Brain Tumour Classification Using Deep Features from Structural MRI

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Advancements and Breakthroughs in Ultrasound Imaging

Ultrasonic imaging is a powerful diagnostic tool available to medical practitioners, engineers and researchers today. Due to the relative safety, and the non-invasive nature, ultrasonic imaging has become one of the most rapidly advancing technologies. These rapid advances are directly related to the parallel advancements in electronics, computing, and transducer technology together with sophisticated signal processing techniques. This book focuses on state of the art developments in ultrasonic imaging applications and underlying technologies presented by leading practitioners and researchers from many parts of the world

Smart Sensors for Healthcare and Medical Applications

This book focuses on new sensing technologies, measurement techniques, and their applications in medicine and healthcare. Specifically, the book briefly describes the potential of smart sensors in the aforementioned applications, collecting 24 articles selected and published in the Special Issue “Smart Sensors for Healthcare and Medical Applications”. We proposed this topic, being aware of the pivotal role that smart sensors can play in the improvement of healthcare services in both acute and chronic conditions as well as in prevention for a healthy life and active aging. The articles selected in this book cover a variety of topics related to the design, validation, and application of smart sensors to healthcare

Ultrasonography for the prediction of musculoskeletal function

Ultrasound (US) imaging is a well-recognised technique for studying in vivo characteristics of a range of biological tissues due to its portability, low cost and ease of use; with recent technological advances that increased the range of choices regarding acquisition and analysis of ultrasound data available for studying dynamic behaviour of different tissues. This thesis focuses on the development and validation of methods to exploit the capabilities of ultrasound technology to investigate dynamic properties of skeletal muscles in vivo exclusively using ultrasound data. The overarching aim was to evaluate the influence of US data properties and the potential of inference algorithms for prediction of net ankle joint torques. A fully synchronised experimental setup was designed and implemented enabling collection of US, Electromyography (EMG) and dynamometer data from the Gastrocnemius medialis muscle and ankle joint of healthy adult volunteers. Participants performed three increasing complexity muscle movement tasks: passive joint rotations, isometric and isotonic contractions. Two frame rates (32 and 1000 fps) and two data precisions (8 and 16-bits) were obtained enabling analysis of the impact of US data temporal resolution and precision on joint torque predictions. Predictions of net joint torque were calculated using five data inference algorithms ranging from simple regression through to Artificial Neural Networks. Results indicated that accurate predictions of net joint torque can be obtained from the analysis of ultrasound data of one muscle. Significantly improved predictions were observed using the faster frame rate during active tasks, with 16-bit data precision and ANN further improving results in isotonic movements. The speed of muscle activation and complexity of fluctuations of the resulting net joint torques were key factors underpinning the prediction errors recorded. The properties of collected US data in combination with the movement tasks to be assessed should therefore be a key consideration in the development of experimental protocols for in vivo assessment of skeletal muscles

In vivo investigation of muscle behaviour during voluntary and electrically induced muscle contraction using B-Mode ultrasound imaging

Musculoskeletal Ultrasound Imaging (USI) is a growing field in literature. It has been proven to be a useful tool for investigating the properties of the muscle. There is growing interest in ultrasound imaging techniques for the description of skeletal muscle function, and different algorithms have been developed for this purpose. The majority of studies limit their focus on a particular area of the muscle, such as the aponeuroses, or on architectural parameters such as fiber length and pennation angle. The investigation of the entire muscle visualised on the ultrasound image may help elucidate the muscle function under normal conditions or when external factors compromise or alter the muscle function. Functional electrical stimulation (FES) is a technique based on the use of electrical current to activate skeletal muscles and facilitate their contraction. It is commonly used for strength training or in rehabilitation to accelerate or enhance the recovery of skeletal muscle's function. The ability of this technique to improve muscle performance in both healthy and diseased muscles has been demonstrated in research and in clinical practice. However the artificial nature of the muscle activation during FES leads to some important differences from the voluntary muscle contraction. Ultrasound Imaging (USI) is a potential tool that could provide objective measurements of the muscle's response during electrical stimulation, thus helping to describe and understand these differences. The aim of this study is to develop techniques based on USI that helps to elucidate the muscle function during electrical stimulation and allow comparison with voluntary contractions. Ultrasound videos were collected from healthy participants during experimental procedures involving voluntary and electrically induced muscle contractions. The videos were analysed using software algorithms for the tracking of features in US images. The resulting parameters were used as the basis for characterisation methods to describe the muscle contraction, both globally and locally. The effectiveness of the USI analysis techniques was tested and methods for extraction of physiological information from the video analysis were implemented. The regional distribution of muscle displacement during the tasks was analysed. Larger displacements were observed at deeper portions of the muscle in both the voluntary and the electrically induced contractions. Differential displacements across muscle depths were observed to differ during voluntary and FES contractions. The electric currents applied induce a uniform muscle contraction across different depths, most likely influenced by the way the electric field recruits muscle fibers. Muscle displacement was correlated to the force exerted by the muscle. Areas close to the deep aponeurosis have higher correlation with torque exerted and a second order polynomial can be used to define the relationship between displacement and torque. The relationship between the whole muscle displacement at different depths and the torque exerted was described using a polynomial surface fitting. Mechanical strain was used to map the muscle activation. Middle areas of the muscle undergo higher positive vertical strain (i.e. the muscle thickens) while deeper portions of the muscle are the most affected by shortening horizontal strain (i.e. the muscle shortens) in both voluntary and FES contractions. The muscle contractility was analysed through strain rate. A time-frequency analysis of the strain rate was performed. More frequency components and higher bandwidths were observed in FES induced contractions when compared to the voluntary. The frequency components might reflect the motor unit activation, suggesting that during FES all the motor units, firing at different rates, are recruited. In this project, USI was used as a tool to characterise the muscle behaviour locally. Regional muscle displacement and strain distribution have been used to elucidate the muscle function and quantify how different muscle areas are mechanically involved in the contraction. Strain rate was correlated with the muscle contractility and hypotheses regarding the correlation with motor units firing rate have been proposed. In conclusion a number of techniques were developed with the purpose to investigate the muscle function in normal conditions and when external factors, such as electrical stimulation, alter the natural muscle behaviour