Machine learning enhanced evaluation of semiconductor quantum dots.

A key challenge in quantum photonics today is the efficient and on-demand generation of high-quality single photons and entangled photon pairs. In this regard, one of the most promising types of emitters are semiconductor quantum dots, fluorescent nanostructures also described as artificial atoms. The main technological challenge in upscaling to an industrial level is the typically random spatial and spectral distribution in their growth. Furthermore, depending on the intended application, different requirements are imposed on a quantum dot, which are reflected in its spectral properties. Given that an in-depth suitability analysis is lengthy and costly, it is common practice to pre-select promising candidate quantum dots using their emission spectrum. Currently, this is done by hand. Therefore, to automate and expedite this process, in this paper, we propose a data-driven machine-learning-based method of evaluating the applicability of a semiconductor quantum dot as single photon source. For this, first, a minimally redundant, but maximally relevant feature representation for quantum dot emission spectra is derived by combining conventional spectral analysis with an autoencoding convolutional neural network. The obtained feature vector is subsequently used as input to a neural network regression model, which is specifically designed to not only return a rating score, gauging the technical suitability of a quantum dot, but also a measure of confidence for its evaluation. For training and testing, a large dataset of self-assembled InAs/GaAs semiconductor quantum dot emission spectra is used, partially labelled by a team of experts in the field. Overall, highly convincing results are achieved, as quantum dots are reliably evaluated correctly. Note, that the presented methodology can account for different spectral requirements and is applicable regardless of the underlying photonic structure, fabrication method and material composition. We therefore consider it the first step towards a fully integrated evaluation framework for quantum dots, proving the use of machine learning beneficial in the advancement of future quantum technologies.

Compensating the Influence of Tremors on Impedance Measurements Through Fourier Analysis.

Electrical mpedance measurements are a promising method for detecting structural changes in tissue and can be used in oncology to differentiate between healthy and tumorous tissue areas. The impedance measurements are so sensitive that they are not only affected by changes in the tissue itself, but also by a fluctuating contact force between sensor and tissue. In this work, the correlation between impedance measurements and movements during the measuring process, such as physiological tremors, are analyzed. To do this, impedance measurements are taken on pig bladders and the sensor-tissue contact force is simultaneously recorded. The tremor frequencies are directly visible in the Fourier transform of the impedance measurement. To counteract these effects, a Butterworth filter is used to filter out tremor frequencies and remove unwanted artefacts. Additionally, placing an spring on top of the impedance sensor helped to achieve a steadier contact force between sensor and tissue to also remove low frequency disturbances in the impedance measurements.Clinical relevance- This approach can help to obtain more reliable impedance measurements on tissue both for ex vivo and in vivo applications.

An Enhanced Synthetic Cystoscopic Environment for Use in Monocular Depth Estimation.

As technology advances and sensing devices improve, it is becoming more and more pertinent to ensure accurate positioning of these devices, especially within the human body. This task remains particularly difficult during manual, minimally invasive surgeries such as cystoscopies where only a monocular, endoscopic camera image is available and driven by hand. Tracking relies on optical localization methods, however, existing classical options do not function well in such a dynamic, non-rigid environment. This work builds on recent works using neural networks to learn a supervised depth estimation from synthetically generated images and, in a second training step, use adversarial training to then apply the network on real images. The improvements made to a synthetic cystoscopic environment are done in such a way to reduce the domain gap between the synthetic images and the real ones. Training with the proposed enhanced environment shows distinct improvements over previously published work when applied to real test images.

In-plane Strain Analysis by Correlating Geometry and Visual Data Through a Gradient-Based Surface Reconstruction.

Abnormalities in tissue can be detected and analyzed by evaluating mechanical properties, such as strain and stiffness. While current sensor systems are effective in measuring longitudinal properties perpendicular to the measurement sensor, identifying in-plane deformation remains a significant challenge. To address this issue, this paper presents a novel method for reconstructing in-plane deformation of observed tissue surfaces using a fringe projection sensor specifically designed for measuring tissue deformations. The method employs the latest techniques from computer vision, such as differentiable rendering, to formulate the in-plane reconstruction as a differentiable optimization problem. This enables the use of gradient-based solvers for an efficient and effective optimization of the problem optimum. Depth information and image information are combined using landmark correspondences between the respective image observations of the undeformed and deformed scenes. By comparing the reconstructed pre- and post-deformation geometry, the in-plane deformation can be revealed through the analysis of relative variations between the corresponding models' geometries. The proposed reconstruction pipeline is validated on an experimental setup, and the potential for intraoperative applications is discussed.

Cystoscopic depth estimation using gated adversarial domain adaptation.

Monocular depth estimation from camera images is very important for surrounding scene evaluation in many technical fields from automotive to medicine. However, traditional triangulation methods using stereo cameras or multiple views with the assumption of a rigid environment are not applicable for endoscopic domains. Particularly in cystoscopies it is not possible to produce ground truth depth information to directly train machine learning algorithms for using a monocular image directly for depth prediction. This work considers first creating a synthetic cystoscopic environment for initial encoding of depth information from synthetically rendered images. Next, the task of predicting pixel-wise depth values for real images is constrained to a domain adaption between the synthetic and real image domains. This adaptation is done through added gated residual blocks in order to simplify the network task and maintain training stability during adversarial training. Training is done on an internally collected cystoscopy dataset from human patients. The results after training demonstrate the ability to predict reasonable depth estimations from actual cystoscopic videos and added stability from using gated residual blocks is shown to prevent mode collapse during adversarial training.

Enhancing Tissue Impedance Measurements Through Modeling of Fluid Flow During Viscoelastic Relaxation.

OBJECTIVE: Bladder cancer recurrence is an important issue after endoscopic urological surgeries. Additional sensor information such as electrical impedance measurements aim to support surgeons to ensure that the entirety of the tumor is removed. The foundation for differentiating lies in the altered sodium contents and cell structures within tumors that change their conductivity and permittivity. Mechanical deformations in the tissue expel fluid from the compressed area and pose a great difficulty, as they also lead to impedance changes. It is crucial to determine if this effect outweighs the alterations due to the tumorous tissue properties. METHODS: Impedance measurements under ongoing viscoelastic relaxation are taken on healthy and tumorous tissue samples from human bladders and breasts. A fluid model to account for extra- and intracellular fluid flow under compression is derived. It is based on the fluid content within the individual tissue compartments and their outflow via diffusion. RESULTS: After an initial deformation, the tissue relaxes and the impedance increases. The proposed model accurately represents these effects and validates the link between fluid flow under mechanical deformation and its impact on tissue impedance. A method to compensate for these undesired effects of fluid flow is proposed and the measurements are assessed in terms of differentiability between tumorous and healthy tissue samples. CONCLUSION: The electrical parameters are found to be promising for differentiation even under varying mechanical deformation, and the distinction is additionally improved by the proposed compensation approach. SIGNIFICANCE: Electrical impedance measurements show great potential to support urologist during endoscopic surgeries.

Detection Limits of Tetrapolar Impedance Sensors for Tissue Differentiation.

Cancer recurrence is an important issue in bladder tumor resections, because tissue cannot generously be removed from the thin bladder wall without impacting its functionality. Electrical impedance measurements during an operation aim to support the surgeon in making the decision which tissue areas to preserve, because physiological changes in tissue due to cancerous mutations can be detected by their altered electrical characteristics. This work investigates the detection limits of tetrapolar sensors when the impedance of heterogeneous tissue is measured. To do this, a finite element analysis is carried out where the sensors are placed on a dielectric medium with inclusions of different sizes, conductivity, and locations relative to the sensor. It is shown that a sensor with four electrodes in a square performs poorly in comparison to a sensor where the electrodes are symmetrically shaped as rings around one center electrode. This is mainly due to its enlarged regions of negative sensitivity. Based on the results, a third, optimized sensor geometry is proposed that shows superior performance to the other sensors in terms of geometry factor, sensitivities, and tumor detection. In simulation, it can reliably detect tumors with only half the radius of the sensor surface. Smaller tumor fractions cannot be detected by either sensor.

Geometric Mapping Evaluation for Real-Time Local Sensor Simulation.

Medical augmented reality and simulated test environments struggle in accurately simulating local sensor measurements across large spatial domains while maintaining the proper resolution of information required and real time capability. Here, a simple method for real-time simulation of intraoperative sensors is presented to aid with medical sensor development and professional training. During a surgical intervention, the interaction between medical sensor systems and tissue leads to mechanical deformation of the tissue. Through the inclusion of detailed finite element simulations in a real-time augmented reality system the method presented will allow for more accurate simulation of intraoperative sensor measurements that are independent of the mechanical state of the tissue. This concept uses a coarse, macro-level deformation mesh to maintain both computational speed and the illusion of reality and a simple geometric point mapping method to include detailed fine mesh information. The resulting system allows for flexible simulation of different types of localized sensor measurement techniques. Preliminary simulation results are provided using a real-time capable simulation environment and prove the feasibility of the method.

2D to 3D Segmentation: Inclusion of Prior Information using Random Walk Kalman Filters.

Augmented reality is a quickly advancing field that has the potential to provide surgeons with computer generated diagnostic results during surgery. Visual classification of diseased tissue generated during a diagnostic procedure, for example, trans-urethral cystoscopy of the urinary bladder, can aid a surgeon during the following resection to ensure no tissue is inadvertently missed. Work with 2D segmentation of camera images is well developed and frameworks already exist to fuse this data real-time in a 3D reconstruction. These existing frame-works, however, maintain only the most recent segmentation information when building the 3D reconstruction. This work proposes a method to build a 3D point cloud classification using random walk Kalman filters. The method enables retention of prior classification information and additionally provides a framework to include additional sensor classifications contributing to a single, final 3D segmentation result. The method is demonstrated using a simulated environment intended to emulate the inside of a human bladder.

Multi-Physical Tissue Modeling of a Human Urinary Bladder.

A multi-physical model of a human urinary bladder is an essential element for the potential application of electrical impedance spectroscopy during transurethral resection surgery, where measurements are taken at different fill levels inside the bladder. This work derives a multi-physical bladder tissue model that incorporates the electrical impedance properties with dependence on mechanical deformation due to filling of the bladder. The volume and ratio of the intracellular to extracellular tissue fluid heavily influence the electrical impedance characteristics and thus provide the connection between the mechanical and electrical domains. Modeling the fluid within the tissue links both the physical and histological processes and enables useful inferences of the properties from empiric observations. This is demonstrated by taking impedance measurements at different fill volumes. The resulting model provides a tool to analyze impedance measurements during surgery at different stress levels. In addition, this model can be used to determine patient-specific tissue parameters.

Geometry Factor Determination for Tetrapolar Impedance Sensor Probes.

Even after successful tumor resection, cancer recurrence remains an important issue for bladder tumors. Intra-operative tissue differentiation can help for diagnostic purposes as well as for ensuring that all cancerous cells are completely removed, therefore, decreasing the risk of recurrence. It has been shown that the electrical properties of tumors differ from healthy tissue due to an altered physiology. This work investigates three sensor configurations to measure the impedance of tissue. Each relies on a four terminal measurement and has a distinct electrode arrangement either inline or as a square. Analytical expressions to calculate the geometry factor of each sensor based on Laplace's equation are derived. The results are verified experimentally and in a finite element simulation. Furthermore, several measurements on pig bladders, both fresh and from frozen storage, are carried out with each sensor.It is shown that the calculated and simulated geometry factors yield the same results and are suitable and uncomplicated methods to determine the geometry factor without an experimental setup. These methods also allow for sensor optimization by knowing the measured potentials before the actual fabrication of the sensor. Moreover, conductivity values close to listed data are obtained for pig bladders, which validates the sensors. Ultimately, the square electrode configuration turns out to be a valid option for minimally invasive sensors, which are necessary for the envisaged application of transurethral bladder cancer diagnostics and surgery. This arrangement both assures reliable data and allows for easier miniaturization than the inline electrode placement.

Machine Learning Methods of Regression for Plasmonic Nanoantenna Glucose Sensing.

The measurement and quantification of glucose concentrations is a field of major interest, whether motivated by potential clinical applications or as a prime example of biosensing in basic research. In recent years, optical sensing methods have emerged as promising glucose measurement techniques in the literature, with surface-enhanced infrared absorption (SEIRA) spectroscopy combining the sensitivity of plasmonic systems and the specificity of standard infrared spectroscopy. The challenge addressed in this paper is to determine the best method to estimate the glucose concentration in aqueous solutions in the presence of fructose from the measured reflectance spectra. This is referred to as the inverse problem of sensing and usually solved via linear regression. Here, instead, several advanced machine learning regression algorithms are proposed and compared, while the sensor data are subject to a pre-processing routine aiming to isolate key patterns from which to extract the relevant information. The most accurate and reliable predictions were finally made by a Gaussian process regression model which improves by more than 60% on previous approaches. Our findings give insight into the applicability of machine learning methods of regression for sensor calibration and explore the limitations of SEIRA glucose sensing.

Precise building deformation measurement using holographic multipoint replication.

For the active control of large-scale structures, especially high-rise buildings and bridges, fast and accurate measurement of local deformations is required. We present a highly accurate and fast vision-based measurement technique and, to the best of our knowledge, first experimental results for the control of an adaptive-structures prototype frame, equipped with hydraulic actuators. Deformations are detected at multiple discrete points, based on a photogrammetric approach with additional holographic spot replication. The replication leads to effective averaging of most error contributions, especially discretization and photon noise. Measurements over a distance of 11.4 m result in a measurement uncertainty of 0.0077 pixel (corresponding to 0.055 mm in object space).

Adaptive Method for Quantitative Estimation of Glucose and Fructose Concentrations in Aqueous Solutions Based on Infrared Nanoantenna Optics.

In life science and health research one observes a continuous need for new concepts and methods to detect and quantify the presence and concentration of certain biomolecules-preferably even in vivo or aqueous solutions. One prominent example, among many others, is the blood glucose level, which is highly important in the treatment of, e.g., diabetes mellitus. Detecting and, in particular, quantifying the amount of such molecular species in a complex sensing environment, such as human body fluids, constitutes a significant challenge. Surface-enhanced infrared absorption (SEIRA) spectroscopy has proven to be uniquely able to differentiate even very similar molecular species in very small concentrations. We are thus employing SEIRA to gather the vibrational response of aqueous glucose and fructose solutions in the mid-infrared spectral range with varying concentration levels down to 10 g/l. In contrast to previous work, we further demonstrate that it is possible to not only extract the presence of the analyte molecules but to determine the quantitative concentrations in a reliable and automated way. For this, a baseline correction method is applied to pre-process the measurement data in order to extract the characteristic vibrational information. Afterwards, a set of basis functions is fitted to capture the characteristic features of the two examined monosaccharides and a potential contribution of the solvent itself. The reconstruction of the actual concentration levels is then performed by superposition of the different basis functions to approximate the measured data. This software-based enhancement of the employed optical sensors leads to an accurate quantitative estimate of glucose and fructose concentrations in aqueous solutions.

Vibrational Sensing Using Infrared Nanoantennas: Toward the Noninvasive Quantitation of Physiological Levels of Glucose and Fructose.

Monosaccharides, which include the simple sugars such as glucose and fructose, are among the most important carbohydrates in the human diet. Certain chronic diseases, e.g., diabetes mellitus, are associated with anomalous glucose blood levels. Detecting and measuring the levels of monosaccharides in vivo or in aqueous solutions is thus of the utmost importance in life science, health, and point-of-care applications. Noninvasive sensing would avoid problems such as pain and potential infection hazards. Here, with the help of surface enhanced infrared absorption (SEIRA) spectroscopy, we demonstrate the reliable optical detection in the mid-infrared spectral range of pure glucose and fructose solutions as well as mixtures of both in aqueous solution. We utilize a reflection flow cell geometry with physiologically relevant concentrations as small as 10 g/L. As significant improvement over the standard baseline correction employed in SEIRA applications, we utilize principal component analysis (PCA) as machine learning algorithm, which is ideally suited for the extraction of vibrational data. We anticipate our results as important step in biosensing applications that will stimulate efforts to further improve the employed SEIRA substrates, the noise level of the spectroscopic light source, as well as the flow cell environment en route to significantly higher sensitivities and quantitative analysis, even in tear drops.

A novel finite element model-based navigation system-supported workflow for breast tumor excision.

In the case of female breast cancer, a breast-conserving excision is often desirable. This surgery is based on preoperatively gathered MRI, mammography, and sonography images. These images are recorded in multiple patient positions, e. g., 2D mammography images in standing position with a compressed breast and 3D MRI images in prone position. In contrast, the surgery happens in supine or beach chair position. Due to these different perspectives and the flexible, thus challenging, breast tissue, the excision puts high demands on the physician. Therefore, this publication presents a novel eight-step excision support workflow that can be used to include information captured preoperatively through medical imaging based on a finite element (FE) model. In addition, an indoor positioning system is integrated in the workflow in order to track surgical devices and the sonography transducer during surgery. The preoperative part of the navigation system-supported workflow is outlined exemplarily based on first experimental results including 3D scans of a patient in different patient positions and her MRI images. Graphical Abstract Finite Element model based navigation system supported workflow for breast tumor excision is based on eight steps and allows inclusion of information from medical images recorded in multiple patient positions.

Classification of postprandial glycemic patterns in type 1 diabetes subjects under closed-loop control: an in silico study.

In this contribution we explore some alternatives in order to obtain filtered and low dimension CGM data to provide well processed CGM data to AP systems. The presented approach explores the possible association of certain patient behaviors with certain glucose patterns. We compare the classical clustering algorithms (K-means, and fuzzy C-means), which has shown some limitations for CGM data processing, with a new clustering algorithm (K-means ellipsoid algorithm) more suited to CGM data. We test this new algorithm in a variety of complex scenarios including variabilty in the amount of ingested carbohydrates, absorption time and intrapatient parameters. The new algorithm overcomes the perceived problems and is able to discriminate between normoglycaemic, moderate and severe hyperglycaemic post-prandial behaviour, even with similar amounts of carbohydrates contained in a meal.

Finite Element Breast Simulation for Sonography Image Registration.

In case of female breast cancer, a breast conserving excision is often necessary. For this purpose, information from multiple medical imaging techniques have to be combined. Sonography imaging is essential for dense breast tissue and the only medical imaging technique available during surgery. During sonography of the outer breast quadrants the woman is usually in contralateral posterior oblique position, being in supine orientation while holding her ipsilateral arm over the head. Thus, these images cannot be directly registered with MRI or mammography images because these imaging technologies are performed in other patient positions with hands on the side of the body. Thus, we present a novel Finite Element approach how to enable a sonography image registration by showing the first time how to transfer the supine position with the arm straight on side into a supine position with the ipsilateral arm over the head which can be used to include information from MRI or mammography images. This approach is shown and validated with 3D scanner breast surface data as proof of concept. When comparing the simulation result with a 3D surface scan in supine orientation with the arm over the head, a mean surface distance error of 1.57 mm is achieved.

In Vitro Monitoring Conformational Changes of Polypeptide Monolayers Using Infrared Plasmonic Nanoantennas.

Proteins and peptides play a predominant role in biochemical reactions of living cells. In these complex environments, not only the constitution of the molecules but also their three-dimensional configuration defines their functionality. This so-called secondary structure of proteins is crucial for understanding their function in living matter. Misfolding, for example, is suspected as the cause of neurodegenerative diseases such as Alzheimer's and Parkinson's disease. Ultimately, it is necessary to study a single protein and its folding dynamics. Here, we report a first step in this direction, namely ultrasensitive detection and discrimination of in vitro polypeptide folding and unfolding processes using resonant plasmonic nanoantennas for surface-enhanced vibrational spectroscopy. We utilize poly-l-lysine as a model system which has been functionalized on the gold surface. By in vitro infrared spectroscopy of a single molecular monolayer at the amide I vibrations we directly monitor the reversible conformational changes between α-helix and ß-sheet states induced by controlled external chemical stimuli. Our scheme in combination with advanced positioning of the peptides and proteins and more brilliant light sources is highly promising for ultrasensitive in vitro studies down to the single protein level.

Biocatalytic spectrophotometric method to detect paracetamol in water samples.

A biocatalytic methodology based on the quantification of the laccase inhibition during the oxidation of a standard substrate ABTS (2,2'-azino-bis(3-ethylbenzothiazoline-6-sulphonic acid) for the indirect determination of paracetamol in drinking water has been developed. The method displayed a fast response time (20 s), and high selectivity to paracetamol in presence of interfering substances such as naproxen, estradiol, ketoprofen, sulfamethoxazole, and diclofenac. The limit of detection (LOD) and limit of quantification (LOQ) were noticed to be 0.55 µM and 8.3 µM, respectively. By comparing the catalytic constants value KM and kcat for ABTS oxidation in the absence and presence of various concentrations of paracetamol, a competitive-type inhibition was disclosed. On the other hand, the close value between Ki and KM indicates similar binding affinity of the enzyme to ABTS and paracetamol corroborated by docking studies. The methodology was successfully applied to real water samples, presenting an interesting potential for further development of a biosensor to paracetamol detection.