6,526 research outputs found

    Radiotherapy dosimetry with ultrasound contrast agents

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    Life on a scale:Deep brain stimulation in anorexia nervosa

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    Anorexia nervosa (AN) is a severe psychiatric disorder marked by low body weight, body image abnormalities, and anxiety and shows elevated rates of morbidity, comorbidity and mortality. Given the limited availability of evidence-based treatments, there is an urgent need to investigate new therapeutic options that are informed by the disorder’s underlying neurobiological mechanisms. This thesis represents the first study in the Netherlands and one of a limited number globally to evaluate the efficacy, safety, and tolerability of deep brain stimulation (DBS) in the treatment of AN. DBS has the advantage of being both reversible and adjustable. Beyond assessing the primary impact of DBS on body weight, psychological parameters, and quality of life, this research is novel in its comprehensive approach. We integrated evaluations of efficacy with critical examinations of the functional impact of DBS in AN, including fMRI, electroencephalography EEG, as well as endocrinological and metabolic assessments. Furthermore, this work situates AN within a broader theoretical framework, specifically focusing on its manifestation as a form of self-destructive behavior. Finally, we reflect on the practical, ethical and philosophical aspects of conducting an experimental, invasive procedure in a vulnerable patient group. This thesis deepens our understanding of the neurobiological underpinnings of AN and paves the way for future research and potential clinical applications of DBS in the management of severe and enduring AN

    Autonomous Radar-based Gait Monitoring System

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    Features related to gait are fundamental metrics of human motion [1]. Human gait has been shown to be a valuable and feasible clinical marker to determine the risk of physical and mental functional decline [2], [3]. Technologies that detect changes in people’s gait patterns, especially older adults, could support the detection, evaluation, and monitoring of parameters related to changes in mobility, cognition, and frailty. Gait assessment has the potential to be leveraged as a clinical measurement as it is not limited to a specific health care discipline and is a consistent and sensitive test [4]. A wireless technology that uses electromagnetic waves (i.e., radar) to continually measure gait parameters at home or in a hospital without a clinician’s participation has been proposed as a suitable solution [3], [5]. This approach is based on the interaction between electromagnetic waves with humans and how their bodies impact the surrounding and scattered wireless signals. Since this approach uses wireless waves, people do not need to wear or carry a device on their bodies. Additionally, an electromagnetic wave wireless sensor has no privacy issues because there is no video-based camera. This thesis presents the design and testing of a radar-based contactless system that can monitor people’s gait patterns and recognize their activities in a range of indoor environments frequently and accurately. In this thesis, the use of commercially available radars for gait monitoring is investigated, which offers opportunities to implement unobtrusive and contactless gait monitoring and activity recognition. A novel fast and easy-to-implement gait extraction algorithm that enables an individual’s spatiotemporal gait parameter extraction at each gait cycle using a single FMCW (Frequency Modulated Continuous Wave) radar is proposed. The proposed system detects changes in gait that may be the signs of changes in mobility, cognition, and frailty, particularly for older adults in individual’s homes, retirement homes and long-term care facilities retirement homes. One of the straightforward applications for gait monitoring using radars is in corridors and hallways, which are commonly available in most residential homes, retirement, and long-term care homes. However, walls in the hallway have a strong “clutter” impact, creating multipath due to the wide beam of commercially available radar antennas. The multipath reflections could result in an inaccurate gait measurement because gait extraction algorithms employ the assumption that the maximum reflected signals come from the torso of the walking person (rather than indirect reflections or multipath) [6]. To address the challenges of hallway gait monitoring, two approaches were used: (1) a novel signal processing method and (2) modifying the radar antenna using a hyperbolic lens. For the first approach, a novel algorithm based on radar signal processing, unsupervised learning, and a subject detection, association and tracking method is proposed. This proposed algorithm could be paired with any type of multiple-input multiple-output (MIMO) or single-input multiple-output (SIMO) FMCW radar to capture human gait in a highly cluttered environment without needing radar antenna alteration. The algorithm functionality was validated by capturing spatiotemporal gait values (e.g., speed, step points, step time, step length, and step count) of people walking in a hallway. The preliminary results demonstrate the promising potential of the algorithm to accurately monitor gait in hallways, which increases opportunities for its applications in institutional and home environments. For the second approach, an in-package hyperbola-based lens antenna was designed that can be integrated with a radar module package empowered by the fast and easy-to-implement gait extraction method. The system functionality was successfully validated by capturing the spatiotemporal gait values of people walking in a hallway filled with metallic cabinets. The results achieved in this work pave the way to explore the use of stand-alone radar-based sensors in long hallways for day-to-day long-term monitoring of gait parameters of older adults or other populations. The possibility of the coexistence of multiple walking subjects is high, especially in long-term care facilities where other people, including older adults, might need assistance during walking. GaitRite and wearables are not able to assess multiple people’s gait at the same time using only one device [7], [8]. In this thesis, a novel radar-based algorithm is proposed that is capable of tracking multiple people or extracting walking speed of a participant with the coexistence of other people. To address the problem of tracking and monitoring multiple walking people in a cluttered environment, a novel iterative framework based on unsupervised learning and advanced signal processing was developed and tested to analyze the reflected radio signals and extract walking movements and trajectories in a hallway environment. Advanced algorithms were developed to remove multipath effects or ghosts created due to the interaction between walking subjects and stationary objects, to identify and separate reflected signals of two participants walking at a close distance, and to track multiple subjects over time. This method allows the extraction of walking speed in multiple closely-spaced subjects simultaneously, which is distinct from previous approaches where the speed of only one subject was obtained. The proposed multiple-people gait monitoring was assessed with 22 participants who participated in a bedrest (BR) study conducted at McGill University Health Centre (MUHC). The system functionality also was assessed for in-home applications. In this regard, a cloud-based system is proposed for non-contact, real-time recognition and monitoring of physical activities and walking periods within a domestic environment. The proposed system employs standalone Internet of Things (IoT)-based millimeter wave radar devices and deep learning models to enable autonomous, free-living activity recognition and gait analysis. Range-Doppler maps generated from a dataset of real-life in-home activities are used to train deep learning models. The performance of several deep learning models was evaluated based on accuracy and prediction time, with the gated recurrent network (GRU) model selected for real-time deployment due to its balance of speed and accuracy compared to 2D Convolutional Neural Network Long Short-Term Memory (2D-CNNLSTM) and Long Short-Term Memory (LSTM) models. In addition to recognizing and differentiating various activities and walking periods, the system also records the subject’s activity level over time, washroom use frequency, sleep/sedentary/active/out-of-home durations, current state, and gait parameters. Importantly, the system maintains privacy by not requiring the subject to wear or carry any additional devices

    An investigation into mild traumatic brain injury identification, management, and mitigation

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    Concussion is classified as a mild traumatic brain injury which can be induced by biomechanical forces such as a physical impact to the head or body, which results in a transient neurological disturbance without obvious structural brain damage. Immediate access to tools that can identify, diagnosis and manage concussion are wide ranging and can lack consistency in application. It is well documented that there are frequent incidences of concussion across amateur and professional sport such as popular contact sports like rugby union. A primary aim of this thesis was to establish the current modalities of ‘pitch side’ concussion management, identification, and diagnosis across amateur and professional sporting populations. Furthermore, the research sought to understand existing concussion management and concussion experiences by means of recording the player’s experiences and perceptions (retired professional rugby union players). These qualitative studies sought to gain insights into concussion experiences, the language used to discuss concussion and the duty of care which medical staff, coaching personnel, and club owners have towards professional rugby players in their employment. In addition, possible interventions to reduce the incidence of concussion in amateur and professional sports were investigated. These included a ‘proof of concept’ using inertial measurement units and a smartphone application, a tackle technique coaching app for amateur sports. Other research data investigating the use of neurological function data and neuromuscular fatigue in current professional rugby players as a novel means of monitoring injury risk were included in this research theme. The findings of these studies suggest that there is an established head injury assessment process for professional sports. However, in amateur sport settings, this is not the existing practice and may expose amateur players to an increased risk of post-concussion syndrome or early retirement. Many past professional rugby union players stated that they did not know the effects of cumulative repetitive head impacts. They discussed how they minimised and ignored repeated concussions due to peer pressure or pressure from coaches or their own internal pressures of maintaining a livelihood. These data suggest that players believed that strong willed medical staff, immutable to pressures from coaching staff or even athletes themselves, were essential for player welfare and that club owners have a long-term duty of care to retired professional rugby union players. However, there are anecdotal methods suggested to reduce concussion incidence. For example, neck strengthening techniques to mitigate against collision impacts. There is, no longitudinal evidence to suggest that neck strength can reduce the impacts of concussion in adult populations . Additionally, other factors such as lowering the tackle height in the professional and amateur game is currently being investigated as a mitigating factor to reduce head injury risk. The final theme of the thesis investigated possible methods to reduce injury incidence in amateur and professional athletes. The novel tackle technique platform could assist inexperienced amateur coaches on how to coach effective tackle technique to youth players. The findings from the neurological function data suggests that this may be an alternative way for coaches to assess and gather fatigue data on professional rugby union players alongside additional subjective measures and neuromuscular function data. Recently, the awareness of concussion as an injury and the recognition of concussion in many sports settings has improved. These incremental improvements have led to increased discussion regarding possible measures to mitigate the effects of concussion. There are many additional procedures to be implemented before a comprehensive concussion management is universally available, particularly in amateur and community sports. These necessary processes could be technological advances (e.g., using smart phone technology) for parents and amateur coaches to assist in the early identification of concussion or evidence-based concussion reduction strategies

    Deep Multimodality Image-Guided System for Assisting Neurosurgery

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    Intrakranielle Hirntumoren gehören zu den zehn hĂ€ufigsten bösartigen Krebsarten und sind fĂŒr eine erhebliche MorbiditĂ€t und MortalitĂ€t verantwortlich. Die grĂ¶ĂŸte histologische Kategorie der primĂ€ren Hirntumoren sind die Gliome, die ein Ă€ußerst heterogenes Erschei-nungsbild aufweisen und radiologisch schwer von anderen HirnlĂ€sionen zu unterscheiden sind. Die Neurochirurgie ist meist die Standardbehandlung fĂŒr neu diagnostizierte Gliom-Patienten und kann von einer Strahlentherapie und einer adjuvanten Temozolomid-Chemotherapie gefolgt werden. Die Hirntumorchirurgie steht jedoch vor großen Herausforderungen, wenn es darum geht, eine maximale Tumorentfernung zu erreichen und gleichzeitig postoperative neurologische Defizite zu vermeiden. Zwei dieser neurochirurgischen Herausforderungen werden im Folgenden vorgestellt. Erstens ist die manuelle Abgrenzung des Glioms einschließlich seiner Unterregionen aufgrund seines infiltrativen Charakters und des Vorhandenseins einer heterogenen KontrastverstĂ€rkung schwierig. Zweitens verformt das Gehirn seine Form ̶ die so genannte "Hirnverschiebung" ̶ als Reaktion auf chirurgische Manipulationen, Schwellungen durch osmotische Medikamente und AnĂ€sthesie, was den Nutzen prĂ€opera-tiver Bilddaten fĂŒr die Steuerung des Eingriffs einschrĂ€nkt. Bildgesteuerte Systeme bieten Ärzten einen unschĂ€tzbaren Einblick in anatomische oder pathologische Ziele auf der Grundlage moderner BildgebungsmodalitĂ€ten wie Magnetreso-nanztomographie (MRT) und Ultraschall (US). Bei den bildgesteuerten Instrumenten handelt es sich hauptsĂ€chlich um computergestĂŒtzte Systeme, die mit Hilfe von Computer-Vision-Methoden die DurchfĂŒhrung perioperativer chirurgischer Eingriffe erleichtern. Die Chirurgen mĂŒssen jedoch immer noch den Operationsplan aus prĂ€operativen Bildern gedanklich mit Echtzeitinformationen zusammenfĂŒhren, wĂ€hrend sie die chirurgischen Instrumente im Körper manipulieren und die Zielerreichung ĂŒberwachen. Daher war die Notwendigkeit einer BildfĂŒhrung wĂ€hrend neurochirurgischer Eingriffe schon immer ein wichtiges Anliegen der Ärzte. Ziel dieser Forschungsarbeit ist die Entwicklung eines neuartigen Systems fĂŒr die peri-operative bildgefĂŒhrte Neurochirurgie (IGN), nĂ€mlich DeepIGN, mit dem die erwarteten Ergebnisse der Hirntumorchirurgie erzielt werden können, wodurch die GesamtĂŒberle-bensrate maximiert und die postoperative neurologische MorbiditĂ€t minimiert wird. Im Rahmen dieser Arbeit werden zunĂ€chst neuartige Methoden fĂŒr die Kernbestandteile des DeepIGN-Systems der Hirntumor-Segmentierung im MRT und der multimodalen prĂ€ope-rativen MRT zur intraoperativen US-Bildregistrierung (iUS) unter Verwendung der jĂŒngs-ten Entwicklungen im Deep Learning vorgeschlagen. Anschließend wird die Ergebnisvor-hersage der verwendeten Deep-Learning-Netze weiter interpretiert und untersucht, indem fĂŒr den Menschen verstĂ€ndliche, erklĂ€rbare Karten erstellt werden. Schließlich wurden Open-Source-Pakete entwickelt und in weithin anerkannte Software integriert, die fĂŒr die Integration von Informationen aus Tracking-Systemen, die Bildvisualisierung und -fusion sowie die Anzeige von Echtzeit-Updates der Instrumente in Bezug auf den Patientenbe-reich zustĂ€ndig ist. Die Komponenten von DeepIGN wurden im Labor validiert und in einem simulierten Operationssaal evaluiert. FĂŒr das Segmentierungsmodul erreichte DeepSeg, ein generisches entkoppeltes Deep-Learning-Framework fĂŒr die automatische Abgrenzung von Gliomen in der MRT des Gehirns, eine Genauigkeit von 0,84 in Bezug auf den WĂŒrfelkoeffizienten fĂŒr das Bruttotumorvolumen. Leistungsverbesserungen wurden bei der Anwendung fort-schrittlicher Deep-Learning-AnsĂ€tze wie 3D-Faltungen ĂŒber alle Schichten, regionenbasier-tes Training, fliegende Datenerweiterungstechniken und Ensemble-Methoden beobachtet. Um Hirnverschiebungen zu kompensieren, wird ein automatisierter, schneller und genauer deformierbarer Ansatz, iRegNet, fĂŒr die Registrierung prĂ€operativer MRT zu iUS-Volumen als Teil des multimodalen Registrierungsmoduls vorgeschlagen. Es wurden umfangreiche Experimente mit zwei Multi-Location-Datenbanken durchgefĂŒhrt: BITE und RESECT. Zwei erfahrene Neurochirurgen fĂŒhrten eine zusĂ€tzliche qualitative Validierung dieser Studie durch, indem sie MRT-iUS-Paare vor und nach der deformierbaren Registrierung ĂŒberlagerten. Die experimentellen Ergebnisse zeigen, dass das vorgeschlagene iRegNet schnell ist und die besten Genauigkeiten erreicht. DarĂŒber hinaus kann das vorgeschlagene iRegNet selbst bei nicht trainierten Bildern konkurrenzfĂ€hige Ergebnisse liefern, was seine AllgemeingĂŒltigkeit unter Beweis stellt und daher fĂŒr die intraoperative neurochirurgische FĂŒhrung von Nutzen sein kann. FĂŒr das Modul "ErklĂ€rbarkeit" wird das NeuroXAI-Framework vorgeschlagen, um das Vertrauen medizinischer Experten in die Anwendung von KI-Techniken und tiefen neuro-nalen Netzen zu erhöhen. Die NeuroXAI umfasst sieben ErklĂ€rungsmethoden, die Visuali-sierungskarten bereitstellen, um tiefe Lernmodelle transparent zu machen. Die experimen-tellen Ergebnisse zeigen, dass der vorgeschlagene XAI-Rahmen eine gute Leistung bei der Extraktion lokaler und globaler Kontexte sowie bei der Erstellung erklĂ€rbarer Salienzkar-ten erzielt, um die Vorhersage des tiefen Netzwerks zu verstehen. DarĂŒber hinaus werden Visualisierungskarten erstellt, um den Informationsfluss in den internen Schichten des Encoder-Decoder-Netzwerks zu erkennen und den Beitrag der MRI-ModalitĂ€ten zur end-gĂŒltigen Vorhersage zu verstehen. Der ErklĂ€rungsprozess könnte medizinischen Fachleu-ten zusĂ€tzliche Informationen ĂŒber die Ergebnisse der Tumorsegmentierung liefern und somit helfen zu verstehen, wie das Deep-Learning-Modell MRT-Daten erfolgreich verar-beiten kann. Außerdem wurde ein interaktives neurochirurgisches Display fĂŒr die EingriffsfĂŒhrung entwickelt, das die verfĂŒgbare kommerzielle Hardware wie iUS-NavigationsgerĂ€te und Instrumentenverfolgungssysteme unterstĂŒtzt. Das klinische Umfeld und die technischen Anforderungen des integrierten multimodalen DeepIGN-Systems wurden mit der FĂ€higkeit zur Integration von (1) prĂ€operativen MRT-Daten und zugehörigen 3D-Volumenrekonstruktionen, (2) Echtzeit-iUS-Daten und (3) positioneller Instrumentenver-folgung geschaffen. Die Genauigkeit dieses Systems wurde anhand eines benutzerdefi-nierten Agar-Phantom-Modells getestet, und sein Einsatz in einem vorklinischen Operati-onssaal wurde simuliert. Die Ergebnisse der klinischen Simulation bestĂ€tigten, dass die Montage des Systems einfach ist, in einer klinisch akzeptablen Zeit von 15 Minuten durchgefĂŒhrt werden kann und mit einer klinisch akzeptablen Genauigkeit erfolgt. In dieser Arbeit wurde ein multimodales IGN-System entwickelt, das die jĂŒngsten Fort-schritte im Bereich des Deep Learning nutzt, um Neurochirurgen prĂ€zise zu fĂŒhren und prĂ€- und intraoperative Patientenbilddaten sowie interventionelle GerĂ€te in das chirurgi-sche Verfahren einzubeziehen. DeepIGN wurde als Open-Source-Forschungssoftware entwickelt, um die Forschung auf diesem Gebiet zu beschleunigen, die gemeinsame Nut-zung durch mehrere Forschungsgruppen zu erleichtern und eine kontinuierliche Weiter-entwicklung durch die Gemeinschaft zu ermöglichen. Die experimentellen Ergebnisse sind sehr vielversprechend fĂŒr die Anwendung von Deep-Learning-Modellen zur UnterstĂŒtzung interventioneller Verfahren - ein entscheidender Schritt zur Verbesserung der chirurgi-schen Behandlung von Hirntumoren und der entsprechenden langfristigen postoperativen Ergebnisse

    An exploration of movement and handling by physiotherapists in a rehabilitation setting: a motion analysis study.

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    Work-related musculoskeletal disorders (WRMSD) affect between 56-80% of physiotherapists, with patient handling often reported as a risk factor. Physiotherapists use therapeutic handling to aid patient rehabilitation. Therapeutic handling involves the physiotherapist "guiding, facilitating, manipulating or providing resistance" to the patient. Therapeutic handling can subject physiotherapists to high loading forces during patient handling. The aims of this doctoral thesis were to quantify physiotherapists' movement during therapeutic patient handling tasks, assess risk of injury against a frequently used ergonomic tool, and investigate whether there may be a relationship between patient handling and WRMSD. This research employed a descriptive cross-sectional study design and a positivistic approach to explore and quantitatively measure physiotherapist movement. A portable three-dimensional motion analysis system, Xsens MTw Awinda, was used to measure physiotherapist movement during patient treatments in a neurological setting. The physiotherapists' movement and posture were quantified, described and assessed using the Rapid Upper Limb Assessment (RULA) tool. The incidence and personal impact of WRMSD were investigated with the extended Nordic Musculoskeletal Questionnaire (NMQ-E) and potential patient tasks of risk were discussed. The physiotherapists used four main positions during patient handling tasks: 1) kneeling; 2) half-kneeling; 3) standing; and 4) sitting. Eight patient handling tasks were identified: 1) lie-to-sit; 2) sit-to-lie; 3) sit-to-stand; 4) upper limb; 5) lower limb; 6) trunk; 7) standing; and 8) walking facilitation. Kneeling or sitting positions were used by the physiotherapists most often during lie-to-sit, sit-to-lie, sit-to-stand, upper limb, trunk and standing facilitation tasks. Standing was the most common physiotherapist position during lower limb and walking tasks. Kneeling, half-kneeling and sitting positions demonstrated greater neck extension, which scored highly with the RULA and indicated potential risk of injury. Standing demonstrated more cervicothoracic flexion than kneeling and sitting, which demonstrated greater lumbosacral flexion than standing. The physiotherapists' hips and knees often maintained end-range flexion when kneeling or half-kneeling, which is discouraged in ergonomics literature. The low back was the most frequent anatomical area of WRMSD, with 60% of the physiotherapists having experienced discomfort there within their career. Physiotherapists were found to temporarily have changed jobs, sought professional help or taken medication for their shoulder, elbow or low back discomfort. However, none of the physiotherapists had taken sick leave in the last twelve months. This research found that tasks were more often performed in kneeling or sitting positions than in standing. Moving and handling guidance considers the handler in a standing position; guidance should therefore start to consider the handler in the variety of positions found in clinical practice. Ergonomic assessments, such as the RULA, consider the trunk as one joint. This research investigated three trunk joints, with different postures found at the cervicothoracic and lumbosacral junctions. Future research should appreciate how the position of the handler can impact trunk posture. More research needs to be conducted to qualitatively investigate physiotherapists' perceptions and experiences of patient handling. This research has provided a detailed exploration into therapeutic handling the neurological setting which can be used to guide future research
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