

    Get PDF
    A drastic reduction in the time available for cadaveric dissection and anatomy teaching in medical and surgical education has increased the requirement to supplement learning with the use of virtual gross anatomy training tools. In light of this, a number of known studies have approached the task of sourcing anatomical data from cadaveric material for end us in creating 3D reconstructions of the human body by producing vast image libraries of anatomical cross sections. However, the processing involved in the conversion of cross sectional images to reconstructions in 3D elicits a number of problems in creating an accurate and adequately detailed end product, suitable for educational. In this paperwe have employed a unique approach in a pilot study acquire anatomical data for end-use in 3D anatomical reconstruction by using topographical 3D laser scanning and high-resolution digital photography of all clinically relevant structures from the lower limb of a male cadaveric specimen. As a result a comprehensive high-resolution dataset, comprising 3D laser scanned data and corresponding colour photography was obtained from all clinically relevant gross anatomical structures associated with the male lower limb. This unique dataset allows a very unique and novel way to capture anatomical data and saves on the laborious processing of image segmentation common to conventional image acquisition used clinically, like CT and MRI scans. From this, it provides a dataset which can then be used across a number of commercial products dependent on the end-users requirements for development of computer training packages in medical and surgical rehearsal

    Estimation of Absolute States of Human Skeletal Muscle via Standard B-Mode Ultrasound Imaging and Deep Convolutional Neural Networks

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    Objective: To test automated in vivo estimation of active and passive skeletal muscle states using ultrasonic imaging. Background: Current technology (electromyography, dynamometry, shear wave imaging) provides no general, non-invasive method for online estimation of skeletal muscle states. Ultrasound (US) allows non-invasive imaging of muscle, yet current computational approaches have never achieved simultaneous extraction nor generalisation of independently varying, active and passive states. We use deep learning to investigate the generalizable content of 2D US muscle images. Method: US data synchronized with electromyography of the calf muscles, with measures of joint moment/angle were recorded from 32 healthy participants (7 female, ages: 27.5, 19-65). We extracted a region of interest of medial gastrocnemius and soleus using our prior developed accurate segmentation algorithm. From the segmented images, a deep convolutional neural network was trained to predict three absolute, driftfree, components of the neurobiomechanical state (activity, joint angle, joint moment) during experimentally designed, simultaneous, independent variation of passive (joint angle) and active (electromyography) inputs. Results: For all 32 held-out participants (16-fold cross-validation) the ankle joint angle, electromyography, and joint moment were estimated to accuracy 55±8%, 57±11%, and 46±9% respectively. Significance: With 2D US imaging, deep neural networks can encode in generalizable form, the activitylength-tension state relationship of these muscles. Observation only, low power, 2D US imaging can provide a new category of technology for non-invasive estimation of neural output, length and tension in skeletal muscle. This proof of principle has value for personalised muscle assessment in pain, injury, neurological conditions, neuropathies, myopathies and ageing

    Spline-based medial axis transform representation of binary images

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    Medial axes are well-known descriptors used for representing, manipulating, and compressing binary images. In this paper, we present a full pipeline for computing a stable and accurate piece-wise B-spline representation of Medial Axis Transforms (MATs) of binary images. A comprehensive evaluation on a benchmark shows that our method, called Spline-based Medial Axis Transform (SMAT), achieves very high compression ratios while keeping quality high. Compared with the regular MAT representation, the SMAT yields a much higher compression ratio at the cost of a slightly lower image quality. We illustrate our approach on a multi-scale SMAT representation, generating super-resolution images, and free-form binary image deformation

    Graphical representation of range of motion in the assessment of total hip arthroplasty : innovation report

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    Total Hip Arthroplasty (THA) is a successful technique restoring lost mobility to patients suffering from osteoarthritis. A successful THA normalises the biomechanics of the hip joint so that a patient can achieve the required range of motion to fulfil their daily activities. A recent development in THA implant technologies has been the introduction of femoral neck modularity. Assessment of femoral neck modularity has been limited by two factors. Firstly, range of motion requirement is not well understood and secondly previous clinical reports have lacked a comparison against an established successful THA implant. This study has successfully addressed these limiting factors by developing an innovative range of motion benchmark which considers the activities a person is required to undertake during their daily routine. The benchmark was developed using a systematic review of the literature focussing on hip joint biomechanics. This has been the first study to provide a clinically meaningful representation of hip joint range of motion which permits operative outcome to be directly compared against an established benchmark. Integration of the range of motion benchmark within the surgical environment was achieved by using a surgical navigation measurement device. Intra-operative measurement meant that post-operative range of motion could be simulated and compared against the requirement set by the range of motion benchmark. Distinct outcome measures have been able to be developed using this comparison which has allowed the surgical process to be assessed like a manufacturing system. Using these outcome measures, it was found that femoral neck modularity has greater potential to adjust implant orientation in comparison to non-modular femoral neck implants to achieve the ideal range of motion. However, this potential is being limited due to the current modular neck options available and because of difficulty experienced by the surgeon in assessing implant orientation. These findings have been used to develop a medical device which provides guidance to the surgeon about the THA implant orientation and thus allow them to able to make the correct modular neck choice to maximise range of motion and improve the operative outcome for the patient
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