Magnetic localization system for short-range positioning: a ready-to-use design tool

Magnetic localization is used in many indoor positioning applications, such as industrial, medical, and IoT, for its benefits related to the absence of line of sight needs, multipath and fading, the low cost of transmitters and receivers, and the simple development of setups made of coils and magnetic sensors. In short-range applications, this technology could bring some advantages with respect to ultrasound, laser, or RF ones. Nevertheless, fixed both the desired accuracy and the energy constraints, the optimal design of a localization system based on magnetic measurement depends on several factors: the dimension, the number and the optimal positions of the anchors, the uncertainties due to the sensing elements, and the data acquisition systems (DAQs). To preliminary fix all these parameters, suitable simulation environments allow developers to save time and money in developing localization applications. Many magnetic field simulators are available, but it is rare to find those that, considering the uncertainty due to the receiver and DAQs, are able to provide optimal anchors scenario given a target accuracy. To address this problem, this article presents a simulation tool providing the user with design requirements for given target accuracy. The aim of this article is to perform the first steps in providing a ready-to-use specification framework that given the localization domain, the mobile sensors, the DAQ characteristics, and the target accuracy and helps the developer of indoor magnetic positioning systems. The actual validity of the simulation model has been tested on a real setup.Postprint (published version

Improving Condition and Sensitivity of Linear Inverse Problems in Magnetic Applications

Die Identifikation nicht direkt zugänglicher Prozesse anhand gemessener Daten ist von großer Bedeutung in vielen Bereichen. Im Fokus dieser Arbeit liegen Applikationen in der Magnetostatik, Magnetokardiographie und Magnetinduktionstomographie. Ein Ansatz zur Identifikation besteht in der Lösung eines entsprechenden linear inversen Problems. Unglücklicherweise haben in den Daten enthaltene Fehler und Rauschen einen signifikanten Einfluss auf die inverse Lösung. Ziel dieser Arbeit ist die Reduktion der Einflüsse von Fehlern und Rauschen durch eine Verbesserung der Kondition des Problems, sowie eine Steigerung der Sensitivität der Messanordnungen. Zur Bestimmung der Kondition wird das Verhältnis des größten und mittleren Singulärwerts der Kernmatrix als neues Maß vorgeschlagen. Darüber hinaus werden Ansätze zur Analyse der Sensitivität hinsichtlich der Messung elektromagnetischer Quellen und der Erfassung elektrischer Leitfähigkeitsveränderungen präsentiert.Strategien zur Verbesserung von Kondition und Sensitivität werden in vier Simulationsstudien beschrieben. In der ersten Studie wird ein Tabu-Suche-Ansatz zur Optimierung der Anordnung magnetischer Sensoren vorgestellt. Anordnungen mit optimierte Sensorpositionen resultieren dabei in einer deutlich besseren Kondition als regelmäßige Anordnungen. In einer zweiten Studie werden Parameter adaptiert,welche den Quellenraum für die Bildgebung durch magnetische Nanopartikel definieren. Als eine Schlussfolgerung sollte der Quellenraum etwas größer als das Sensorareal definiert werden. Diese Arbeit zeigt ebenfalls, dass Variationen in den Sensorrichtungen für monoaxiale Sensorarrays zu einer Verbesserung der Kondition führen. Zudem wird die Sensitivität von Spulenanordnungen für die Magnetinduktionstomographie bewertet und verglichen. Durch Nutzung relativ großer Spulen, die das Messgebiet nahezu vollständig abdecken, können Kondition und Sensitivität wesentlich verbessert werden.Die präsentierten Methoden und Strategien ermöglichen eine substantielle Verbesserung der Kondition des linear inversen Problems bei der Analyse magnetischer Messungen. Insbesondere die Anordnung von Sensoren in Bezug auf das Messobjekt ist kritisch für die Kondition, sowie die Qualität inverser Lösungen. Die vorgestellten Methoden sind darüber hinaus für linear inverse Probleme in zahlreichen Bereichen einsetzbar.The identification and reconstruction of hidden, not directly accessible processes from measured data is important in many areas of research and engineering. This thesis focusses on applications in magnetostatics, magnetocardiography, and magneticinduction tomography. One approach to identify these processes is to solve a related linear inverse problem. Unfortunately, noise and errors in the data have a significant impact on inverse solutions.The aim of this work is to reduce the effects of noise and errors by improving the condition of the problem and to increase the sensitivity of measurement setups. To quantify the condition, we propose the ratio of the largest and the mean singular value of the kernel matrix. Moreover, we outline approaches to analyse quantitatively and qualitatively the sensitivity to electromagnetic sources and electrical conductivity changes.In four simulation studies, strategies to improve the condition and sensitivity inmagnetic applications are described. First, we present a tabu search algorithm to optimize arrangements of magnetic sensors. Optimized sensor arrays result in a considerably improved condition compared with regular arrangements. Second, we adapt parameters that define source space grids for magnetic nanoparticle imaging. One conclusion is that the source space should be defined slightly larger than the sensor area. Third, we demonstrate for mono-axial sensor arrays that variations in thesensor directions and small variations in the sensor positions lead to improvements of the condition, too. Finally, we evaluate and compare the sensitivities of six coil setups for magnetic induction tomography. Our investigations indicate a rapid decay of sensitivity by several orders of magnitude within a range of a few centimetres. By using relatively large coils that cover the measurement region almost completely, the condition and sensitivity can be improved clearly.The methods and strategies presented in this thesis facilitate substantial improvements of the condition for linear inverse problems in magnetic applications. In particular, the arrangement of sensors relative to the measurement object is critical to the condition and to the quality of inverse solutions. Moreover, the presented methods are applicable to linear inverse problems in various fields

Development of A Soft Robotic Approach for An Intra-abdominal Wireless Laparoscopic Camera

In Single-Incision Laparoscopic Surgery (SILS), the Magnetic Anchoring and Guidance System (MAGS) arises as a promising technique to provide larger workspaces and field of vision for the laparoscopes, relief space for other instruments, and require fewer incisions. Inspired by MAGS, many concept designs related to fully insertable magnetically driven laparoscopes are developed and tested on the transabdominal operation. However, ignoring the tissue interaction and insertion procedure, most of the designs adopt rigid structures, which not only damage the patients\u27 tissue with excess stress concentration and sliding motion but also require complicated operation for the insertion. Meanwhile, lacking state tracking of the insertable camera including pose and contact force, the camera systems operate in open-loop control. This provides mediocre locomotion precision and limited robustness to uncertainties in the environment. This dissertation proposes, develops, and validates a soft robotic approach for an intra-abdominal wireless laparoscopic camera. Contributions presented in this work include (1) feasibility of a soft intra-abdominal laparoscopic camera with friendly tissue interaction and convenient insertion, (2) six degrees of freedom (DOF) real-time localization, (3) Closed-loop control for a robotic-assisted laparoscopic system and (4) untethering solution for wireless communication and high-quality video transmission. Embedding magnet pairs into the camera and external actuator, the camera can be steered and anchored along the abdominal wall through transabdominal magnetic coupling. To avoid the tissue rapture by the sliding motion and dry friction, a wheel structure is applied to achieve rolling motion. Borrowing the ideas from soft robotic research, the main body of the camera implements silicone material, which grants it the bendability to passively attach along the curved abdominal wall and the deformability for easier insertion. The six-DOF pose is estimated in real-time with internal multi-sensor fusion and Newton-Raphson iteration. Combining the pose tracking and force-torque sensor measurement, an interaction model between the deformable camera and tissue is established to evaluate the interaction force over the tissue surface. Moreover, the proposed laparoscopic system is integrated with a multi-DOF manipulator into a robotic-assisted surgical system, where a closed-loop control is realized based on a feedback controller and online optimization. Finally, the wireless control and video streaming are accomplished with Bluetooth Low Energy (BLE) and Analog Video (AV) transmission. Experimental assessments have been implemented to evaluate the performance of the laparoscopic system

Neurosurgical Ultrasound Pose Estimation Using Image-Based Registration and Sensor Fusion - A Feasibility Study

Modern neurosurgical procedures often rely on computer-assisted real-time guidance using multiple medical imaging modalities. State-of-the-art commercial products enable the fusion of pre-operative with intra-operative images (e.g., magnetic resonance [MR] with ultrasound [US] images), as well as the on-screen visualization of procedures in progress. In so doing, US images can be employed as a template to which pre-operative images can be registered, to correct for anatomical changes, to provide live-image feedback, and consequently to improve confidence when making resection margin decisions near eloquent regions during tumour surgery. In spite of the potential for tracked ultrasound to improve many neurosurgical procedures, it is not widely used. State-of-the-art systems are handicapped by optical tracking’s need for consistent line-of-sight, keeping tracked rigid bodies clean and rigidly fixed, and requiring a calibration workflow. The goal of this work is to improve the value offered by co-registered ultrasound images without the workflow drawbacks of conventional systems. The novel work in this thesis includes: the exploration and development of a GPU-enabled 2D-3D multi-modal registration algorithm based on the existing LC2 metric; and the use of this registration algorithm in the context of a sensor and image-fusion algorithm. The work presented here is a motivating step in a vision towards a heterogeneous tracking framework for image-guided interventions where the knowledge from intraoperative imaging, pre-operative imaging, and (potentially disjoint) wireless sensors in the surgical field are seamlessly integrated for the benefit of the surgeon. The technology described in this thesis, inspired by advances in robot localization demonstrate how inaccurate pose data from disjoint sources can produce a localization system greater than the sum of its parts

Development and Evaluation of Data Processing Techniques in Magnetoencephalography

With MEG, the tiny magnetic fields produced by neuronal currents within the brain can be measured completely non-invasively. But the signals are very small (~100 fT) and often obscured by spontaneous brain activity and external noise. So, a recurrent issue in MEG data analysis is the identification and elimination of this unwanted interference within the recordings. Various strategies exist to meet this purpose. In this thesis, two of these strategies are scrutinized in detail. The first is the commonly used procedure of averaging over trials which is a successfully applied data reduction method in many neurocognitive studies. However, the brain does not always respond identically to repeated stimuli, so averaging can eliminate valuable information. Alternative approaches aiming at single trial analysis are difficult to realize and many of them focus on temporal patterns. Here, a compromise involving random subaveraging of trials and repeated source localization is presented. A simulation study with numerous examples demonstrates the applicability of the new method. As a result, inferences about the generators of single trials can be drawn which allows deeper insight into neuronal processes of the human brain. The second technique examined in this thesis is a preprocessing tool termed Signal Space Separation (SSS). It is widely used for preprocessing of MEG data, including noise reduction by suppression of external interference, as well as movement correction. Here, the mathematical principles of the SSS series expansion and the rules for its application are investigated. The most important mathematical precondition is a source-free sensor space. Using three data sets, the influence of a violation of this convergence criterion on source localization accuracy is demonstrated. The analysis reveals that the SSS method works reliably, even when the convergence criterion is not fully obeyed. This leads to utilizing the SSS method for the transformation of MEG data to virtual sensors on the scalp surface. Having MEG data directly on the individual scalp surface would alleviate sensor space analysis across subjects and comparability with EEG. A comparison study of the transformation results obtained with SSS and those produced by inverse and subsequent forward computation is performed. It shows strong dependence on the relative position of sources and sensors. In addition, the latter approach yields superior results for the intended purpose of data transformation

Strategies for optimal design of biomagnetic sensor systems

Magnetic field imaging (MFI) is a technique to record contact free the magnetic field distribution and estimate the underlying source distribution in the heart. Currently, the cardiomagnetic fields are recorded with superconducting quantum interference devices (SQUIDs), which are restricted to the inside of a cryostat filled with liquid helium or nitrogen. New room temperature optical magnetometers allow less restrictive sensor positioning, which raises the question of how to optimally place the sensors for robust field reconstruction. The objective in this study is to develop a generic object-oriented framework for optimizing sensor arrangements (sensor positions and orientations) which supports the necessary constraints of a limited search volume (only outside the body) and the technical minimum distance of sensors (e.g. 1 cm). In order to test the framework, a new quasi-continuous particle swarm optimizer (PSO) component is developed as well as an exemplary goal function component using the condition number (CN) of the leadfield matrix. Generic constraint handling algorithms are designed and implemented, that decompose complex constraints into basic ones. The constraint components interface to an operational exemplary optimization strategy which is validated on the magnetocardiographic sensor arrangement problem. The simulation setup includes a three compartment boundary element model of a torso with a fitted multi-dipole heart model. The results show that the CN, representing the reconstruction robustness of the inverse problem, can be reduced with our optimization by one order of magnitude within a sensor plane (the cryostat bottom) in front of the torso compared to a regular sensor grid. Reduction of another order of magnitude is achieved by optimizing sensor positions on the entire torso surface. Results also indicate that the number of sensors may be reduced to 20-30 without loss of robustness in terms of CN. The original contributions are the generic reusable framework and exemplary components, the quasicontinuous PSO algorithm with constraint support and the composite constraint handling algorithms

Magnetoelectric Sensor Systems and Applications

In the field of magnetic sensing, a wide variety of different magnetometer and gradiometer sensor types, as well as the corresponding read-out concepts, are available. Well-established sensor concepts such as Hall sensors and magnetoresistive sensors based on giant magnetoresistances (and many more) have been researched for decades. The development of these types of sensors has reached maturity in many aspects (e.g., performance metrics, reliability, and physical understanding), and these types of sensors are established in a large variety of industrial applications. Magnetic sensors based on the magnetoelectric effect are a relatively new type of magnetic sensor. The potential of magnetoelectric sensors has not yet been fully investigated. Especially in biomedical applications, magnetoelectric sensors show several advantages compared to other concepts for their ability, for example, to operate in magnetically unshielded environments and the absence of required cooling or heating systems. In recent years, research has focused on understanding the different aspects influencing the performance of magnetoelectric sensors. At Kiel University, Germany, the Collaborative Research Center 1261 “Magnetoelectric Sensors: From Composite Materials to Biomagnetic Diagnostics”, funded by the German Research Foundation, has dedicated its work to establishing a fundamental understanding of magnetoelectric sensors and their performance parameters, pushing the performance of magnetoelectric sensors to the limits and establishing full magnetoelectric sensor systems in biological and clinical practice