Dipl.-Phys.

König, Niels

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  • Publication
    Young’s Modulus-Independent Determination of Fibre Parameters for Rayleigh-Based Optical Frequency Domain Reflectometry from Cryogenic Temperatures up to 353 K
    ( 2023-05-09)
    Girmen, Caroline
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    Dittmar, Clemens
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    Siedenburg, Thorsten
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    Gastens, Markus
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    Wlochal, Michael
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    König, Niels orcid-logo
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    Schröder, Kai-Uwe
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    Schael, Stefan
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    Schmitt, Robert H.
    The magnetic spectrometer AMS-100, which includes a superconducting coil, is designed to measure cosmic rays and detect cosmic antimatter in space. This extreme environment requires a suitable sensing solution to monitor critical changes in the structure such as the beginning of a quench in the superconducting coil. Rayleigh-scattering-based distributed optical fibre sensors (DOFS) fulfil the high requirements for these extreme conditions but require precise calibration of the temperature and strain coefficients of the optical fibre. Therefore, the fibre-dependent strain and temperature coefficients 𝐾T and 𝐾𝜖 for the temperature range from 77 K to 353 K were investigated in this study. The fibre was integrated into an aluminium tensile test sample with well-calibrated strain gauges to determine the fibre’s 𝐾𝜖 independently of its Young’s modulus. Simulations were used to validate that the strain caused by changes in temperature or mechanical conditions was the same in the optical fibre as in the aluminium test sample. The results indicated a linear temperature dependence of 𝐾𝜖 and a non-linear temperature dependence of 𝐾T. With the parameters presented in this work, it was possible to accurately determine the strain or temperature of an aluminium structure over the entire temperature range from 77 K to 353 K using the DOFS.
  • Publication
    Adaptive phase contrast microscopy to compensate for the meniscus effect
    ( 2023-04-08)
    Nienhaus, Florian orcid-logo
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    Piotrowski, Tobias
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    Nießing, Bastian
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    König, Niels orcid-logo
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    Schmitt, Robert H.
    Phase contrast is one of the most important microscopic methods for making visible transparent, unstained cells. Cell cultures are often cultivated in microtiter plates, consisting of several cylindrical wells. The surface tension of the culture medium forms a liquid lens within the well, causing phase contrast conditions to fail in the more curved edge areas, preventing cell observation. Adaptive phase contrast microscopy is a method to strongly increase the observable area by optically compensating for the meniscus effect. The microscope’s condenser annulus is replaced by a transmissive LCD to allow dynamic changes. A deformable, liquid-filled prism is placed in the illumination path. The prism’s surface angle is adaptively inclined to refract transmitted light so that the tangential angle of the liquid lens can be compensated. Besides the observation of the phase contrast image, a beam splitter allows to simultaneously view condenser annulus and phase ring displacement. Algorithms analyze the displacement to dynamically adjust the LCD and prism to guarantee phase contrast conditions. Experiments show a significant increase in observable area, especially for small well sizes. For 96-well-plates, more than twelve times the area can be examined under phase contrast conditions instead of standard phase contrast microscopy.
  • Publication
    LIFTOSCOPE: development of an automated AI-based module for time-effective and contactless analysis and isolation of cells in microtiter plates
    ( 2023-02-07)
    Narrog, Florian orcid-logo
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    Lensing, Richard
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    Piotrowski, Tobias
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    Nottrodt, Nadine
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    Wehner, Martin
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    Nießing, Bastian
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    König, Niels orcid-logo
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    Gillner, Arnold
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    Schmitt, Robert H.
    Background: The cultivation, analysis, and isolation of single cells or cell cultures are fundamental to modern biological and medical processes. The novel LIFTOSCOPE technology aims to integrate analysis and isolation into one versatile, fully automated device. Methods: LIFTOSCOPE’s three core technologies are high-speed microscopy for rapid full-surface imaging of cell culture vessels, AI-based semantic segmentation of microscope images for localization and evaluation of cells, and laser-induced forward transfer (LIFT) for contact-free isolation of cells and cell clusters. LIFT transfers cells from a standard microtiter plate (MTP) across an air gap to a receiver plate, from where they can be further cultivated. The LIFT laser is integrated into the optical path of an inverse microscope, allowing to switch quickly between microscopic observation and cell transfer. Results: Tests of the individual process steps prove the feasibility of the concept. A prototype setup shows the compatibility of the microscope stage with the LIFT laser. A specifically designed MTP adapter to hold a receiver plate has been designed and successfully used for material transfers. A suitable AI algorithm has been found for cell selection. Conclusion: LIFTOSCOPE speeds up cell cultivation and analysis with a target process time of 10 minutes, which can be achieved if the cell transfer is sped up using a more efficient path-finding algorithm. Some challenges remain, like finding a suitable cell transfer medium. Significance: The LIFTOSCOPE system can be used to extend existing cell cultivation systems and microscopes for fully automated biotechnological applications.
  • Publication
    Optical coherence tomography and convolutional neural networks can differentiate colorectal liver metastases from liver parenchyma ex vivo
    ( 2023)
    Amygdalos, Iakovos
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    Hachgenei, Enno
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    Burkl, Luisa
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    Vargas, David
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    Goßmann, Paul
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    Wolff, Laura I.
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    Druzenko, Mariia
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    Frye, Maik
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    König, Niels orcid-logo
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    Schmitt, Robert
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    Chrysos, Alexandros
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    Jöchle, Katharina
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    Ulmer, Florian
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    Lambertz, Andreas
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    Knüchel-Clarke, Ruth
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    Neumann, Ulf Peter
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    Lang, Sven A.
    Purpose: Optical coherence tomography (OCT) is an imaging technology based on low-coherence interferometry, which provides non-invasive, high-resolution cross-sectional images of biological tissues. A potential clinical application is the intraoperative examination of resection margins, as a real-time adjunct to histological examination. In this ex vivo study, we investigated the ability of OCT to differentiate colorectal liver metastases (CRLM) from healthy liver parenchyma, when combined with convolutional neural networks (CNN). Methods: Between June and August 2020, consecutive adult patients undergoing elective liver resections for CRLM were included in this study. Fresh resection specimens were scanned ex vivo, before fixation in formalin, using a table-top OCT device at 1310 nm wavelength. Scanned areas were marked and histologically examined. A pre-trained CNN (Xception) was used to match OCT scans to their corresponding histological diagnoses. To validate the results, a stratified k-fold cross-validation (CV) was carried out. Results: A total of 26 scans (containing approx. 26,500 images in total) were obtained from 15 patients. Of these, 13 were of normal liver parenchyma and 13 of CRLM. The CNN distinguished CRLM from healthy liver parenchyma with an F1-score of 0.93 (0.03), and a sensitivity and specificity of 0.94 (0.04) and 0.93 (0.04), respectively. Conclusion: Optical coherence tomography combined with CNN can distinguish between healthy liver and CRLM with great accuracy ex vivo. Further studies are needed to improve upon these results and develop in vivo diagnostic technologies, such as intraoperative scanning of resection margins.
  • Publication
    Optical coherence tomography combined with convolutional neural networks can differentiate between intrahepatic cholangiocarcinoma and liver parenchyma ex vivo
    ( 2023)
    Wolff, Laura I.
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    Hachgenei, Enno
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    Goßmann, Paul
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    Druzenko, Mariia
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    Frye, Maik
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    König, Niels orcid-logo
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    Schmitt, Robert
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    Chrysos, Alexandros
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    Jöchle, Katharina
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    Truhn, Daniel
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    Kather, Jakob Nikolas
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    Lambertz, Andreas
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    Gaisa, Nadine Therese
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    Jonigk, Danny David
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    Ulmer, Florian T.
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    Neumann, Ulf Peter
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    Lang, Sven Arke
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    Amygdalos, Iakovos
    Purpose: Surgical resection with complete tumor excision (R0) provides the best chance of long-term survival for patients with intrahepatic cholangiocarcinoma (iCCA). A non-invasive imaging technology, which could provide quick intraoperative assessment of resection margins, as an adjunct to histological examination, is optical coherence tomography (OCT). In this study, we investigated the ability of OCT combined with convolutional neural networks (CNN), to differentiate iCCA from normal liver parenchyma ex vivo. Methods: Consecutive adult patients undergoing elective liver resections for iCCA between June 2020 and April 2021 (n = 11) were included in this study. Areas of interest from resection specimens were scanned ex vivo, before formalin fixation, using a table-top OCT device at 1310 nm wavelength. Scanned areas were marked and histologically examined, providing a diagnosis for each scan. An Xception CNN was trained, validated, and tested in matching OCT scans to their corresponding histological diagnoses, through a 5 × 5 stratified cross-validation process. Results: Twenty-four three-dimensional scans (corresponding to approx. 85,603 individual) from ten patients were included in the analysis. In 5 × 5 cross-validation, the model achieved a mean F1-score, sensitivity, and specificity of 0.94, 0.94, and 0.93, respectively. Conclusion: Optical coherence tomography combined with CNN can differentiate iCCA from liver parenchyma ex vivo. Further studies are necessary to expand on these results and lead to innovative in vivo OCT applications, such as intraoperative or endoscopic scanning.
  • Publication
    High-Speed-Microscopy for Scalable Quality Control in Automated Production of Stem Cell Spheroids for Tissue Engineering
    ( 2023)
    Krieger, Judith
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    Nießing, Bastian
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    König, Niels orcid-logo
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    Schmitt, Robert
    The EU Horizon 2020 project »JointPromise« implies the conception and implementation of an end-to-end automated production platform for three-dimensional joint implants, paving the way for tissue-engineered implants able to regenerate deep osteochondral defects. Spheroid-based implants provide a novel approach in tissue engineering by aggregating progenitor cells into potent microtissues. After the differentiation of cartilaginous microtissues, functional joint implants are assembled via 3D bioprinting to match the complex structural organization of native cartilage tissue. As the automation approach of the project aims to overcome bottlenecks in manual production such as product variability, lack of scalability and high personnel costs, a high-throughput quality control system is crucial for the production of reliable Advanced Therapy Medicinal Products (ATMPs). By establishing not only a technical solution for the full digitization of the cell culture plates but also an intelligent image processing algorithm for the detection of the cell spheroids, relevant process parameters like size distribution and growth curves can be detected. Critical thresholds in spheroid growth are evaluated to minimize risks of carcinogenic tissue formation in vivo as well as to define harvest criteria to prevent inhomogeneous bioprinting results. In order to calculate the required throughput and elaborate optimization potentials of the automated spheroid production, voids in the cultivation vessel or disrupted aggregates due to media changes or transportation are detected. Ultimately, the high-speed-microscopy complies with the requirements of a high-throughput automated cell production platform to meet the rising demand for alternative therapeutic approaches in regenerative medicine.
  • Publication
    Toward Rapid, Widely Available Autologous CAR-T Cell Therapy - Artificial Intelligence and Automation Enabling the Smart Manufacturing Hospital
    ( 2022-06-06)
    Hort, Simon orcid-logo
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    Herbst, Laura
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    Bäckel, Niklas
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    Erkens, Frederik
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    Nießing, Bastian
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    Frye, Maik
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    König, Niels orcid-logo
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    Papantoniou, Ioannis
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    Hudecek, Michael
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    Jacobs, John J. L.
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    Schmitt, Robert
    CAR-T cell therapy is a promising treatment for acute leukemia and lymphoma. CAR-T cell therapies take a pioneering role in autologous gene therapy with three EMA-approved products. However, the chance of clinical success remains relatively low as the applicability of CAR-T cell therapy suffers from long, labor-intensive manufacturing and a lack of comprehensive insight into the bioprocess. This leads to high manufacturing costs and limited clinical success, preventing the widespread use of CAR-T cell therapies. New manufacturing approaches are needed to lower costs to improve manufacturing capacity and shorten provision times. Semi-automated devices such as the Miltenyi Prodigy® were developed to reduce hands-on production time. However, these devices are not equipped with the process analytical technology necessary to fully characterize and control the process. An automated AI-driven CAR-T cell manufacturing platform in smart manufacturing hospitals (SMH) is being developed to address these challenges. Automation will increase the cost-effectiveness and robustness of manufacturing. Using Artificial Intelligence (AI) to interpret the data collected on the platform will provide valuable process insights and drive decisions for process optimization. The smart integration of automated CAR-T cell manufacturing platforms into hospitals enables the independent manufacture of autologous CAR-T cell products. In this perspective, we will be discussing current challenges and opportunities of the patient-specific but highly automated, AI-enabled CAR-T cell manufacturing. A first automation concept will be shown, including a system architecture based on current Industry 4.0 approaches for AI integration.
  • Publication
    Performance of 5G Trials for Industrial Automation
    ( 2022)
    Ansari, Junaid
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    Andersson, Christian
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    Bruin, Peter de
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    Farkas, János
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    Grosjean, Leefke
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    Sachs, Joachim
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    Torsner, Johan
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    Varga, Balázs
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    Harutyunyan, Davit
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    König, Niels orcid-logo
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    Schmitt, Robert H.
    Wireless- and 5G-enabled industrial automation is expected to include a plethora of different applications with a wide variety of requirements. In this article, evaluations are undertaken for the deployment of 5G in realistic industrial production environments with realistic deployment settings. Both deployments using commercial 5G systems and a 5G prototype system including pre-commercial and standard compliant URLLC functionality have been investigated. Systematic latency and reliability measurements were performed, over the air and in live networks, for different packet sizes, different devices, and networks with different capabilities (at different sites) to characterize the expected performance. The results indicate that today’s 5G latency performance significantly depends on packet size, transmission direction (uplink or downlink), and network configuration as well as on the end device’s design and capabilities. Our over-the-air measurements also empirically show that 5G technology and future networks have the capability of providing one-way latency of around 1 ms in both uplink and downlink for the various packet sizes tested. It is concluded that the requirements for very low latencies can be achieved with high reliability guarantees, as required in some of the most stringent industrial IoT applications.
  • Publication
    Techno-Economic Evaluation of 5G-NSA-NPN for Networked Control Systems
    ( 2022)
    Kiesel, Raphael
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    Schmitt, Sarah orcid-logo
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    König, Niels orcid-logo
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    Brochhaus, Maximilian
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    Vollmer, Thomas
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    Stichling, Kirstin
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    Mann, Alexander
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    Schmitt, Robert H.
    Wireless closed-loop control systems, so-called networked control systems (NCS) promise technical and economic benefits for production applications. To realize prospective benefits, the right communication technology is key. The fifth generation of mobile communication is predicted to have a significant impact on the deployment of NCS in the industrial connectivity landscape. However, there are different options for 5G deployment influencing both technical performance and economic aspects of the network. This in turn is expected to have a techno-economic influence on the production itself. Thus, a trade-off between the necessary technical performance of the 5G network and the benefits for the production must be executed. This paper, therefore, aims to analyze the techno-economic benefits of 5G deployment for closed-loop control systems in production. To reach this aim, first, the fundamentals of techno-economic analysis are introduced. Second, the results of an experimental performance analysis of a 5G-NSA-NPN at Fraunhofer IPT in Aachen are shown. Third, based on the results from the experimental study, a model-based techno-economic ex-ante evaluation of 5G-NSA-NPN for closed-loop applications is performed, and an exemplar is shown for a BLISK milling use case. Finally, the results are summarized and an outlook for further research is given. The analysis shows a difference in net present value for 5G deployment of EUR 2.6 M after 10 years and a difference of OPEX per product of around EUR -1000 per BLISK. Furthermore, analysis shows an increase in productivity (0.73%), quality (30.75%), and sustainability (2.87%). This indicates a noticeable improvement of a 5G-controlled NCS.