TY - JOUR A1 - Dhillon, Maninder Singh A1 - Kübert-Flock, Carina A1 - Dahms, Thorsten A1 - Rummler, Thomas A1 - Arnault, Joel A1 - Steffan-Dewenter, Ingolf A1 - Ullmann, Tobias T1 - Evaluation of MODIS, Landsat 8 and Sentinel-2 data for accurate crop yield predictions: a case study using STARFM NDVI in Bavaria, Germany JF - Remote Sensing N2 - The increasing availability and variety of global satellite products and the rapid development of new algorithms has provided great potential to generate a new level of data with different spatial, temporal, and spectral resolutions. However, the ability of these synthetic spatiotemporal datasets to accurately map and monitor our planet on a field or regional scale remains underexplored. This study aimed to support future research efforts in estimating crop yields by identifying the optimal spatial (10 m, 30 m, or 250 m) and temporal (8 or 16 days) resolutions on a regional scale. The current study explored and discussed the suitability of four different synthetic (Landsat (L)-MOD13Q1 (30 m, 8 and 16 days) and Sentinel-2 (S)-MOD13Q1 (10 m, 8 and 16 days)) and two real (MOD13Q1 (250 m, 8 and 16 days)) NDVI products combined separately to two widely used crop growth models (CGMs) (World Food Studies (WOFOST), and the semi-empiric Light Use Efficiency approach (LUE)) for winter wheat (WW) and oil seed rape (OSR) yield forecasts in Bavaria (70,550 km\(^2\)) for the year 2019. For WW and OSR, the synthetic products’ high spatial and temporal resolution resulted in higher yield accuracies using LUE and WOFOST. The observations of high temporal resolution (8-day) products of both S-MOD13Q1 and L-MOD13Q1 played a significant role in accurately measuring the yield of WW and OSR. For example, L- and S-MOD13Q1 resulted in an R\(^2\) = 0.82 and 0.85, RMSE = 5.46 and 5.01 dt/ha for WW, R\(^2\) = 0.89 and 0.82, and RMSE = 2.23 and 2.11 dt/ha for OSR using the LUE model, respectively. Similarly, for the 8- and 16-day products, the simple LUE model (R\(^2\) = 0.77 and relative RMSE (RRMSE) = 8.17%) required fewer input parameters to simulate crop yield and was highly accurate, reliable, and more precise than the complex WOFOST model (R\(^2\) = 0.66 and RRMSE = 11.35%) with higher input parameters. Conclusively, both S-MOD13Q1 and L-MOD13Q1, in combination with LUE, were more prominent for predicting crop yields on a regional scale than the 16-day products; however, L-MOD13Q1 was advantageous for generating and exploring the long-term yield time series due to the availability of Landsat data since 1982, with a maximum resolution of 30 m. In addition, this study recommended the further use of its findings for implementing and validating the long-term crop yield time series in different regions of the world. KW - MODIS KW - Sentinel-2 KW - Landsat 8 KW - sustainable agriculture KW - decision-making KW - winter wheat KW - oil seed rape KW - resolution Y1 - 2023 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-311132 SN - 2072-4292 VL - 15 IS - 7 ER - TY - THES A1 - Kleineisel, Jonas T1 - Variational networks in magnetic resonance imaging - Application to spiral cardiac MRI and investigations on image quality T1 - Variational Networks in der Magnetresonanztomographie - Anwendung auf spirale Herzbildgebung und Untersuchungen zur Bildqualität N2 - Acceleration is a central aim of clinical and technical research in magnetic resonance imaging (MRI) today, with the potential to increase robustness, accessibility and patient comfort, reduce cost, and enable entirely new kinds of examinations. A key component in this endeavor is image reconstruction, as most modern approaches build on advanced signal and image processing. Here, deep learning (DL)-based methods have recently shown considerable potential, with numerous publications demonstrating benefits for MRI reconstruction. However, these methods often come at the cost of an increased risk for subtle yet critical errors. Therefore, the aim of this thesis is to advance DL-based MRI reconstruction, while ensuring high quality and fidelity with measured data. A network architecture specifically suited for this purpose is the variational network (VN). To investigate the benefits these can bring to non-Cartesian cardiac imaging, the first part presents an application of VNs, which were specifically adapted to the reconstruction of accelerated spiral acquisitions. The proposed method is compared to a segmented exam, a U-Net and a compressed sensing (CS) model using qualitative and quantitative measures. While the U-Net performed poorly, the VN as well as the CS reconstruction showed good output quality. In functional cardiac imaging, the proposed real-time method with VN reconstruction substantially accelerates examinations over the gold-standard, from over 10 to just 1 minute. Clinical parameters agreed on average. Generally in MRI reconstruction, the assessment of image quality is complex, in particular for modern non-linear methods. Therefore, advanced techniques for precise evaluation of quality were subsequently demonstrated. With two distinct methods, resolution and amplification or suppression of noise are quantified locally in each pixel of a reconstruction. Using these, local maps of resolution and noise in parallel imaging (GRAPPA), CS, U-Net and VN reconstructions were determined for MR images of the brain. In the tested images, GRAPPA delivers uniform and ideal resolution, but amplifies noise noticeably. The other methods adapt their behavior to image structure, where different levels of local blurring were observed at edges compared to homogeneous areas, and noise was suppressed except at edges. Overall, VNs were found to combine a number of advantageous properties, including a good trade-off between resolution and noise, fast reconstruction times, and high overall image quality and fidelity of the produced output. Therefore, this network architecture seems highly promising for MRI reconstruction. N2 - Eine Beschleunigung des Bildgebungsprozesses ist heute ein wichtiges Ziel von klinischer und technischer Forschung in der Magnetresonanztomographie (MRT). Dadurch könnten Robustheit, Verfügbarkeit und Patientenkomfort erhöht, Kosten gesenkt und ganz neue Arten von Untersuchungen möglich gemacht werden. Da sich die meisten modernen Ansätze hierfür auf eine fortgeschrittene Signal- und Bildverarbeitung stützen, ist die Bildrekonstruktion ein zentraler Baustein. In diesem Bereich haben Deep Learning (DL)-basierte Methoden in der jüngeren Vergangenheit bemerkenswertes Potenzial gezeigt und eine Vielzahl an Publikationen konnte deren Nutzen in der MRT-Rekonstruktion feststellen. Allerdings besteht dabei das Risiko von subtilen und doch kritischen Fehlern. Daher ist das Ziel dieser Arbeit, die DL-basierte MRT-Rekonstruktion weiterzuentwickeln, während gleichzeitig hohe Bildqualität und Treue der erzeugten Bilder mit den gemessenen Daten gewährleistet wird. Eine Netzwerkarchitektur, die dafür besonders geeignet ist, ist das Variational Network (VN). Um den Nutzen dieser Netzwerke für nicht-kartesische Herzbildgebung zu untersuchen, beschreibt der erste Teil dieser Arbeit eine Anwendung von VNs, welche spezifisch für die Rekonstruktion von beschleunigten Akquisitionen mit spiralen Auslesetrajektorien angepasst wurden. Die vorgeschlagene Methode wird mit einer segmentierten Rekonstruktion, einem U-Net, und einem Compressed Sensing (CS)-Modell anhand von qualitativen und quantitativen Metriken verglichen. Während das U-Net schlecht abschneidet, zeigen die VN- und CS-Methoden eine gute Bildqualität. In der funktionalen Herzbildgebung beschleunigt die vorgeschlagene Echtzeit-Methode mit VN-Rekonstruktion die Aufnahme gegenüber dem Goldstandard wesentlich, von etwa zehn zu nur einer Minute. Klinische Parameter stimmen im Mittel überein. Die Bewertung von Bildqualität in der MRT-Rekonstruktion ist im Allgemeinen komplex, vor allem für moderne, nichtlineare Methoden. Daher wurden anschließend forgeschrittene Techniken zur präsizen Analyse von Bildqualität demonstriert. Mit zwei separaten Methoden wurde einerseits die Auflösung und andererseits die Verstärkung oder Unterdrückung von Rauschen in jedem Pixel eines untersuchten Bildes lokal quantifiziert. Damit wurden lokale Karten von Auflösung und Rauschen in Rekonstruktionen durch Parallele Bildgebung (GRAPPA), CS, U-Net und VN für MR-Aufnahmen des Gehirns berechnet. In den untersuchten Bildern zeigte GRAPPA gleichmäßig eine ideale Auflösung, aber merkliche Rauschverstärkung. Die anderen Methoden verhalten sich lokal unterschiedlich je nach Struktur des untersuchten Bildes. Die gemessene lokale Unschärfe unterschied sich an den Kanten gegenüber homogenen Bildbereichen, und Rauschen wurde überall außer an Kanten unterdrückt. Insgesamt wurde für VNs eine Kombination von verschiedenen günstigen Eigenschaften festgestellt, unter anderem ein guter Kompromiss zwischen Auflösung und Rauschen, schnelle Laufzeit, und hohe Qualität und Datentreue der erzeugten Bilder. Daher erscheint diese Netzwerkarchitektur als ein äußerst vielversprechender Ansatz für MRT-Rekonstruktion. KW - Kernspintomografie KW - Convolutional Neural Network KW - Maschinelles Lernen KW - Bildgebendes Verfahren KW - magnetic resonance imaging KW - convolutional neural network KW - variational network KW - cardiac imaging KW - machine learning KW - local point-spread function KW - resolution KW - g-factor Y1 - 2024 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-347370 ER - TY - JOUR A1 - Laine, Romain F. A1 - Albecka, Anna A1 - van de Linde, Sebastian A1 - Rees, Eric J. A1 - Crump, Colin M. A1 - Kaminski, Clemens F. T1 - Structural analysis of herpes simplex virus by optical super-resolution imaging JF - Nature Communications N2 - Herpes simplex virus type-1 (HSV-1) is one of the most widespread pathogens among humans. Although the structure of HSV-1 has been extensively investigated, the precise organization of tegument and envelope proteins remains elusive. Here we use super-resolution imaging by direct stochastic optical reconstruction microscopy (dSTORM) in combination with a model-based analysis of single-molecule localization data, to determine the position of protein layers within virus particles. We resolve different protein layers within individual HSV-1 particles using multi-colour dSTORM imaging and discriminate envelope-anchored glycoproteins from tegument proteins, both in purified virions and in virions present in infected cells. Precise characterization of HSV-1 structure was achieved by particle averaging of purified viruses and model-based analysis of the radial distribution of the tegument proteins VP16, VP1/2 and pUL37, and envelope protein gD. From this data, we propose a model of the protein organization inside the tegument. KW - tegument protein pUL36 KW - fluorescence microscopy KW - monoclonal antibodies KW - 3-dimensional structure KW - type-1 KW - nuclear pore complex KW - reconstruction microscopy KW - localization microscopy KW - resolution KW - envelopment Y1 - 2015 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-144623 VL - 6 IS - 5980 ER - TY - JOUR A1 - Nanguneri, Siddharth A1 - Flottmann, Benjamin A1 - Horstmann, Heinz A1 - Heilemann, Mike A1 - Kuner, Thomas T1 - Three-Dimensional, Tomographic Super-Resolution Fluorescence Imaging of Serially Sectioned Thick Samples JF - PLoS One N2 - Three-dimensional fluorescence imaging of thick tissue samples with near-molecular resolution remains a fundamental challenge in the life sciences. To tackle this, we developed tomoSTORM, an approach combining single-molecule localization-based super-resolution microscopy with array tomography of structurally intact brain tissue. Consecutive sections organized in a ribbon were serially imaged with a lateral resolution of 28 nm and an axial resolution of 40 nm in tissue volumes of up to 50 \(\mu\)mx50\(\mu\)mx2.5\(\mu\)m. Using targeted expression of membrane bound (m)GFP and immunohistochemistry at the calyx of Held, a model synapse for central glutamatergic neurotransmission, we delineated the course of the membrane and fine-structure of mitochondria. This method allows multiplexed super-resolution imaging in large tissue volumes with a resolution three orders of magnitude better than confocal microscopy. KW - architecture KW - rat calyx KW - in-vivo KW - microscopy KW - resolution KW - proteins KW - transmission KW - ultrastructure KW - reconstruction KW - localization Y1 - 2012 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-134434 VL - 7 IS - 5 ER - TY - JOUR A1 - Willems, Coen H. M. P. A1 - Urlichs, Florian A1 - Seidenspinner, Silvia A1 - Kunzmann, Steffen A1 - Speer, Christian P. A1 - Kramer, Boris W. T1 - Poractant alfa (Curosurf (R)) increases phagocytosis of apoptotic neutrophils by alveolar macrophages in vivo JF - Respiratory Research N2 - Background: Clearance of apoptotic neutrophils in the lung is an essential process to limit inflammation, since they could become a pro-inflammatory stimulus themselves. The clearance is partially mediated by alveolar macrophages, which phagocytose these apoptotic cells. The phagocytosis of apoptotic immune cells by monocytes in vitro has been shown to be augmented by several constituents of pulmonary surfactant, e. g. phospholipids and hydrophobic surfactant proteins. In this study, we assessed the influence of exogenous poractant alfa (Curosurf (R)) instillation on the in vivo phagocytosis of apoptotic neutrophils by alveolar macrophages. Methods: Poractant alfa (200 mg/kg) was instilled intratracheally in the lungs of three months old adult male C57/Black 6 mice, followed by apoptotic neutrophil instillation. Bronchoalveloar lavage was performed and alveolar macrophages and neutrophils were counted. Phagocytosis of apoptotic neutrophils was quantified by determining the number of apoptotic neutrophils per alveolar macrophages. Results: Exogenous surfactant increased the number of alveolar macrophages engulfing apoptotic neutrophils 2.6 fold. The phagocytosis of apoptotic neutrophils was increased in the presence of exogenous surfactant by a 4.7 fold increase in phagocytosed apoptotic neutrophils per alveolar macrophage. Conclusions: We conclude that the anti-inflammatory properties of surfactant therapy may be mediated in part by increased numbers of alveolar macrophages and increased phagocytosis of apoptotic neutrophils by alveolar macrophages. KW - preterm KW - surfactant protein-A KW - respiratory-distress-syndrome KW - synthetic surfactant KW - human monocytes KW - SIRP-alpha KW - lung KW - cells KW - inflammation KW - resolution KW - anti inflammation KW - drug therapy KW - surfactant Y1 - 2012 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-130721 VL - 13 IS - 17 ER -