Control techniques for mechatronic assisted surgery

The treatment response for traumatic head injured patients can be improved by using an autonomous robotic system to perform basic, time-critical emergency neurosurgery, reducing costs and saving lives. In this thesis, a concept for a neurosurgical robotic system is proposed to perform three specific emergency neurosurgical procedures; they are the placement of an intracranial pressure monitor, external ventricular drainage, and the evacuation of chronic subdural haematoma. The control methods for this system are investigated following a curiosity led approach. Individual problems are interpreted in the widest sense and solutions posed that are general in nature. Three main contributions result from this approach: 1) a clinical evidence based review of surgical robotics and a methodology to assist in their evaluation, 2) a new controller for soft-grasping of objects, and 3) new propositions and theorems for chatter suppression sliding mode controllers. These contributions directly assist in the design of the control system of the neurosurgical robot and, more broadly, impact other areas outside the narrow con nes of the target application. A methodology for applied research in surgical robotics is proposed. The methodology sets out a hierarchy of criteria consisting of three tiers, with the most important being the bottom tier and the least being the top tier. It is argued that a robotic system must adhere to these criteria in order to achieve acceptability. Recent commercial systems are reviewed against these criteria, and are found to conform up to at least the bottom and intermediate tiers. However, the lack of conformity to the criteria in the top tier, combined with the inability to conclusively prove increased clinical benefit, particularly symptomatic benefit, is shown to be hampering the potential of surgical robotics in gaining wide establishment. A control scheme for soft-grasping objects is presented. Grasping a soft or fragile object requires the use of minimum contact force to prevent damage or deformation. Without precise knowledge of object parameters, real-time feedback control must be used to regulate the contact force and prevent slip. Moreover, the controller must be designed to have good performance characteristics to rapidly modulate the fingertip contact force in response to a slip event. A fuzzy sliding mode controller combined with a disturbance observer is proposed for contact force control and slip prevention. The robustness of the controller is evaluated through both simulation and experiment. The control scheme was found to be effective and robust to parameter uncertainty. When tested on a real system, however, chattering phenomena, well known to sliding mode research, was induced by the unmodelled suboptimal components of the system (filtering, backlash, and time delays). This reduced the controller performance. The problem of chattering and potential solutions are explored. Real systems using sliding mode controllers, such as the control scheme for soft-grasping, have a tendency to chatter at high frequencies. This is caused by the sliding mode controller interacting with un-modelled parasitic dynamics at the actuator-input and sensor-output of the plant. As a result, new chatter-suppression sliding mode controllers have been developed, which introduce new parameters into the system. However, the effect any particular choice of parameters has on system performance is unclear, and this can make tuning the parameters to meet a set of performance criteria di cult. In this thesis, common chatter-suppression sliding mode control strategies are surveyed and simple design and estimation methods are proposed. The estimation methods predict convergence, chattering amplitude, settling time, and maximum output bounds (overshoot) using harmonic linearizations and invariant ellipsoid sets

An Implantable Low Pressure Biosensor Transponder

The human body’s intracranial pressure (ICP) is a critical element in sustaining healthy blood flow to the brain while allowing adequate volume for brain tissue within the relatively rigid structure of the cranium. Disruptions in the body’s maintenance of intracranial pressure are often caused by hemorrhage, tumors, edema, or excess cerebral spinal fluid resulting in treatments that are estimated to globally cost up to approximately five billion dollars annually. A critical element in the contemporary management of acute head injury, intracranial hemorrhage, stroke, or other conditions resulting in intracranial hypertension, is the real-time monitoring of ICP. Currently such monitoring can only take place short-term within an acute care hospital, is prone to measurement drift, and is comprised of externally tethered pressure sensors that are temporarily implanted into the brain, thus carrying a significant risk of infection. To date, reliable, low drift, completely internalized, long-term ICP monitoring devices remain elusive. In addition to being safer and more reliable in the short-term, such a device would expand the use of ICP monitoring for the management of chronic diseases involving ICP hypertension and further expand research into these disorders. This research studies the current challenges of existing ICP monitoring systems and investigates opportunities for potentially allowing long-term implantable bio-pressure sensing, facilitating possible improvements in treatment strategies. Based upon the research, this thesis evaluates piezo-resistive strain sensing for low power, sub-millimeter of mercury resolution, in application to implantable intracranial pressure sensing

NNeMo (Neonatal NeuroMonitor) - a hybrid optical system to characterize perfusion and metabolism in the newborn brain

Premature birth, defined as a gestational period less than 37 weeks, occurs in 8% of infants born in Canada. These births are associated with a higher risk of developing neurological complications. Infants born with very low birth weights (VLBW, \u3c 1500 g) experience cognitive or behavioural deficits at a rate of 40-50%, while a further 5-10% develop major disorders such as cerebral palsy. The likelihood of injury increases with a shorter gestational period and/or a lower birthweight. Intraventricular hemorrhaging (IVH) occurs in 20-25% of VLBW infants, characterized by bleeding in the germinal matrix and surrounding white matter. This highly vascularized region is particularly susceptible to bleeds due to underdeveloped cerebrovascular structures. Severe IVH causes an inflammatory response and subsequent obstruction of cerebrospinal fluid (CSF) drainage, resulting in enlargement of the brain’s ventricles, referred to as post-hemorrhagic ventricular dilatation (PHVD). PHVD increases intracranial pressure and can result in compression/damage of brain tissue. Diagnosis of IVH and PHVD is regularly performed using cranial ultrasound. Clinicians can visually assess and grade hemorrhaging/ventricle dilatation. Ultrasound, however, is limited in its ability to continuously monitor and only detects irreversible damage. NNeMo (Neonatal NeuroMonitor) is a hybrid optical device combining diffuse correlation (DCS) and near-infrared spectroscopy (NIRS) to simultaneous monitor cerebral blood flow (CBF) and metabolism at the bedside. DCS analyzes light scatter from red blood cells to infer their motion and calculate CBF while NIRS exploits light absorption properties to quantify changes in oxidized cytochrome c oxidase (oxCCO), a direct marker of energy metabolism. System validation was presented in a piglet model of neonatal hypoxia-ischemia. Clinical translation of NNeMo was demonstrated in PHVD infants during ventricular taps (i.e., CSF drainage). Changes in perfusion and metabolism are presented in premature infants at high risk of IVH within the first 72 hours of life. Lastly, NNeMo was translated to the cardiac operating room, in patients undergoing surgery with cardiopulmonary bypass, to observe metabolic response to large intraoperative changes in CBF. Optical measures of perfusion and metabolism show potential to act as prognostic markers of injury and could aid clinicians in patient management before significant damage persists

Implantable microdevice for the treatment of hydrocephalus

We present a novel microdevice for the treatment of hydrocephalus. Hydrocephalus is a pathological condition in which excessive cerebrospinal fluid (CSF) is accumulated within the subarachnoid space of the brain due to deficient arachnoid granulations, resulting in the brain damage or death. Current treatment for hydrocephalus is to surgically implant a shunt device to drain the excessive fluid from the ventricles to peritoneal cavity or other parts of the body. This method has over 50% failure rate due to occlusions and mechanical failures of shunt components. The proposed microfabricated device can mimic the function of normalarachnoid granulations and thus can replace the deficient arachnoid granulations. The microfabricated arachnoid granulations (MAG) consist of arrays of microvalves and microneedles.The microvalves are made of a PDMS/Parylene composite layer and have a 3-D dome petal shape. Such geometry enables the microvalve to rectify fluid flow in the forward and backward direction due to pressure differentials like normal arachnoid granulation. Microvalve design was optimized using 3-D numerical simulation. The microvalves were fabricated using three main microfabrication techniques: diffuser lithography for dome-shaped SU-8 mold fabrication, thin polymer film deposition and reflow for PDMS/Parylene membrane formation, and excimer laser machining for valve opening. The pressure drop vs. flow rate characteristics of the fabricated microvalve was investigated through in-vitro flow tests using a bench-top CSF simulator. The results showed that a 10x10 microvalve array with combined opening shape is optimal for our application.The microneedle array is to surgically pierce the dura mater membrane after being assembled with the microvalve. The microneedles were fabricated using three main techniques: diffraction photolithography for tapered SU-8 needle fabrication, RIE etching for needle sharpening, and excimer laser machining for through-hole creation. Puncture tests were conducted using pig’s dura mater and the microneedles coated with a Ti layer showed promising results (16 out of 100 needles pierced dura and the needles were not deformed). Blood adhesion tests were also carried out using human blood simulating the CSF dynamics and no significant platelet adhesion was observed at the microneedles. The MAG presented in this dissertation demonstrates a great potential for the treatment of hydrocephalus.Ph.D., Mechanical Engineering and Mechanics -- Drexel University, 201

Coeur & Cerveau. Lien entre les pathologies cardiovasculaires et la neurodégénérescence par une approche combinée biophysique et statistique

Clinical studies have identified several cardiovascular risk factors associated to dementia and cardiac pathologies, but their pathological interaction remains poorly understood. Classically, the investigation of the heart-brain relationship is mostly carried out through statistical analysis exploring the association between cardiac indicators and cognitive biomarkers. This kind of investigations are usually performed in large-scale epidemiological datasets, for which joint measurements of both brain and heart are available. For this reason, most of these analyses are performed on cohorts representing the general population. Therefore, the generalisation of these findings to dementia studies is generally difficult, since extensive assessments of cardiac and cardiovascular function in currently available dementia dataset is usually lacking. Another limiting factor of current studies is the limited interpretability of the complex pathophysiological relations between heart and brain allowed by standard correlation analyses. Improving our understanding of the implications of cardiovascular function in dementia ultimately requires the development of more refined mechanistic models of cardiac physiology, as well as the development of novel approaches allowing to integrate these models with image-based brain biomarkers. To address these challenges, in this thesis we developed new computational tools based on the integration of mechanistic models within a statistical learning framework. First, we studied the association between non-observable physiological indicators, such as cardiac contractility, and brain-derived imaging features. To this end, the parameter-space of a mechanistic model of the cardiac function was constrained during the personalisation stage based on the relationships between the parameters of the cardiac model and brain information. This allows to tackle the ill-posedness of the inverse problem associated to model personalisation, and obtain patient-specific solutions that are comparable population-wise.Second, we developed a probabilistic imputation model that allows to impute missing cardiac information in datasets with limited data. The imputation leverages on the cardiac-brain dynamics learned in a large-scale population analysis, and uses this knowledge to obtain plausible solutions in datasets with partial data. The generative nature of the approach allows to simulate the evolution of cardiac model parameters as brain features change. The framework is based on a conditional variational autoencoder (CVAE) combined with Gaussian process (GP) regression. Third, we analysed the potential role of cardiac model parameters as early biomarkers for dementia, which could help to identify individuals at risk. To this end, we imputed missing cardiac information in an Alzheimer's disease (AD) longitudinal cohort. Next, via disease progression modelling we estimated the disease stage for each individual based on the evolution of biomarkers. This allowed to obtain a model of the disease evolution, to analyse the role of cardiac function in AD, and to identify cardiac model parameters as potential early-stage biomarkers of dementia. These results demonstrate the importance of the developed tools by providing clinically plausible associations between cardiac model parameters and brain imaging features in an epidemiological dataset, as well as highlighting insights about the physiological relationship between cardiac function and dementia biomarkers. The obtained results open new research directions, such as the use of more complex mechanistic models that allow to better characterise the heart-brain relationship, or the use of biophysical cardiac models to derive in-silico biomarkers for identifying individuals at risk of dementia in clinical routine, and/or for their inclusion in neuroprotective trials.Les études cliniques ont identifié plusieurs facteurs de risque cardiovasculaire associés à la démence et aux pathologies cardiaques, mais leur interaction pathologique reste mal comprise. Habituellement, l'étude de la relation cœur-cerveau est réalisée à travers d'analyses statistiques explorant l'association entre les indicateurs cardiaques et les biomarqueurs cognitifs. Ce type d'étude est généralement réalisé dans des bases de données épidémiologiques, pour lesquelles des mesures conjointes du cerveau et du cœur sont disponibles. Par conséquent, la généralisation de ces résultats aux études sur la démence est difficile, car les évaluations approfondies des fonctions cardiovasculaires dans les bases de données sur la démence actuellement disponibles font généralement défaut. Un autre facteur limitatif des études actuelles est l'interprétabilité limitée des relations physiopathologiques entre le cœur et le cerveau. L'amélioration de notre compréhension des implications de la fonction cardiovasculaire dans la démence nécessite le développement de modèles mécniaques de la physiologie cardiaque, ainsi que le développement de nouvelles approches permettant d'intégrer ces modèles avec des biomarqueurs cérébraux basés sur l'image. Pour relever ces défis, nous avons développé dans cette thèse de nouveaux outils informatiques basés sur l'intégration de modèles mécaniques dans un cadre d'apprentissage statistique. Premièrement, nous avons étudié l'association entre des indicateurs physiologiques non observables, tels que la contractilité cardiaque, et des caractéristiques d'imagerie dérivées du cerveau. À cette fin, l'espace des paramètres d'un modèle mécanique de la fonction cardiaque a été contraint pendant l'étape de personnalisation sur la base des relations entre les paramètres du modèle cardiaque et les informations cérébrales. Cela permet d’attenuer le caractère mal defini du problème inverse associé à la personnalisation du modèle, et d'obtenir des solutions spécifiques au patient qui sont comparables au sein de la population.Deuxièmement, nous avons développé un modèle d'imputation probabiliste qui permet d'imputer les informations cardiaques manquantes dans des bases de données limitées. L'imputation repose sur les dynamiques cœur-cerveau apprises à partir de l'analyse d'une grande population de sujets, et utilise cette connaissance pour obtenir des solutions plausibles dans des bases de données partielles. La nature générative de l'approche permet de simuler l'évolution des paramètres du modèle cardiaque lorsque les caractéristiques du cerveau changent. Troisièmement, nous avons analysé le rôle des paramètres du modèle cardiaque comme biomarqueurs précoces de la démence, ce qui pourrait aider à identifier les individus à risque. Dans ce but, nous avons imputé les informations cardiaques manquantes dans une cohorte longitudinale de la maladie d'Alzheimer. Ensuite, grâce à la modélisation de la progression de la maladie, nous avons estimé le stade de la maladie pour chaque individu sur la base de l'évolution des biomarqueurs. Ceci a permis d'obtenir un modèle de l'évolution de la maladie, d'analyser le rôle de la fonction cardiaque, et d'identifier les paramètres du modèle cardiaque comme biomarqueurs potentiels de la démence à un stade précoce. Les résultats démontrent l'importance des outils développés en obtenant des associations cliniquement plausibles entre les paramètres du modèle cardiaque et les caractéristiques de l'imagerie cérébrale. Ces résultats mettent également en évidence des informations sur la relation physiologique entre la fonction cardiaque et les biomarqueurs de la démence. Les résultats obtenus ouvrent de nouvelles voies de recherche, telles que l'utilisation de modèles mécaniques plus complexes permettant de mieux caractériser la relation cœur-cerveau, ou l'utilisation de modèles cardiaques biophysiques pour dériver des biomarqueurs in-silico afin d'identifier les individus à risque de démence

Creating a multivariable model to predict primary graft dysfunction after heart transplantation in the United Kingdom using the 2014 International Society of Heart and Lung Transplantation consensus definition

Heart failure places a global strain on healthcare provision. It has an increasing incidence and represents the endpoint of a variety of cardiovascular diseases. The preceding decades have carved out a clear management algorithm for the use of pharmacotherapies (neurohormonal antagonists), device-based therapies (Implantable Cardioverting Defibrillator (ICD) and Cardiac Resynchronisation Therapy (CRT)) and mechanical therapies including left ventricular assist devices and heart transplantation. While heart transplantation remains the gold standard for the suitable few, the advancement of healthcare systems and improved working conditions and safety regulations have changed the demographics of the typical organ donor which traditionally were young brainstem death donors (DBD) with minimal other comorbidities. Nevertheless, transplantation confers a substantial survival benefit for selected patients with advanced heart failure, achieving a 1-year survival rate of ≥80%. The primary cause for early mortality in recipients remains primary graft dysfunction (PGD). The incidence of PGD throughout the UK and the world are variable due to the lack of a standardised definition until 2014. My research explored the true incidence of PGD throughout the UK using data collected from each of the 6 transplant centres alongside the National Health Service Blood and Transplant database. I then looked at risk factors for PGD which culminated in the largest PGD study recorded at the time of writing. I also looked into the role of mechanical circulatory support to bridge patients in cardiogenic shock post-myocardial infarction in Scotland. I finally developed 2 scoring systems, 1 for Primary Graft Dysfunction (PREDICTA) and 1 using the modified Delphi Method of a consensus agreement (GTS) to factor in elements of frailty which had been garnering increasing interest at conferences I had attended