Advances in Real-Time 3D Reconstruction for Medical Endoscopy.

This contribution is intended to provide researchers with a comprehensive overview of the current state-of-the-art concerning real-time 3D reconstruction methods suitable for medical endoscopy. Over the past decade, there have been various technological advancements in computational power and an increased research effort in many computer vision fields such as autonomous driving, robotics, and unmanned aerial vehicles. Some of these advancements can also be adapted to the field of medical endoscopy while coping with challenges such as featureless surfaces, varying lighting conditions, and deformable structures. To provide a comprehensive overview, a logical division of monocular, binocular, trinocular, and multiocular methods is performed and also active and passive methods are distinguished. Within these categories, we consider both flexible and non-flexible endoscopes to cover the state-of-the-art as fully as possible. The relevant error metrics to compare the publications presented here are discussed, and the choice of when to choose a GPU rather than an FPGA for camera-based 3D reconstruction is debated. We elaborate on the good practice of using datasets and provide a direct comparison of the presented work. It is important to note that in addition to medical publications, publications evaluated on the KITTI and Middlebury datasets are also considered to include related methods that may be suited for medical 3D reconstruction.

In Situ Performance Monitoring of Electrochemical Oxygen and Hydrogen Peroxide Sensors in an Additively Manufactured Modular Microreactor.

Miniaturized and microstructured reactors in process engineering are essential for a more decentralized, flexible, sustainable, and resilient chemical production. Modern, additive manufacturing methods for metals enable complex reactor-geometries, increased functionality, and faster design iterations, a clear advantage over classical subtractive machining and polymer-based approaches. Integrated microsensors allow online, in situ process monitoring to optimize processes like the direct synthesis of hydrogen peroxide. We developed a modular tube-in-tube membrane reactor fabricated from stainless steel via 3D printing by laser powder bed fusion of metals (PBF-LB/M). The reactor concept enables the spatially separated dosage and resaturation of two gaseous reactants across a membrane into a liquid process medium. Uniquely, we integrated platinum-based electrochemical sensors for the online detection of analytes to reveal the dynamics inside the reactor. An advanced chronoamperometric protocol combined the simultaneous concentration measurement of hydrogen peroxide and oxygen with monitoring of the sensor performance and self-calibration in long-term use. We demonstrated the highly linear and sensitive monitoring of hydrogen peroxide and dissolved oxygen entering the liquid phase through the membrane. Our measurements delivered important real-time insights into the dynamics of the concentrations in the reactor, highlighting the power of electrochemical sensors applied in process engineering. We demonstrated the stable continuous measurement over 1 week and estimated the sensor lifetime for months in the acidic process medium. Our approach combines electrochemical sensors for process monitoring with advanced, additively manufactured stainless steel membrane microreactors, supporting the power of sensor-equipped microreactors as contributors to the paradigm change in process engineering and toward a greener chemistry.

Influence of a Structural Prior Mask on EIT Image Reconstruction.

Electrical Impedance Tomography (EIT) is a low-cost imaging method with promising results in visualizing ventilation distribution within the lungs. However, in clinical settings, the interpretability of EIT images is often limited by blurred anatomical alignment and reconstruction artifacts. Integrating structural priors into the EIT reconstruction process can enhance the interpretability of EIT images. In this contribution, we introduced a patient-specific structural prior mask into the EIT reconstruction process. Such prior mask ensures that only conductivity changes within the lung regions are reconstructed. With the aim to investigate the influence of the structural prior mask on the EIT images, we conducted numerical simulations in terms of four different ventilation status. EIT images were reconstructed with Gauss-Newton algorithm and discrete cosine transform-based EIT algorithm. We carried out quantitative analysis including the reconstruction error and figures of merit for the evaluation. The results show that the morphological structures of the lungs introduced by the prior mask are preserved in the EIT images, and the reconstruction artefacts are also limited. In conclusion, the incorporation of the structural prior mask enhances the interpretability of EIT images in clinical settings.Clinical relevance-The correct interpretation of an EIT image is crucial for a clinical diagnosis. This research demonstrates that a structural prior mask might have the potential to improve the interpretability of an EIT image, which facilitates the clinicians with a better understanding of EIT results.

Sensor Selection for Tidal Volume Determination via Linear Regression-Impact of Lasso versus Ridge Regression.

The measurement of respiratory volume based on upper body movements by means of a smart shirt is increasingly requested in medical applications. This research used upper body surface motions obtained by a motion capture system, and two regression methods to determine the optimal selection and placement of sensors on a smart shirt to recover respiratory parameters from benchmark spirometry values. The results of the two regression methods (Ridge regression and the least absolute shrinkage and selection operator (Lasso)) were compared. This work shows that the Lasso method offers advantages compared to the Ridge regression, as it provides sparse solutions and is more robust to outliers. However, both methods can be used in this application since they lead to a similar sensor subset with lower computational demand (from exponential effort for full exhaustive search down to the order of O (n2)). A smart shirt for respiratory volume estimation could replace spirometry in some cases and would allow for a more convenient measurement of respiratory parameters in home care or hospital settings.

Electrochemical Methods in the Cloud: FreiStat, an IoT-Enabled Embedded Potentiostat.

Embedded potentiostats enable electrochemical measurements in the Internet-of-Things (IoT) or other decentralized applications, such as remote environmental monitoring, electrochemical energy systems, and biomedical point-of-care applications. We report on Freiburg's Potentiostat (FreiStat) based on the AD5941 potentiostat circuit from Analog Devices, together with custom firmware, as the key to precise and advanced electrochemical methods. We demonstrated its analytical performance by various cyclic voltammetry measurements, advanced techniques such as differential pulse voltammetry, and a lactate biosensor measurement with currents in the nA range and a resolution of 54 pA. The FreiStat yielded analytical results comparable to benchtop devices and outperformed current commercial embedded potentiostats at significantly lower cost, smaller size, and lower power consumption. Decentralized corrosion analysis by a Tafel plot using the IBM Cloud showed its applicability in a typical IoT scenario. The developed open-source software framework facilitates the integration of electrochemical instrumentation into applications utilizing machine learning and other artificial intelligence. Together with the affordable and highly capable embedded potentiostat, our approach can leverage analytical chemistry toward increasingly important, more widespread and decentralized applications.

Automatic Life Detection Based on Efficient Features of Ground-Penetrating Rescue Radar Signals.

Good feature engineering is a prerequisite for accurate classification, especially in challenging scenarios such as detecting the breathing of living persons trapped under building rubble using bioradar. Unlike monitoring patients' breathing through the air, the measuring conditions of a rescue bioradar are very complex. The ultimate goal of search and rescue is to determine the presence of a living person, which requires extracting representative features that can distinguish measurements with the presence of a person and without. To address this challenge, we conducted a bioradar test scenario under laboratory conditions and decomposed the radar signal into different range intervals to derive multiple virtual scenes from the real one. We then extracted physical and statistical quantitative features that represent a measurement, aiming to find those features that are robust to the complexity of rescue-radar measuring conditions, including different rubble sites, breathing rates, signal strengths, and short-duration disturbances. To this end, we utilized two methods, Analysis of Variance (ANOVA), and Minimum Redundancy Maximum Relevance (MRMR), to analyze the significance of the extracted features. We then trained the classification model using a linear kernel support vector machine (SVM). As the main result of this work, we identified an optimal feature set of four features based on the feature ranking and the improvement in the classification accuracy of the SVM model. These four features are related to four different physical quantities and independent from different rubble sites.

Advanced electrochemical potential monitoring for improved understanding of electrical neurostimulation protocols.

Objective.Current-controlled neurostimulation is increasingly used in the clinical treatment of neurological disorders and widely applied in neural prostheses such as cochlear implants. Despite its importance, time-dependent potential traces of electrodes during microsecond-scale current pulses, especially with respect to a reference electrode (RE), are not precisely understood. However, this knowledge is critical to predict contributions of chemical reactions at the electrodes, and ultimately electrode stability, biocompatibility, and stimulation safety and efficacy.Approach.We assessed the electrochemistry of neurostimulation protocolsin vitrowith Pt microelectrodes from millisecond (classical electroanalysis) to microsecond (neurostimulation) timescales. We developed a dual-channel instrumentation amplifier to include a RE in neurostimulation setups. Uniquely, we combined potential measurements with potentiostatic prepolarization to control and investigate the surface status, which is not possible in typical stimulation setups.Main results.We thoroughly validated the instrumentation and highlighted the importance of monitoring individual electrochemical electrode potentials in different configurations of neurostimulation. We investigated electrode processes such as oxide formation and oxygen reduction by chronopotentiometry, bridging the gap between milli- and microsecond timescales. Our results demonstrate how much impact on potential traces the electrode's initial surface state and electrochemical surface processes have, even on a microsecond scale.Significance.Our unique use of preconditioning in combination with stimulation reveals that interpreting potential traces with respect to electrode processes is misleading without rigorous control of the electrode's surface state. Especiallyin vivo, where the microenvironment is unknown, simply measuring the voltage between two electrodes cannot accurately reflect the electrode's state and processes. Potential boundaries determine charge transfer, corrosion, and alterations of the electrode/tissue interface such as pH and oxygenation, particularly in long-termin vivouse. Our findings are relevant for all use-cases of constant-current stimulation, strongly advocating for electrochemicalin situinvestigations in many applications like the development of new electrode materials and stimulation methods.

Structural priors represented by discrete cosine transform improve EIT functional imaging.

Structural prior information can improve electrical impedance tomography (EIT) reconstruction. In this contribution, we introduce a discrete cosine transformation-based (DCT-based) EIT reconstruction algorithm to demonstrate a way to incorporate the structural prior with the EIT reconstruction process. Structural prior information is obtained from other available imaging methods, e.g., thorax-CT. The DCT-based approach creates a functional EIT image of regional lung ventilation while preserving the introduced structural information. This leads to an easier interpretation in clinical settings while maintaining the advantages of EIT in terms of bedside monitoring during mechanical ventilation. Structural priors introduced in the DCT-based approach are of two categories in terms of different levels of information included: a contour prior only differentiates lung and non-lung region, while a detail prior includes information, such as atelectasis, within the lung area. To demonstrate the increased interpretability of the EIT image through structural prior in the DCT-based approach, the DCT-based reconstructions were compared with reconstructions from a widely applied one-step Gauss-Newton solver with background prior and from the advanced GREIT algorithm. The comparisons were conducted both on simulation data and retrospective patient data. In the simulation, we used two sets of forward models to simulate different lung conditions. A contour prior and a detail prior were derived from simulation ground truth. With these two structural priors, the reconstructions from the DCT-based approach were compared with the reconstructions from both the one-step Gauss-Newton solver and the GREIT. The difference between the reconstructions and the simulation ground truth is calculated by the ℓ2-norm image difference. In retrospective patient data analysis, datasets from six lung disease patients were included. For each patient, a detail prior was derived from the patient's CT, respectively. The detail prior was used for the reconstructions using the DCT-based approach, which was compared with the reconstructions from the GREIT. The reconstructions from the DCT-based approach are more comprehensive and interpretable in terms of preserving the structure specified by the priors, both in simulation and retrospective patient data analysis. In simulation analysis, the ℓ2-norm image difference of the DCT-based approach with a contour prior decreased on average by 34% from GREIT and 49% from the Gauss-Newton solver with background prior; for reconstructions of the DCT-based approach with detail prior, on average the ℓ2-norm image difference is 53% less than GREIT and 63% less than the reconstruction with background prior. In retrospective patient data analysis, the reconstructions from both the DCT-based approach and GREIT can indicate the current patient status, but the DCT-based approach yields more interpretable results. However, it is worth noting that the preserved structure in the DCT-based approach is derived from another imaging method, not from the EIT measurement. If the structural prior is outdated or wrong, the result might be misleadingly interpreted, which induces false clinical conclusions. Further research in terms of evaluating the validity of the structural prior and detecting the outdated prior is necessary.

Effect of a Patient-Specific Structural Prior Mask on Electrical Impedance Tomography Image Reconstructions.

Electrical Impedance Tomography (EIT) is a low-cost imaging method which reconstructs two-dimensional cross-sectional images, visualising the impedance change within the thorax. However, the reconstruction of an EIT image is an ill-posed inverse problem. In addition, blurring, anatomical alignment, and reconstruction artefacts can hinder the interpretation of EIT images. In this contribution, we introduce a patient-specific structural prior mask into the EIT reconstruction process, with the aim of improving image interpretability. Such a prior mask ensures that only conductivity changes within the lung regions are reconstructed. To evaluate the influence of the introduced structural prior mask, we conducted numerical simulations with two scopes in terms of their different ventilation statuses and varying atelectasis scales. Quantitative analysis, including the reconstruction error and figures of merit, was applied in the evaluation procedure. The results show that the morphological structures of the lungs introduced by the mask are preserved in the EIT reconstructions and the reconstruction artefacts are decreased, reducing the reconstruction error by 25.9% and 17.7%, respectively, in the two EIT algorithms included in this contribution. The use of the structural prior mask conclusively improves the interpretability of the EIT images, which could facilitate better diagnosis and decision-making in clinical settings.

Wireless Passive Sensor Technology through Electrically Conductive Media over an Acoustic Channel.

Hydrogen-based technologies provide a potential route to more climate-friendly mobility in the automotive and aviation industries. High-pressure tanks consisting of carbon-fiber-reinforced polymers (CFRPs) are exploited for the storage of compressed hydrogen and have to be monitored for safe and long-term operation. Since neither wired sensors nor wireless radio technology can be used inside these tanks, acoustic communication through the hull of the tank has been the subject of research in recent years. In this paper, we present for the first time a passive wireless sensor technology exploiting an ultrasonic communication channel through an electrically conductive transmission medium with an analog resonant sensor featuring a high quality factor. The instrumentation system comprised a readout unit outside and a passive sensor node inside the tank, coupled with geometrically opposing electromechanical transducers. The readout unit wirelessly excited a resonant sensor, whose temperature-dependent resonance frequency was extracted from the backscattered signal. This paper provides a description of the underlying passive sensor technology and characterizes the electric impedances and acoustic transmission as an electrical 2-Port to design a functional measurement setup. We demonstrated a wireless temperature measurement through a 10 mm CFRP plate in its full operable temperature range from -40 to 110 °C with a resolution of less than 1 mK.

Characterisation and Quantification of Upper Body Surface Motions for Tidal Volume Determination in Lung-Healthy Individuals.

Measurement of accurate tidal volumes based on respiration-induced surface movements of the upper body would be valuable in clinical and sports monitoring applications, but most current methods lack the precision, ease of use, or cost effectiveness required for wide-scale uptake. In this paper, the theoretical ability of different sensors, such as inertial measurement units, strain gauges, or circumference measurement devices to determine tidal volumes were investigated, scrutinised and evaluated. Sixteen subjects performed different breathing patterns of different tidal volumes, while using a motion capture system to record surface motions and a spirometer as a reference to obtain tidal volumes. Subsequently, the motion-capture data were used to determine upper-body circumferences, tilt angles, distance changes, movements and accelerations-such data could potentially be measured using optical encoders, inertial measurement units, or strain gauges. From these parameters, the measurement range and correlation with the volume signal of the spirometer were determined. The highest correlations were found between the spirometer volume and upper body circumferences; surface deflection was also well correlated, while accelerations carried minor respiratory information. The ranges of thorax motion parameters measurable with common sensors and the values and correlations to respiratory volume are presented. This article thus provides a novel tool for sensor selection for a smart shirt analysis of respiration.

Frequency Comb-Based Ground-Penetrating Bioradar: System Implementation and Signal Processing.

Radars can be used as sensors to detect the breathing of victims trapped under layers of building materials in catastrophes like earthquakes or gas explosions. In this contribution, we present the implementation of a novel frequency comb continuous wave (FCCW) bioradar module using a commercial software-defined radio (SDR). The FCCW radar transmits multiple equally spaced frequency components simultaneously. The data acquisition of the received combs is frequency domain-based. Hence, it does not require synchronization between the transmit and receive channels, as time domain-based broadband radars, such as ultra wideband (UWB) pulse radar and frequency-modulated CW (FMCW) radar, do. Since a frequency comb has an instantaneous wide bandwidth, the effective scan rate is much higher than that of a step frequency CW (SFCW) radar. This FCCW radar is particularly suitable for small motion detection. Using inverse fast Fourier transform (IFFT), we can decompose the received frequency comb into different ranges and remove ghost signals and interference of further range intervals. The frequency comb we use in this report has a bandwidth of only 60 MHz, resulting in a range resolution of up to 2.5 m, much larger than respiration-induced chest wall motions. However, we demonstrate that in the centimeter range, motions can be detected and evaluated by processing the received comb signals. We want to integrate the bioradar into an unmanned aircraft system for fast and safe search and rescue operations. As a trade-off between ground penetrability and the size and weight of the antenna and the radar module, we use 1.3 GHz as the center frequency. Field measurements show that the proposed FCCW bioradar can detect an alive person through different nonmetallic building materials.

Transit Time of Lamb Wave-Based Ultrasonic Flow Meters and the Effect of Temperature.

Transit-time flow meters need to compensate for cross-sensitivity to temperature. We show that Lamb wave-based setups are less affected by temperature. An optimality criterion is derived that allows to tune the meter into a zero local sensitivity to temperature. For this end, the flow-induced change in ultrasonic transit time is revisited first. While wetted piston transducer meters are directly sensitive to the change in propagation speed, the change in time of flight of Lamb wave-based systems is due to the beam displacement. Second, the effect of temperature is incorporated analytically. It is found that the temperature-dependent radiation angle of Lamb waves is able to compensate for changes in the speed of sound, leading to an (almost) unaffected overall time of flight. This effect is achievable with any fluid and in a wide temperature range. As an example, we discuss a water meter in the range from 0°C to 100°C. The model is validated against temperature and flow rate-dependent measurements obtained on a prototype. The measured data fits well to the developed model and confirms the reduced cross-sensitivity to temperature. Although an in-line meter is considered here, the results extend to clamp-on devices.

Influence of hyperparameter on the Untrue Prior Detection in Discrete Transformation-based EIT Algorithm.

Incorporated with a structural prior, discrete cosine transformation (DCT) based electrical impedance tomog-raphy (EIT) algorithm can improve the interpretability of EIT images in clinical settings. However, this benefit comes with a risk of the untrue prior which yields a misleading result compromising clinical decision. The redistribution index is able to detect an untrue prior by analysing EIT reconstructions. In addition to structural priors, EIT reconstruction is also affected by the choice of hyperparameter A in DCT-based EIT algorithm. In this research, influence of hyperparameter on untrue prior detection is investigated in terms of simulation experiment. A series of simulation settings consisting of 30 different atelectasis scales was conducted, then reconstructed with 20 different hyperparameters, to investigate the behavior of redistribution index. The result shows, despite the fact that redistribution index is indeed influenced by the choice of the hyperparameter A, the detection of an untrue prior is not significantly affected. The untrue prior detection is rather stable regardless of the optimal hyperparameter. Clinical Relevance - Optimal hyperparameter is not always guaranteed in clinical settings. This research confirms that the untrue prior detection is not strongly influenced by the hyperparameter. An update of untrue priors incorporated into EIT approach will facilitate a better interpretation of EIT results and an accurate clinical decision.

Estimation of Leaf Area Index with a Multi-Channel Spectral Micro-Sensor for Wireless Sensing Networks.

The leaf area index (LAI) is a key parameter in the context of monitoring the development of tree crowns and plants in general. As parameters such as carbon assimilation, environmental stress on carbon, and the water fluxes within tree canopies are correlated to the leaves surface, this parameter is essential for understanding and modeling ecological processes. However, its continuous monitoring using manual state-of-the-art measurement instruments is still challenging. To address this challenge, we present an innovative sensor concept to obtain the LAI based on the cheap and easy to integrate multi-channel spectral sensor AS7341. Additionally, we present a method for processing and filtering the gathered data, which enables very high accuracy measurements with an nRMSE of only 0.098, compared to the manually-operated state-of-the-art instrument LAI-2200C (LiCor). The sensor that is embedded on a sensor node has been tested in long-term experiments, proving its suitability for continuous deployment over an entire season. It permits the estimation of both the plant area index (PAI) and leaf area index (LAI) and provides the first wireless system that obtains the LAI solely powered by solar cells. Its energy autonomy and wireless connectivity make it suitable for a massive deployment over large areas and at different levels of the tree crown. It may be upgraded to allow the parallel measurement of photosynthetic active radiation (PAR) and light quality, relevant parameters for monitoring processes within tree canopies.

Behavioral Modeling of DC/DC Converters in Self-Powered Sensor Systems with Modelica.

DC/DC converters are the essential component of power management in applications such as self-powered systems. Their simulation plays an important role in the configuration, analysis and design. A major drawback is the lack of behavioral models for DC/DC converters for long-term simulations (days or months). Available models are cycle-to-cycle-based due to the switch-mode nature of the converters and are therefore not applicable. In this work, we present a new behavioral model of a DC/DC power converter. The model is based on a thorough discussion of the model aspects that are relevant for self-powered systems, such as electrical representation and the causal connection if input and output. The model implementation is shown in the Modelica language and is available as an open-source library. The highlights of the model are a feedback controller for operation at the maximum power point (MPP), a loss-based efficiency function, and the start/stop behavior. The model's capabilities are demonstrated in a 24h-experiment to predict voltage levels and the conversion efficiency.

Remote Photoacoustic Sensing Using Single Speckle Analysis by an Ultra-Fast Four Quadrant Photo-Detector.

The need for tissue contact makes photoacoustic imaging not applicable for special medical applications like wound imaging, endoscopy, or laser surgery. An easy, stable, and contact-free sensing technique might thus help to broaden the applications of the medical imaging modality. In this work, it is demonstrated for the first time that remote photoacoustic sensing by speckle analysis can be performed in the MHz sampling range by tracking a single speckle using a four quadrant photo-detector. A single speckle, which is created by self-interference of surface back-reflection, is temporally analyzed using this photo-detector. Phantoms and skin samples are measured in transmission and reflection mode. The potential for miniaturization for endoscopic application is demonstrated by fiber bundle measurements. In addition, sensing parameters are discussed. Photoacoustic sensing in the MHz sampling range by single speckle analysis with the four quadrant detector is successfully demonstrated. Furthermore, the endoscopic applicability is proven, and the sensing parameters are convenient for photoacoustic sensing. It can be concluded that a single speckle contains all the relevant information for remote photoacoustic signal detection. Single speckle sensing is therefore an easy, robust, contact-free photoacoustic detection technique and holds the potential for economical, ultra-fast photoacoustic sensing. The new detection technique might thus help to broaden the field of photoacoustic imaging applications in the future.

All-Oxide Varactor Electromechanical Properties Extracted by Highly Accurate Modeling Over a Broad Frequency and Electric Bias Range.

Since the dielectric permittivity of ferroelectric materials depends on the electric field, they allow designing switchable and continuously tunable devices for adaptive microwave front ends. Part of the ongoing research is the field of all-oxide devices, where epitaxial oxide conductors are used instead of polycrystalline metal electrodes, leading to epitaxial ferroelectric layers and resulting in high device performance. In particular, they allow engineering the acoustic properties separated from the electric ones due to the structural similarity between the dielectric and conducting oxide films. Two major results are reported in this work. First, a highly accurate model for the microwave impedance of ferroelectric varactors is derived that tracks the superposition of induced piezoelectricity and field extrusion into the substrate caused by thin electrodes. In difference to previous works, this model covers both a wide frequency and biasing range up to 12 GHz and 100 V/ [Formula: see text]. Second, the high model accuracy enables the determination of all relevant electric and mechanic properties based on a mere microwave characterization. This approach will be especially valuable when independent measurements of mechanical properties of the thin-film materials are impeded by a high integration of the devices. Though derived for all-oxide varactors, the presented model can as well be adapted for thin-film bulk acoustic wave resonators (FBARs) and varactors with conventional metal electrodes when eventual dead layers at the interface are modeled correctly.

Simultaneous Power Feedback and Maximum Efficiency Point Tracking for Miniaturized RF Wireless Power Transfer Systems.

Near-field interfaces with miniaturized coil systems and low output power levels, such as applied in biomedical sensor systems, can suffer from severe efficiency degradation due to dynamic impedance mismatches, reducing battery life of the power transmitter unit and requiring to increase the level of electromagnetic emission. Moreover, the stability of weakly-coupled power transfer systems is generally limited by transient changes in coil alignment and load power consumption. Hence, a central research question in the domain of wireless power transfer is how to realize an adaptive impedance matching system under the constraints of a simultaneous power feedback to increase the system's efficiency and stability, while maintaining circuit characteristics such as small size, low power consumption and fast reaction times. This paper presents a novel approach based on a two-stage control loop implemented in the primary-side reader unit, which uses a digital PI controller to maintain the rectifier output voltage for power feedback and an on-top perturb-and-observe controller configuring the setpoint of the voltage controller to maximize efficiency. The paper mathematically analyzes the AC and DC transfer characteristics of a resonant inductive link to design the reactive AC matching network, the digital voltage controller and ultimately the DC-domain impedance matching algorithm. It was found that static reactive L networks result in suitable efficiency levels for coils with sufficiently high quality factor even without adaptive tuning of operational frequency or reactive components. Furthermore, the regulated output voltage of the rectifier is a direct measure of the DC load impedance when using a regular DC/DC converter to supply the load circuits, so that this quantity can be tuned to maximize efficiency. A prototype implementation demonstrates the algorithms in a 40.68 MHz inductive link with load power levels from 10 to 100 mW and tuning time constants of 300 ms, while allowing for a simplified receiver with a footprint smaller than 200 mm2 and a self-consumption below 1 mW. Hence, the presented concepts enable adaptive impedance matching with favorable characteristics for low-energy sensor systems, i.e., minimized footprint, power level and reaction time.

Quantitative Determination of Local Density of Iron Oxide Nanoparticles Used for Drug Targeting Employing Inverse Magnetomotive Ultrasound.

Numerous medical applications make use of magnetic nanoparticles, which increase the demand for imaging procedures that are capable of visualizing this kind of particle. Magnetomotive ultrasound (MMUS) is an ultrasound-based imaging modality that can detect tissue, which is permeated by magnetic nanoparticles. However, currently, MMUS can only provide a qualitative mapping of the particle density in the particle-loaded tissue. In this contribution, we present an enhanced MMUS procedure, which enables an estimation of the quantitative level of the local nanoparticle concentration in tissue. The introduced modality involves an adjustment of simulated data to measurement data. To generate these simulated data, the physical processes that arise during the MMUS imaging procedure have to be emulated which can be a computing-intensive proceeding. Since this considerable calculation effort may handicap clinical applications, we further present an efficient approach to calculate the decisive physical quantities and a suitable way to adjust these simulated quantities to the measurement data with only moderate computational effort. For this purpose, we use the result data of a conventional MMUS measurement and the knowledge on the magnetic field quantities and on the mechanical parameters describing the biological tissue, namely, the density, the longitudinal wave velocity, and the shear wave velocity. Experiments on tissue-mimicking phantoms demonstrate that the presented technique can indeed be utilized to determine the local nanoparticle concentration in tissue quantitatively in the correct order of magnitude. By investigating test phantoms of simple geometry, the mean particle concentration of the particle-laden area could be determined with less than 22% deviation to the nominal value.