Spinal disorders such as back pain incur a substantial societal and economic burden. Unfortunately, there is lack of understanding and treatment of these disorders are further impeded by the inability to assess spinal forces in vivo. The aim of this project is to address this challenge by developing and testing a novel image-driven approach that will assess the forces in an individual's spine in vivo by incorporating information acquired from multimodal imaging (magnetic resonance imaging (MRI) and biplane X-rays) in a subject-specific model. Magnetic resonance and biplane X-ray imaging are used to capture information about the anatomy, tissues, and motion of an individual's spine as they perform a range of everyday activities. This information is then utilised in a subject-specific computational model based on the finite element method to predict the forces in their spine. The project is also utilising novel machine learning algorithms and in vitro, six-axis mechanical testing on human, porcine and bovine samples to develop and test the modelling methods rigorously.Abstract
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Biplane video X-ray (BVX) – with models segmented from magnetic resonance imaging (MRI) – is used to directly track bones during dynamic activities. Investigating tibiofemoral kinematics helps to understand effects of disease, injury, and possible interventions. Develop a protocol and compare in-vivo kinematics during loaded dynamic activities using BVX and MRI. BVX (60 FPS) was captured whilst three healthy volunteers performed three repeats of lunge, stair ascent and gait. MRI scans were performed (Magnetom 3T Prisma, Siemens). 3D bone models of the tibia and femur were segmented (Simpleware Scan IP, Synopsis). Bone poses were obtained by manually matching bone models to X-rays (DSX Suite, C-Motion Inc.). Mean range of motion (ROM) of the contact points on the medial and lateral tibial plateau were calculated using custom MATLAB code (MathWorks). Results were filtered using an adaptive low pass Butterworth filter (Frequency range: 5-29Hz). Gait and Stair ascent activities from one participant's data showed increased ROM for medial-lateral (ML) translation in the medial compartment but decreased ROM in anterior-posterior (AP) translation when comparing against the same translations on the lateral compartment of the tibial plateau. Lunge activity showed increased ROM for both ML and AP translation in the medial compartment when compared with the lateral compartment. These results highlight the variability in condylar translations between different activities. Understanding healthy in-vivo kinematics across different activities allows the determination of suitable activities to best investigate the kinematic changes due to disease or injury and assess the efficacy of different interventions. Acknowledgements: This research was supported by the Engineering and Physical Sciences Research Council (EPSRC) doctoral training grant (EP/T517951/1).
Current tools to measure pain are broadly subjective impressions of the impact of the nociceptive impulse felt by the patient. A direct measure of nociception may offer a more objective indicator. Specifically, movement-induced physiological responses to nociception may offer a useful way to monitor knee OA. In this proof-of-concept study, we evaluated whether integrated biomechanical and physiological sensor datasets could display linked and quantifiable information to a nociceptive stimulus. Following ethical approval, we applied a quantified thermal pain stimulus to a volunteer during stationary standing in a gait lab setting. An inertial measurement unit (IMU) and an electromyography (EMG) lower body marker set were tested and integrated with ground reaction force (GRF) data collection. Galvanic skin response electrodes and skin thermal sensors were manually timestamp linked to the integrated system.Abstract
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