bluuebunny/crossref_metadata_2025_split huggingface dataset:
Visualization and Parametrization of the Motion Behaviour of Subviral Particles
Authors: Kaak Michelle , Rausch Andreas , Müller Dennis , Schanze Thomas 
Published: September 2018

Abstract
The development of a drug against pathogens of hemorrhagic fever, like the Marburg virus, is a great challenge. Therefore, accurate knowledge of the properties of subviral particles in the host cell must be obtained. The base for subviral particle analysis is a special fluorescence microscopy technique. In order to automate and visualize the subviral particles’ motion patterns, previously a tracking algorithm was developed. In this article a new algorithm to parameterize and visualize the achieved particle tracks is introduced. A good potential for a fast data recognition is shown, with constantly respecting a high usability for pharmaceutical researchers. This algorithm was tested on both simulated and real data and provides reproducible results.
Magnetoneurograhy of an Electrically Stimulated Arm Nerve
Authors: Elzenheimer Eric , Laufs Helmut , Sander-Thömmes Tilmann , Schmidt Gerhard 
Published: September 2018

Abstract
For further development and optimization of novel uncooled magnetoelectric (ME) sensors, a better understanding of spectral power distribution and signal strength of nerve signals is of high interest. For obtaining information on these signal properties, Superconducting Quantum Interference Device (SQUID) measurements were performed at the Physikalisch-Technische Bundesanstalt (PTB) in Berlin. Signal amplitudes were subject-dependent and ranged from 17 fT to 60 fT in a frequency range from 100 Hz to 1 kHz. The required SQUID averaging time was in the range of minutes, while for current ME sensors significantly longer averaging times are expected to be necessary.
Measuring of calcification risk with polymer microchips
Authors: Bavendiek Julia , Sackmann Johannes , Schomburg Werner Karl , Gräber Steffen , Jahnen-Dechent Wilhelm , Pasch Andreas 
Published: September 2018

Abstract
It has been shown that the formation of calcium phosphate crystals can be detected in patient blood using a polymer microchip manufactured by ultrasonic processing. Ultrasonic processing is recently evolving for the fabrication of low-cost microfluidic devices from thermoplastic polymers. The formation of calcium phosphate crystals can be measured in the blood serum of a patient both optically from a change in turbidity or in electrical resistance.
Dynamic Time Warping of Pulse Wave Curves
Authors: Pielmuş Alexandru-Gabriel , Osterland Dennis , Tigges Timo , Klum Michael , Orglmeister Reinhold , Feldheiser Aarne , Hunsicker Oliver 
Published: September 2018

Abstract
Being able to non-obtrusively and continuously monitor arterial blood pressure is of great interest, particularly in the context of wearable sensors. A common limitation is the need for dedicated hardware, which is either obtrusive or expensive. In our current work, we investigate unimodal pulse waves from three handily accessible heterogeneous sources: photoplethysmography, bioimpedance and pulse applanation tonometry. We derive and evaluate multiple parameters regarding their correlation to reference blood pressure curves. These parameters stem from features of the warping paths resulting from dynamic and derivative dynamic time warping. The warping is performed between adjacent pulses or to a reference waveform. Spearman Rho coefficients of up to 0.98 and averaging 0.77 at highly significant p-values are recorded for single parameters. We record mean absolute deviation values of 0.08 across subjects. The results indicate there are negligible lags between reference and parameter curves. The sign of the correlation coefficients is consistent only for a small subset of parameters; the underlying cause could not yet be identified. We conclude that the warping path approach seems a promising way to go, yet still needs refinement. In particular, developing a time and amplitude warping method is paramount. Since warping quantizes all the morphological changes in the pulse wave without fiducial point detection, it could become a powerful tool for future investigations.
Using singular value decomposition for generalized linear autoregression of signals
Authors: Schanze Thomas 
Published: September 2018

Abstract
Autoregressive models (AR) are fundamental for analysis, representation, and prediction of signals. AR modelling uses the premise that past signal values influence current ones. This influence is causal and is modelled as a linear superposition, because a weighted addition of past values is used. The calculation of the required linear superposition parameters or weights can be done by the classical Yule-Walker approach or by least squares procedures. Here we show how to use singular value decomposition (SVD) for generalized linear autoregression (GLAR), i.e. using SVD to compute the weights of a linear combination of functions of given signal values and to check or optimize the GLAR model. The GLAR approach opens the possibility to take directly into account non-linear influences from past to current signal values. T he potential of this approach for analysis and representation is presented and demonstrated for simulated signals, i.e. pure and noisy sequences of non-linear recursions, and biomedical signals.
Monte-Carlo parameter variation study of cardiovascular pathologies to quantify parameter specific signal uncertainty
Authors: Krickl Stefan , Bernhard Stefan 
Published: September 2018

Abstract
Cardiovascular diseases are the leading cause of death worldwide. Early detection of abnormal vascular morphologies like aneurysms in the abdominal (abdominal aortic aneurysm, AAA) or thoracic aorta (thoracic aortic aneurysm, TAA) are essential to prevent fatal events. The aim of this study is the development of a patient-specific simulation model to obtain statistical information about ab- /normal pressure-flow conditions to improve the basic understanding and methods for the early detection of diseases. For this purpose, the numerical cardiovascular modeling tool SISCA was used, to generate a series of simulations by Monte- Carlo parameter variation. The considered variational scenario was built upon a control group of normal patients, deriving two pathological conditions for AAA and TAA with different severity and location. Therefore, the nominal diameters were enlarged between 200 % and 500 %, while the length of the aneurysms were modified within a range of 30 and 90 mm. Within each statistical set the convergence was tested by the bootstrap method ensuring that within a set of 3500 runs a 2 % deviation error of the mean value of the blood pressure was obtained compared to a set of 10000 runs.The parameter variation method allows the generation of disease specific data in the context of physiological/clinical findings and consequently the disease specific quantification of signal uncertainties and variances.
High-density Mapping Reveals Short-term Reversibility of Atrial Ablation Lesions
Authors: Huhn Oliver , Pollnow Stefan , Unger Laura , Dössel Olaf , Oesterlein Tobias , Luik Armin , Schmitt Claus 
Published: September 2018

Abstract
Cardiac arrhythmias such as atrial fibrillation occur frequently in industrialized countries. Radiofrequency ablation (RFA) is a standard treatment if drug therapy fails. This minimally invasive surgery aims at stabilizing the heart rhythm on a permanent basis. However, the procedure commonly needs to be repeated because of the high recurrence rate of arrhythmias. Non-transmural lesions as well as gaps within linear lesions are among the main problems during the RFA. The assessment of lesion formation is not adequate in state of the art procedures. Therefore, the aim of this study is to investigate the short-term reversibility of lesions using human electrograms recorded by a high-density mapping system during an electrophysiological study (EPS). A predefined measurement protocol was executed during the EPS in order to create three ablation points in the left atrium. Subsequently, after preprocessing the recorded signals, electrogram (EGM) paths were formed along the endocardial surface of the atrium. By analyzing changes of peak to peak amplitudes of unipolar EGMs before and after ablation, it was possible to distinguish lesion area and healthy myocardium. The peak to peak amplitudes of the EGMs decreased by 40-61% after 30 seconds of ablation. Furthermore, we analyzed the morphological changes of EGMs surrounding the lesion. High-density mapping data showed that not only the tissue, which had direct contact with the catheter tip during the RFA, but also the surrounding tissue was affected. This was demonstrated by low peak to peak amplitudes in large areas with a width of 14 mm around the center of the ablation lesion. After right pulmonary vein isolation, high-density mapping was repeated on the previous lesions. The outer region of RFA-treated tissue appears to recover as opposed to the central core of the ablation point. This observation suggests that the meaningfulness of an immediate remap after ablation during an EPS may lead the physician to false conclusions
Seizure Prediction by Multivariate Autoregressive Model Order Optimization
Authors: Mühlberg Katja , Müller Jens , Tetzlaff Ronald 
Published: September 2018

Abstract
For several decades, researchers are aiming for the detection of precursors of epileptic seizures. A system that is able to issue a warning about an impending seizure could dramatically improve the quality of life of affected patients. In this work, we apply multivariate autoregressive (MVAR) modeling to intracranial electroencephalography (iEEG) recordings of patients with therapy resistant epilepsy. As compared to our previous investigations, we studied the optimal model order of the autoregressive process as a feature for seizure prediction. In a statistical evaluation, we obtain significant results for 17 out of 20 patients.
Hyperspectral based discrimination of thyroid and parathyroid during surgery
Authors: Barberio Manuel , Maktabi Marianne , Gockel Ines , Rayes Nada , Jansen-Winkeln Boris , Köhler Hannes , Rabe Sebastian M. , Seidemann Lena , Takoh Jonathan P. , Diana Michele , Neumuth Thomas , Chalopin Claire 
Published: September 2018

Abstract
Unintended injuring of anatomical structures during endocrine neck operations can have severe consequences for patient. Especially the nerves and the parathyroid gland can be hard to identify visually. Therefore, intraoperative methods are needed to support the surgeon in this task. Hyperspectral imaging (HSI) is a new approach in the medical area which combines a camera with a spectrometer. It showed promising results for the discrimination of tissue. In this work, HSI-data of seven patients were acquired during thyroid and parathyroid operations. The mean absorbance spectra of both glands showed differences in the range between 600 and 700 nm and at 760 and 960 nm. This means that thyroid and parathyroid have different oxygenation states and different contents of deoxygenated hemoglobin and water. From these observations, it is possible to define spectral signatures to characterize both glands. We showed on one patient how spectral signatures can be used in classification algorithms to automatically identify the thyroid and parathyroid from other structures.
Minimally invasive mastoidectomy approach using a mouldable surgical targeting system
Authors: Rau Thomas S. , Witte Sina , Uhlenbusch Lea , Lexow Jakob , Hügl Silke , Majdani Omid , Lenarz Thomas 
Published: September 2018

Abstract
Hearing restoration using a cochlear implant requires a surgical access to the inner ear. In order to enhance patient safety, reduce trauma, and shorten the patient’s time under anaesthesia current research focusses on minimally invasive cochlear implantation surgery by drilling only a single bore hole. This demands a highly accurate surgical assistance device to guide the drill along a predetermined trajectory planned in patient’s image data. In this study a recently developed surgical targeting system was evaluated for the first time in a human cadaver trial. After screwing a reference frame on a temporal bone specimen and imaging of both, a trajectory through the facial recess was planned in order to reach the middle ear. Based on this plan a patient specific surgical template including a linear guide for the surgical drill was composed utilizing bone cement. After the hardening of the bone cement the surgical template was mounted on top of the reference frame. The drilling could be performed as previously planned without harming facial nerve and chorda tympani. The deviation of the actual drill hole to the planned trajectory was 0.17 mm at the level of the facial recess. The minimal distance of the drill hole to the facial nerve was 0.59 mm. This proof-of-concept study demonstrates the feasibility of performing the access to the middle ear in a minimally invasive manner using the mouldable surgical targeting system. The presented process allows the patient specific individualization of a drill guide under sterile conditions. This might facilitate its integration into clinical routine.
Modular design of versatile surgical mini-robots
Authors: Theisgen Lukas , de la Fuente Matías , Radermacher Klaus 
Published: September 2018

Abstract
Surgical robots have been introduced in the field of Computer Assisted Surgery (CAS) to assist the surgeon by providing an accurate link between the computer-based plan and the exact (a) positioning or (b) dynamic path control of an instrument on the operating site respectively. Whereas initial systems mostly have been based on an active supervisory control scheme of industrial robots with large universal workspaces, later on specialized miniaturized kinematics have been proposed, with restricted workspaces adapted to specific applications in order to ease handling and provide inherent safety properties. However, this specialization resulted in even narrower fields of application, low quantities and higher costs. Modularization seems to be a key factor to combine the benefits of both approaches. In this paper two modular robotic solutions, validated in the context of their purpose, are analysed regarding their modular design. Based on this, the potential of modularity for instrument guiding tasks in surgery is discussed.
Evaluating Convolutional Neural Network and Hidden Markov Model for Recognising Surgical Phases in Sigmoid Resection
Authors: Jalal Nour Aldeen , Abdulnaki Alshirbaji Tamer , Möller Knut 
Published: September 2018

Abstract
Surgical workflow analysis in laparoscopic surgeries has been studied widely during last years because of its various applications. For example, optimising the schedule of operating rooms (OR) and developing a context-aware system that supports surgical team during the intervention. Surgical phase recognition has been applied to various kinds of laparoscopic procedures, mainly of type cholecystectomy. Sigmoid resection procedures are considered more complex than cholecystectomy, and they have not been extensively studied. Therefore, the focus of this work is to study phase recognition in sigmoid resection. In this paper, a convolutional neural network (CNN) architecture and Hidden Markov Model (HMM) were evaluated for performing phase recognition in sigmoid resection videos. The CNN is an extension of a pretrained model, and it was fine-tuned to perform the recognition. To consider the temporal aspect of the phase sequences, confidences obtained by the CNN were then provided into a HMM to release final classification. Experimental results show a low performance of the proposed method to recognise surgical phases in such complex procedures. Therefore, the dataset used for the evaluation was also reviewed, and statistics of each phase were generated.
Detecting Signatures in Hyperspectral Image Data of Wounds: A Compound Model of Self- Organizing Map and Least Square Fitting
Authors: Mohammed Redwan Abdo A. , Schäle Daniel , Hornberger Christoph , Emmert Steffen 
Published: September 2018

Abstract
The purpose of this study is to develop a method to discriminate spectral signatures in wound tissue. We have collected a training set of the intensity of the remitted light for different types of wound tissue from different patients using a TIVITA™ tissue camera. We used a neural network technique (self-organizing map) to group areas with the same spectral properties together. The results of this work indicates that neural network models are capable of finding clusters of closely related hyperspectral signatures in wound tissue, and thus can be used as a powerful tool to reach the anticipated classification. Moreover, we used a least square method to fit literature spectra (i.e. oxygenated haemoglobin (O2Hb), deoxygenated haemoglobin (HHb), water and fat) to the learned spectral classes. This procedure enables us to label each spectral class with the corresponding absorbance properties for the different absorbance of interest (i.e. O2Hb, HHb, water and fat). The calculated parameters of a testing set were consistent with the expected behaviour and show a good agreement with the results of a second algorithm which is used in the TIVITA™ tissue camera.
Hierarchical Analysis of Thorax Models to Measure Tidal Volume
Authors: Laufer Bernhard , Krueger-Ziolek Sabine , Moeller Knut , Docherty Paul David , Hoeflinger Fabian , Reindl Leonhard 
Published: September 2018

Abstract
Motion tracking of thorax kinematics can be used to determine respiration. However, determining a minimal sensor configuration from 64 candidate sensor locations is associated with high computational costs. Hence, a hierarchical optimization method was proposed to determine the optimal combination of sensors. The hierarchical method was assessed by its ability to quickly determine the sensor combination that will yield optimal modelled tidal volume compared to body plethysmograph measurements. This method was able to find the optimal sensor combinations, in approximately 2% of the estimated time required by an exhaustive search.
CurvChip - chip platform for investigating cell responses to curved surface features
Authors: Kemkemer Ralf , Frey Kerstin , Fischer Alena , Krastev Rumen 
Published: September 2018

Abstract
Surface topographies are often discussed as an important parameter influencing basic cell behavior. Whereas most in-vitro studies deal with microstructures with sharp edges, smooth, curved microscale topographies might be more relevant concerning in-vivo situations. Addressing the lack of highly defined surfaces with varying curvature, we present a topography chip system with 3D curved features of varying spacing, curvature radii as well as varying overall dimensions of curved surfaces. The CurvChip is produced by low-cost photolithography with thermal reflow, subsequent (repetitive) PDMS molding and hot embossing. The platform facilitates the systematic invitro investigation of the impact of substrate curvature on cell types like epithelial, endothelial, smooth muscle cells, or stem cells. Such investigations will not only help to further understand the mechanism of curvature sensation but may also contribute to optimize cellmaterial interactions in the field of regenerative medicine.
Sonographic Detection of Iron Oxide Nanoparticles Employing Shear Waves
Authors: Fink Michael , Lyer Stefan , Alexiou Christoph , Ermert Helmut 
Published: September 2018

Abstract
Research in biomedical nanotechnology led already to a variety of applications of nanoparticles in diagnosis as well as in therapy. One of these medical applications is Magnetic Drug Targeting, a promising cancer treatment technique. The aim of this medical attendance is a local chemotherapeutic treatment of the cancerous tissue. For this purpose, chemotherapeutic drugs are bound to magnetic nanoparticles and accumulated in the tumor area by means of an external static magnetic field. Hereby, a well-defined particle concentration in the cancerous tissue requires monitoring of the particle accumulation. Therefore, we present an ultrasound imaging technique that is capable of detecting quantitatively the concentration of iron oxide nanoparticles in biological tissue. The evaluation is based on the variation of the speed of sound of an induced shear wave with respect to the particle concentration.
Vascular response towards biodegradable sirolimus-eluting polymeric scaffolds in the porcine model
Authors: Kischkel Sabine , Bünger Carsten M. , Püschel Anja , Schareck Wolfgang , Schmitz Klaus-Peter , Grabow Niels 
Published: September 2018

Abstract
In this study, PLLA-based sirolimus-eluting polymer scaffolds and permanent bare-metal stents (316L) were implanted interventionally into both common carotid arteries (CCA) of 6 female pigs via the left common iliac artery (8F-sheath). The pigs were administered dual antiplatelet drugs peroral starting 3 days before the procedure until the end of the study. Stented CCA segments were explanted after 12 weeks, and processed for quantitative histomorphometry, and estimation of vascular inflammation and injury scores. SIR/polymer scaffolds showed a decreased residual lumen area and higher restenosis after 12 weeks (4.45 ± 2.23 mm² and 61.68 ± 22.39%) as compared to the 316L reference stent (16.53 ± 1.23 mm² and 6.65 ± 1.30%). After 12 weeks, inflammation score and vascular injury score were higher in the SIR/polymer group (1.90 ± 1.15 and 1.26 ± 0.87) compared to the 316L group (0.57 ± 0.37 and 0.83 ± 0.34).
Development of a graphical user interface and cross manufacturer adaptation of a program for determining the pulse wave velocity in the aorta from phase-contrast magnetic resonance images
Authors: Schareck Constantin , Oechtering Thekla , Frydrychowicz Alex , Koch Martin 
Published: September 2018

Abstract
Due to the periodic contraction of the heart, a pulse-shaped course of the blood velocity is caused in the aortic system. The speed at which this pulse wave moves is called pulse wave velocity (PWV). Since it depends, among other parameters, on the elasticity of the vascular wall, it can be used clinically as an indicator of atherosclerotic changes and, above all, as a measure of the risk of stroke for hypertensive patients. PWV can be determined non-invasively if the blood flow in the aorta is measured via flow-sensitive MRI. Within the scope of this work, existing phase-contrast (PC) image analysis algorithms, resulting in the PWV, were embedded in a graphical user interface (GUI). These algorithms were only able to evaluate PC images which were acquired on a Philips tomograph. Therefore they have been extended for the analysis of PC images acquired on a Siemens tomograph.
Biphasic parameter identification of equine articular cartilage from creep indentation data using an optimized 3D FE-based method
Authors: Reuter Thomas , Hurschler Christof 
Published: September 2018

Abstract
Mechanical parameters of hard and soft tissues are explicit markers for quantitative tissue characterization. In this study, we present a biphasic 3D-FE-based method to determine the biomechanical properties of equine articular cartilage from creep indentation tests (F = 0.1 N, t = 1000 s). The FE-model computation was optimized by exploiting the axial symmetry and mesh resolution. Parameter identification was executed with the Levenberg-Marquardt-algorithm. Additionally, sensitivity analyses of the calculated biomechanical parameters were performed. Results show that the Young’s modulus has the largest influence and the Poisson’s ratio of ν ≤ 0.1 is rather insensitive. The R² of the fit results varies between 0.882 and 0.974. The determined values for the Young’s modulus were 0.806 ± 0.093 MPa, the Poisson’s ratio 0.03 ± 0.06 and the permeability 0.012 ± 0.002 mm4/Ns. Future work will deal with mathematical extensions of the biphasic 3D-FE-model.