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2,437 results on '"Nikolaou, Konstantin"'

1. SwarmRL: Building the Future of Smart Active Systems

Author

Tovey, Samuel, Lohrmann, Christoph, Merkt, Tobias, Zimmer, David, Nikolaou, Konstantin, Koppenhöfer, Simon, Bushmakina, Anna, Scheunemann, Jonas, and Holm, Christian

Subjects
Computer Science - Robotics, Condensed Matter - Soft Condensed Matter, Computer Science - Artificial Intelligence, Computer Science - Multiagent Systems, Physics - Biological Physics
Abstract

This work introduces SwarmRL, a Python package designed to study intelligent active particles. SwarmRL provides an easy-to-use interface for developing models to control microscopic colloids using classical control and deep reinforcement learning approaches. These models may be deployed in simulations or real-world environments under a common framework. We explain the structure of the software and its key features and demonstrate how it can be used to accelerate research. With SwarmRL, we aim to streamline research into micro-robotic control while bridging the gap between experimental and simulation-driven sciences. SwarmRL is available open-source on GitHub at https://github.com/SwarmRL/SwarmRL., Comment: 16 pages, 3 figures

Published
2024

6. Towards a Phenomenological Understanding of Neural Networks: Data

Author

Tovey, Samuel, Krippendorf, Sven, Nikolaou, Konstantin, and Holm, Christian

Subjects
Computer Science - Machine Learning, Physics - Computational Physics
Abstract

A theory of neural networks (NNs) built upon collective variables would provide scientists with the tools to better understand the learning process at every stage. In this work, we introduce two such variables, the entropy and the trace of the empirical neural tangent kernel (NTK) built on the training data passed to the model. We empirically analyze the NN performance in the context of these variables and find that there exists correlation between the starting entropy, the trace of the NTK, and the generalization of the model computed after training is complete. This framework is then applied to the problem of optimal data selection for the training of NNs. To this end, random network distillation (RND) is used as a means of selecting training data which is then compared with random selection of data. It is shown that not only does RND select data-sets capable of outperforming random selection, but that the collective variables associated with the RND data-sets are larger than those of the randomly selected sets. The results of this investigation provide a stable ground from which the selection of data for NN training can be driven by this phenomenological framework., Comment: 13 pages, 7 figures

Published
2023

8. Competence of radiologists in cardiac CT and MR imaging in Europe: insights from the ESCR Registry

Author

Catapano, Federica, Moser, Lukas Jakob, Francone, Marco, Catalano, Carlo, Vliegenthart, Rozemarijn, Budde, Ricardo P. J., Salgado, Rodrigo, Hrabak Paar, Maja, Pirnat, Maja, Loewe, Christian, Nikolaou, Konstantin, Williams, Michelle C., Muscogiuri, Giuseppe, Natale, Luigi, Lehmkuhl, Lukas, Sieren, Malte Maria, Gutberlet, Matthias, and Alkadhi, Hatem

Published
2024
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9. Characterization of harmonic modes and parasitic resonances in multi-mode superconducting coplanar resonators

Author

Beydeda, Cenk, Nikolaou, Konstantin, Tochtermann, Marius, Ebensperger, Nikolaj G., Untereiner, Gabriele, Farag, Ahmed, Karl, Philipp, Ubl, Monika, Giessen, Harald, Dressel, Martin, and Scheffler, Marc

Subjects
Condensed Matter - Superconductivity
Abstract

Planar superconducting microwave transmission line resonators can be operated at multiple harmonic resonance frequencies. This allows covering wide spectral regimes with high sensitivity, as it is desired e.g. for cryogenic microwave spectroscopy. A common complication of such experiments is the presence of undesired 'spurious' additional resonances, which are due to standing waves within the resonator substrate or housing box. Identifying the nature of individual resonances ('designed' vs. 'spurious') can become challenging for higher frequencies or if elements with unknown material properties are included, as is common for microwave spectroscopy. Here we discuss various experimental strategies to distinguish designed and spurious modes in coplanar superconducting resonators that are operated in a broad frequency range up to 20 GHz. These strategies include tracking resonance evolution as a function of temperature, magnetic field, and microwave power. We also demonstrate that local modification of the resonator, by applying minute amounts of dielectric or ESR-active materials, lead to characteristic signatures in the various resonance modes, depending on the local strength of the electric or magnetic microwave fields.

Published
2023
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12. Computed tomography angiography versus Agatston score for diagnosis of coronary artery disease in patients with stable chest pain: individual patient data meta-analysis of the international COME-CCT Consortium

Author

Wieske, Viktoria, Walther, Mario, Dubourg, Benjamin, Alkadhi, Hatem, Nørgaard, Bjarne L, Meijs, Matthijs FL, Diederichsen, Axel CP, Wan, Yung-Liang, Mickley, Hans, Nikolaou, Konstantin, Shabestari, Abbas A, Halvorsen, Bjørn A, Martuscelli, Eugenio, Sun, Kai, Herzog, Bernhard A, Marcus, Roy P, Leschka, Sebastian, Garcia, Mario J, Ovrehus, Kristian A, Knuuti, Juhani, Mendoza-Rodriguez, Vladymir, Bettencourt, Nuno, Muraglia, Simone, Buechel, Ronny R, Kaufmann, Philipp A, Zimmermann, Elke, Tardif, Jean-Claude, Budoff, Matthew J, Schlattmann, Peter, and Dewey, Marc

Subjects
Biomedical and Clinical Sciences, Cardiovascular Medicine and Haematology, Clinical Sciences, Heart Disease - Coronary Heart Disease, Atherosclerosis, Cardiovascular, Heart Disease, Biomedical Imaging, Clinical Research, 4.2 Evaluation of markers and technologies, Detection, screening and diagnosis, Calcium, Chest Pain, Computed Tomography Angiography, Coronary Angiography, Coronary Artery Disease, Coronary Stenosis, Humans, Predictive Value of Tests, Tomography, X-Ray Computed, Computed tomography angiography, Coronary angiography, Coronary artery disease, COME-CCT Consortium, Nuclear Medicine & Medical Imaging, Clinical sciences
Abstract

ObjectivesThere is conflicting evidence about the comparative diagnostic accuracy of the Agatston score versus computed tomography angiography (CTA) in patients with suspected obstructive coronary artery disease (CAD).PurposeTo determine whether CTA is superior to the Agatston score in the diagnosis of CAD.MethodsIn total 2452 patients with stable chest pain and a clinical indication for invasive coronary angiography (ICA) for suspected CAD were included by the Collaborative Meta-analysis of Cardiac CT (COME-CCT) Consortium. An Agatston score of > 400 was considered positive, and obstructive CAD defined as at least 50% coronary diameter stenosis on ICA was used as the reference standard.ResultsObstructive CAD was diagnosed in 44.9% of patients (1100/2452). The median Agatston score was 74. Diagnostic accuracy of CTA for the detection of obstructive CAD (81.1%, 95% confidence interval [CI]: 77.5 to 84.1%) was significantly higher than that of the Agatston score (68.8%, 95% CI: 64.2 to 73.1%, p < 0.001). Among patients with an Agatston score of zero, 17% (101/600) had obstructive CAD. Diagnostic accuracy of CTA was not significantly different in patients with low to intermediate (1 to < 100, 100-400) versus moderate to high Agatston scores (401-1000, > 1000).ConclusionsResults in our international cohort show CTA to have significantly higher diagnostic accuracy than the Agatston score in patients with stable chest pain, suspected CAD, and a clinical indication for ICA. Diagnostic performance of CTA is not affected by a higher Agatston score while an Agatston score of zero does not reliably exclude obstructive CAD.Key points• CTA showed significantly higher diagnostic accuracy (81.1%, 95% confidence interval [CI]: 77.5 to 84.1%) for diagnosis of coronary artery disease when compared to the Agatston score (68.8%, 95% CI: 64.2 to 73.1%, p < 0.001). • Diagnostic performance of CTA was not affected by increased amount of calcium and was not significantly different in patients with low to intermediate (1 to 1000). • Seventeen percent of patients with an Agatston score of zero showed obstructive coronary artery disease by invasive angiography showing absence of coronary artery calcium cannot reliably exclude coronary artery disease.

Published
2022

23. Uterine allograft removal by total laparoscopic hysterectomy after successful cesarean delivery in a living-donor uterus recipient with uterovaginal agenesis (MRKHS)

Author

Brucker, Sara Yvonne, Krämer, Bernhard, Abele, Harald, Henes, Melanie, Hoopmann, Markus, Schöller, Dorit, Königsrainer, Alfred, Bösmüller, Hans, Nikolaou, Konstantin, Krumm, Patrick, Rosenberger, Peter, Heim, Eckhard, Amend, Bastian, Rausch, Steffen, Althaus, Karina, Bakchoul, Tamam, Guthoff, Martina, Heyne, Nils, Nadalin, Silvio, and Rall, Kristin Katharina

Published
2023
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24. State-of-the-art CT and MR imaging and assessment of atherosclerotic carotid artery disease: standardization of scanning protocols and measurements—a consensus document by the European Society of Cardiovascular Radiology (ESCR)

Author

Saba, Luca, Loewe, Christian, Weikert, Thomas, Williams, Michelle C., Galea, Nicola, Budde, Ricardo P. J., Vliegenthart, Rozemarijn, Velthuis, Birgitta K., Francone, Marco, Bremerich, Jens, Natale, Luigi, Nikolaou, Konstantin, Dacher, Jean-Nicolas, Peebles, Charles, Caobelli, Federico, Redheuil, Alban, Dewey, Marc, Kreitner, Karl-Friedrich, and Salgado, Rodrigo

Published
2023
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25. State-of-the-art CT and MR imaging and assessment of atherosclerotic carotid artery disease: the reporting—a consensus document by the European Society of Cardiovascular Radiology (ESCR)

Author

Saba, Luca, Loewe, Christian, Weikert, Thomas, Williams, Michelle C., Galea, Nicola, Budde, Ricardo P. J., Vliegenthart, Rozemarijn, Velthuis, Birgitta K., Francone, Marco, Bremerich, Jens, Natale, Luigi, Nikolaou, Konstantin, Dacher, Jean-Nicolas, Peebles, Charles, Caobelli, Federico, Redheuil, Alban, Dewey, Marc, Kreitner, Karl-Friedrich, and Salgado, Rodrigo

Published
2023
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26. Longitudinal monitoring of Apparent Diffusion Coefficient (ADC) in patients with prostate cancer undergoing MR-guided radiotherapy on an MR-Linac at 1.5 T: a prospective feasibility study

Author

Almansour Haidara, Schick Fritz, Nachbar Marcel, Afat Saif, Fritz Victor, Thorwarth Daniela, Zips Daniel, Bertram Felix, Müller Arndt-Christian, Nikolaou Konstantin, Othman Ahmed E, and Wegener Daniel

Subjects
prostate carcinoma, mri, adaptive radiotherapy, image guidance, mr-linac, adc, Medical physics. Medical radiology. Nuclear medicine, R895-920
Abstract

Hybrid MRI linear accelerators (MR-Linac) might enable individualized online adaptation of radiotherapy using quantitative MRI sequences as diffusion-weighted imaging (DWI). The purpose of this study was to investigate the dynamics of lesion apparent diffusion coefficient (ADC) in patients with prostate cancer undergoing MR-guided radiation therapy (MRgRT) on a 1.5T MR-Linac. The ADC values at a diagnostic 3T MRI scanner were used as the reference standard.

Published
2023
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27. Fully Automated and Standardized Segmentation of Adipose Tissue Compartments by Deep Learning in Three-dimensional Whole-body MRI of Epidemiological Cohort Studies

Author

Küstner, Thomas, Hepp, Tobias, Fischer, Marc, Schwartz, Martin, Fritsche, Andreas, Häring, Hans-Ulrich, Nikolaou, Konstantin, Bamberg, Fabian, Yang, Bin, Schick, Fritz, Gatidis, Sergios, and Machann, Jürgen

Subjects
Computer Science - Computer Vision and Pattern Recognition, Electrical Engineering and Systems Science - Image and Video Processing, Physics - Medical Physics
Abstract

Purpose: To enable fast and reliable assessment of subcutaneous and visceral adipose tissue compartments derived from whole-body MRI. Methods: Quantification and localization of different adipose tissue compartments from whole-body MR images is of high interest to examine metabolic conditions. For correct identification and phenotyping of individuals at increased risk for metabolic diseases, a reliable automatic segmentation of adipose tissue into subcutaneous and visceral adipose tissue is required. In this work we propose a 3D convolutional neural network (DCNet) to provide a robust and objective segmentation. In this retrospective study, we collected 1000 cases (66$\pm$ 13 years; 523 women) from the Tuebingen Family Study and from the German Center for Diabetes research (TUEF/DZD), as well as 300 cases (53$\pm$ 11 years; 152 women) from the German National Cohort (NAKO) database for model training, validation, and testing with a transfer learning between the cohorts. These datasets had variable imaging sequences, imaging contrasts, receiver coil arrangements, scanners and imaging field strengths. The proposed DCNet was compared against a comparable 3D UNet segmentation in terms of sensitivity, specificity, precision, accuracy, and Dice overlap. Results: Fast (5-7seconds) and reliable adipose tissue segmentation can be obtained with high Dice overlap (0.94), sensitivity (96.6%), specificity (95.1%), precision (92.1%) and accuracy (98.4%) from 3D whole-body MR datasets (field of view coverage 450x450x2000mm${}^3$). Segmentation masks and adipose tissue profiles are automatically reported back to the referring physician. Conclusion: Automatic adipose tissue segmentation is feasible in 3D whole-body MR data sets and is generalizable to different epidemiological cohort studies with the proposed DCNet., Comment: This manuscript has been accepted for publication in Radiology: Artificial Intelligence (https://pubs.rsna.org/journal/ai), which is published by the Radiological Society of North America (RSNA)

Published
2020

32. Framework and baseline examination of the German National Cohort (NAKO)

Author

Peters, Annette, Peters, Annette, Greiser, Karin Halina, Göttlicher, Susanne, Ahrens, Wolfgang, Albrecht, Maren, Bamberg, Fabian, Bärnighausen, Till, Becher, Heiko, Berger, Klaus, Beule, Achim, Boeing, Heiner, Bohn, Barbara, Bohnert, Kerstin, Braun, Bettina, Brenner, Hermann, Bülow, Robin, Castell, Stefanie, Damms-Machado, Antje, Dörr, Marcus, Ebert, Nina, Ecker, Margit, Emmel, Carina, Fischer, Beate, Franzke, Claus-Werner, Gastell, Sylvia, Giani, Guido, Günther, Matthias, Günther, Kathrin, Günther, Klaus-Peter, Haerting, Johannes, Haug, Ulrike, Heid, Iris M., Heier, Margit, Heinemeyer, Diana, Hendel, Thomas, Herbolsheimer, Florian, Hirsch, Jochen, Hoffmann, Wolfgang, Holleczek, Bernd, Hölling, Heike, Hörlein, Andreas, Jöckel, Karl-Heinz, Kaaks, Rudolf, Karch, André, Karrasch, Stefan, Kartschmit, Nadja, Kauczor, Hans-Ulrich, Keil, Thomas, Kemmling, Yvonne, Klee, Bianca, Klüppelholz, Birgit, Kluttig, Alexander, Kofink, Lisa, Köttgen, Anna, Kraft, Daniel, Krause, Gérard, Kretz, Lisa, Krist, Lilian, Kühnisch, Jan, Kuß, Oliver, Legath, Nicole, Lehnich, Anna-Therese, Leitzmann, Michael, Lieb, Wolfgang, Linseisen, Jakob, Loeffler, Markus, Macdonald, Anke, Maier-Hein, Klaus H., Mangold, Nina, Meinke-Franze, Claudia, Meisinger, Christa, Melzer, Juliane, Mergarten, Björn, Michels, Karin B., Mikolajczyk, Rafael, Moebus, Susanne, Mueller, Ulrich, Nauck, Matthias, Niendorf, Thoralf, Nikolaou, Konstantin, Obi, Nadia, Ostrzinski, Stefan, Panreck, Leo, Pigeot, Iris, Pischon, Tobias, Pschibul-Thamm, Irene, Rathmann, Wolfgang, Reineke, Achim, Roloff, Stefanie, Rujescu, Dan, Rupf, Stefan, Sander, Oliver, Schikowski, Tamara, Schipf, Sabine, Schirmacher, Peter, Schlett, Christopher L., Schmidt, Börge, Schmidt, Georg, Schmidt, Martin, Schöne, Gina, Schulz, Holger, Schulze, Matthias B., Schweig, Alexandra, Sedlmeier, Anja M., Selder, Sonja, Six-Merker, Julia, Sowade, Ramona, Stang, Andreas, Stegle, Oliver, Steindorf, Karen, Stübs, Gunthard, Swart, Enno, Teismann, Henning, Thiele, Inke, Thierry, Sigrid, Ueffing, Marius, Völzke, Henry, Waniek, Sabina, Weber, Andrea, Werner, Nicole, Wichmann, H.-Erich, Willich, Stefan N., Wirkner, Kerstin, Wolf, Kathrin, Wolff, Robert, Zeeb, Hajo, Zinkhan, Melanie, and Zschocke, Johannes

Published
2022
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35. Impact of endorectal filling on interobserver variability of MRI based rectal primary tumor delineation

Author

Lo Russo, Monica, Nachbar, Marcel, Barry, Aisling, Bhide, Shree, Chang, Amy, Hall, William, Intven, Martijn, Marijnen, Corrie, Peters, Femke, Minsky, Bruce, Romesser, Paul B., Sarkar, Reith, Tan, Alex, Boeke, Simon, Wegener, Daniel, Butzer, Sarah, Boldt, Jessica, Gatidis, Sergios, Nikolaou, Konstantin, Thorwarth, Daniela, Zips, Daniel, and Gani, Cihan

Published
2023
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36. Quality and safety of coronary computed tomography angiography at academic and non-academic sites: insights from a large European registry (ESCR MR/CT Registry)

Author

Foldyna, Borek, Uhlig, Johannes, Gohmann, Robin, Lücke, Christian, Mayrhofer, Thomas, Lehmkuhl, Lukas, Natale, Luigi, Vliegenthart, Rozemarijn, Lotz, Joachim, Salgado, Rodrigo, Francone, Marco, Loewe, Christian, Nikolaou, Konstantin, Bamberg, Fabian, Maintz, David, Maurovich-Horvat, Pal, Thiele, Holger, Hoffmann, Udo, and Gutberlet, Matthias

Published
2022
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37. Radiation Exposure and Safety Considerations in Interventional Radiology: Comparison of a Twin Robotic X-ray System to a Conventional Angiography System

Author

Ruff, Christer, primary, Partovi, Sasan, additional, Strobel, Isabella, additional, Kaleth, Stella, additional, Herz, Klaus, additional, Nikolaou, Konstantin, additional, Levitin, Abraham, additional, Kirksey, Levester, additional, Syha, Roland, additional, Artzner, Christoph, additional, and Grözinger, Gerd, additional

Published
2024
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40. Retrospective correction of Rigid and Non-Rigid MR motion artifacts using GANs

Author

Armanious, Karim, Gatidis, Sergios, Nikolaou, Konstantin, Yang, Bin, and Küstner, Thomas

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Motion artifacts are a primary source of magnetic resonance (MR) image quality deterioration with strong repercussions on diagnostic performance. Currently, MR motion correction is carried out either prospectively, with the help of motion tracking systems, or retrospectively by mainly utilizing computationally expensive iterative algorithms. In this paper, we utilize a new adversarial framework, titled MedGAN, for the joint retrospective correction of rigid and non-rigid motion artifacts in different body regions and without the need for a reference image. MedGAN utilizes a unique combination of non-adversarial losses and a new generator architecture to capture the textures and fine-detailed structures of the desired artifact-free MR images. Quantitative and qualitative comparisons with other adversarial techniques have illustrated the proposed model performance., Comment: 5 pages, 2 figures, under review for the IEEE International Symposium for Biomedical Images

Published
2018
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41. A Machine-learning framework for automatic reference-free quality assessment in MRI

Author

Küstner, Thomas, Gatidis, Sergios, Liebgott, Annika, Schwartz, Martin, Mauch, Lukas, Martirosian, Petros, Schmidt, Holger, Schwenzer, Nina F., Nikolaou, Konstantin, Bamberg, Fabian, Yang, Bin, and Schick, Fritz

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning, Statistics - Machine Learning
Abstract

Magnetic resonance (MR) imaging offers a wide variety of imaging techniques. A large amount of data is created per examination which needs to be checked for sufficient quality in order to derive a meaningful diagnosis. This is a manual process and therefore time- and cost-intensive. Any imaging artifacts originating from scanner hardware, signal processing or induced by the patient may reduce the image quality and complicate the diagnosis or any image post-processing. Therefore, the assessment or the ensurance of sufficient image quality in an automated manner is of high interest. Usually no reference image is available or difficult to define. Therefore, classical reference-based approaches are not applicable. Model observers mimicking the human observers (HO) can assist in this task. Thus, we propose a new machine-learning-based reference-free MR image quality assessment framework which is trained on HO-derived labels to assess MR image quality immediately after each acquisition. We include the concept of active learning and present an efficient blinded reading platform to reduce the effort in the HO labeling procedure. Derived image features and the applied classifiers (support-vector-machine, deep neural network) are investigated for a cohort of 250 patients. The MR image quality assessment framework can achieve a high test accuracy of 93.7$\%$ for estimating quality classes on a 5-point Likert-scale. The proposed MR image quality assessment framework is able to provide an accurate and efficient quality estimation which can be used as a prospective quality assurance including automatic acquisition adaptation or guided MR scanner operation, and/or as a retrospective quality assessment including support of diagnostic decisions or quality control in cohort studies.

Published
2018
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42. MedGAN: Medical Image Translation using GANs

Author

Armanious, Karim, Jiang, Chenming, Fischer, Marc, Küstner, Thomas, Nikolaou, Konstantin, Gatidis, Sergios, and Yang, Bin

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Image-to-image translation is considered a new frontier in the field of medical image analysis, with numerous potential applications. However, a large portion of recent approaches offers individualized solutions based on specialized task-specific architectures or require refinement through non-end-to-end training. In this paper, we propose a new framework, named MedGAN, for medical image-to-image translation which operates on the image level in an end-to-end manner. MedGAN builds upon recent advances in the field of generative adversarial networks (GANs) by merging the adversarial framework with a new combination of non-adversarial losses. We utilize a discriminator network as a trainable feature extractor which penalizes the discrepancy between the translated medical images and the desired modalities. Moreover, style-transfer losses are utilized to match the textures and fine-structures of the desired target images to the translated images. Additionally, we present a new generator architecture, titled CasNet, which enhances the sharpness of the translated medical outputs through progressive refinement via encoder-decoder pairs. Without any application-specific modifications, we apply MedGAN on three different tasks: PET-CT translation, correction of MR motion artefacts and PET image denoising. Perceptual analysis by radiologists and quantitative evaluations illustrate that the MedGAN outperforms other existing translation approaches., Comment: 16 pages, 8 figures

Published
2018
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49. Dynamic Myocardial Perfusion CT for the Detection of Hemodynamically Significant Coronary Artery Disease

Author

Nous, Fay M.A., Geisler, Tobias, Kruk, Mariusz B.P., Alkadhi, Hatem, Kitagawa, Kakuya, Vliegenthart, Rozemarijn, Hell, Michaela M., Hausleiter, Jörg, Nguyen, Patricia K., Budde, Ricardo P.J., Nikolaou, Konstantin, Kepka, Cezary, Manka, Robert, Sakuma, Hajime, Malik, Sachin B., Coenen, Adriaan, Zijlstra, Felix, Klotz, Ernst, van der Harst, Pim, Artzner, Christoph, Dedic, Admir, Pugliese, Francesca, Bamberg, Fabian, and Nieman, Koen

Published
2022
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50. Correction to: State-of-the-art CT and MR imaging and assessment of atherosclerotic carotid artery disease: the reporting—a consensus document by the European Society of Cardiovascular Radiology (ESCR)

Author

Saba, Luca, Loewe, Christian, Weikert, Thomas, Williams, Michelle C., Galea, Nicola, Budde, Ricardo P. J., Vliegenthart, Rozemarijn, Velthuis, Birgitta K., Francone, Marco, Bremerich, Jens, Natale, Luigi, Nikolaou, Konstantin, Dacher, Jean-Nicolas, Peebles, Charles, Caobelli, Federico, Redheuil, Alban, Dewey, Marc, Kreitner, Karl-Friedrich, and Salgado, Rodrigo

Published
2023
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