TY - JOUR A1 - Gram, Maximilian A1 - Gensler, Daniel A1 - Albertova, Petra A1 - Gutjahr, Fabian Tobias A1 - Lau, Kolja A1 - Arias-Loza, Paula-Anahi A1 - Jakob, Peter Michael A1 - Nordbeck, Peter T1 - Quantification correction for free-breathing myocardial T1ρ mapping in mice using a recursively derived description of a T\(_{1p}\)\(^{*}\) relaxation pathway JF - Journal of Cardiovascular Magnetic Resonance N2 - Background Fast and accurate T1ρ mapping in myocardium is still a major challenge, particularly in small animal models. The complex sequence design owing to electrocardiogram and respiratory gating leads to quantification errors in in vivo experiments, due to variations of the T\(_{1p}\) relaxation pathway. In this study, we present an improved quantification method for T\(_{1p}\) using a newly derived formalism of a T\(_{1p}\)\(^{*}\) relaxation pathway. Methods The new signal equation was derived by solving a recursion problem for spin-lock prepared fast gradient echo readouts. Based on Bloch simulations, we compared quantification errors using the common monoexponential model and our corrected model. The method was validated in phantom experiments and tested in vivo for myocardial T\(_{1p}\) mapping in mice. Here, the impact of the breath dependent spin recovery time T\(_{rec}\) on the quantification results was examined in detail. Results Simulations indicate that a correction is necessary, since systematically underestimated values are measured under in vivo conditions. In the phantom study, the mean quantification error could be reduced from − 7.4% to − 0.97%. In vivo, a correlation of uncorrected T\(_{1p}\) with the respiratory cycle was observed. Using the newly derived correction method, this correlation was significantly reduced from r = 0.708 (p < 0.001) to r = 0.204 and the standard deviation of left ventricular T\(_{1p}\) values in different animals was reduced by at least 39%. Conclusion The suggested quantification formalism enables fast and precise myocardial T\(_{1p}\) quantification for small animals during free breathing and can improve the comparability of study results. Our new technique offers a reasonable tool for assessing myocardial diseases, since pathologies that cause a change in heart or breathing rates do not lead to systematic misinterpretations. Besides, the derived signal equation can be used for sequence optimization or for subsequent correction of prior study results. KW - T1rho KW - radial KW - cardiac KW - correction KW - quantitative MRI KW - mapping KW - spin-lock KW - T1ρ Y1 - 2022 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-300491 VL - 24 IS - 1 ER - TY - JOUR A1 - Andelovic, Kristina A1 - Winter, Patrick A1 - Kampf, Thomas A1 - Xu, Anton A1 - Jakob, Peter Michael A1 - Herold, Volker A1 - Bauer, Wolfgang Rudolf A1 - Zernecke, Alma T1 - 2D Projection Maps of WSS and OSI Reveal Distinct Spatiotemporal Changes in Hemodynamics in the Murine Aorta during Ageing and Atherosclerosis JF - Biomedicines N2 - Growth, ageing and atherosclerotic plaque development alter the biomechanical forces acting on the vessel wall. However, monitoring the detailed local changes in wall shear stress (WSS) at distinct sites of the murine aortic arch over time has been challenging. Here, we studied the temporal and spatial changes in flow, WSS, oscillatory shear index (OSI) and elastic properties of healthy wildtype (WT, n = 5) and atherosclerotic apolipoprotein E-deficient (Apoe\(^{−/−}\), n = 6) mice during ageing and atherosclerosis using high-resolution 4D flow magnetic resonance imaging (MRI). Spatially resolved 2D projection maps of WSS and OSI of the complete aortic arch were generated, allowing the pixel-wise statistical analysis of inter- and intragroup hemodynamic changes over time and local correlations between WSS, pulse wave velocity (PWV), plaque and vessel wall characteristics. The study revealed converse differences of local hemodynamic profiles in healthy WT and atherosclerotic Apoe\(^{−/−}\) mice, and we identified the circumferential WSS as potential marker of plaque size and composition in advanced atherosclerosis and the radial strain as a potential marker for vascular elasticity. Two-dimensional (2D) projection maps of WSS and OSI, including statistical analysis provide a powerful tool to monitor local aortic hemodynamics during ageing and atherosclerosis. The correlation of spatially resolved hemodynamics and plaque characteristics could significantly improve our understanding of the impact of hemodynamics on atherosclerosis, which may be key to understand plaque progression towards vulnerability. KW - atherosclerosis KW - mouse KW - 4D flow MRI KW - aortic arch KW - flow dynamics KW - WSS KW - mapping KW - PWV KW - plaque characteristics Y1 - 2021 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-252164 SN - 2227-9059 VL - 9 IS - 12 ER - TY - JOUR A1 - Du, Shitong A1 - Lauterbach, Helge A. A1 - Li, Xuyou A1 - Demisse, Girum G. A1 - Borrmann, Dorit A1 - Nüchter, Andreas T1 - Curvefusion — A Method for Combining Estimated Trajectories with Applications to SLAM and Time-Calibration JF - Sensors N2 - Mapping and localization of mobile robots in an unknown environment are essential for most high-level operations like autonomous navigation or exploration. This paper presents a novel approach for combining estimated trajectories, namely curvefusion. The robot used in the experiments is equipped with a horizontally mounted 2D profiler, a constantly spinning 3D laser scanner and a GPS module. The proposed algorithm first combines trajectories from different sensors to optimize poses of the planar three degrees of freedom (DoF) trajectory, which is then fed into continuous-time simultaneous localization and mapping (SLAM) to further improve the trajectory. While state-of-the-art multi-sensor fusion methods mainly focus on probabilistic methods, our approach instead adopts a deformation-based method to optimize poses. To this end, a similarity metric for curved shapes is introduced into the robotics community to fuse the estimated trajectories. Additionally, a shape-based point correspondence estimation method is applied to the multi-sensor time calibration. Experiments show that the proposed fusion method can achieve relatively better accuracy, even if the error of the trajectory before fusion is large, which demonstrates that our method can still maintain a certain degree of accuracy in an environment where typical pose estimation methods have poor performance. In addition, the proposed time-calibration method also achieves high accuracy in estimating point correspondences. KW - mapping KW - continuous-time SLAM KW - deformation-based method KW - time calibration Y1 - 2020 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-219988 SN - 1424-8220 VL - 20 IS - 23 ER - TY - THES A1 - Koch, Rainer T1 - Sensor Fusion for Precise Mapping of Transparent and Specular Reflective Objects T1 - Sensorfusion zur präzisen Kartierung von transparenten und reflektierender Objekten N2 - Almost once a week broadcasts about earthquakes, hurricanes, tsunamis, or forest fires are filling the news. While oneself feels it is hard to watch such news, it is even harder for rescue troops to enter such areas. They need some skills to get a quick overview of the devastated area and find victims. Time is ticking, since the chance for survival shrinks the longer it takes till help is available. To coordinate the teams efficiently, all information needs to be collected at the command center. Therefore, teams investigate the destroyed houses and hollow spaces for victims. Doing so, they never can be sure that the building will not fully collapse while they are inside. Here, rescue robots are welcome helpers, as they are replaceable and make work more secure. Unfortunately, rescue robots are not usable off-the-shelf, yet. There is no doubt, that such a robot has to fulfil essential requirements to successfully accomplish a rescue mission. Apart from the mechanical requirements it has to be able to build a 3D map of the environment. This is essential to navigate through rough terrain and fulfil manipulation tasks (e.g. open doors). To build a map and gather environmental information, robots are equipped with multiple sensors. Since laser scanners produce precise measurements and support a wide scanning range, they are common visual sensors utilized for mapping. Unfortunately, they produce erroneous measurements when scanning transparent (e.g. glass, transparent plastic) or specular reflective objects (e.g. mirror, shiny metal). It is understood that such objects can be everywhere and a pre-manipulation to prevent their influences is impossible. Using additional sensors also bear risks. The problem is that these objects are occasionally visible, based on the incident angle of the laser beam, the surface, and the type of object. Hence, for transparent objects, measurements might result from the object surface or objects behind it. For specular reflective objects, measurements might result from the object surface or a mirrored object. These mirrored objects are illustrated behind the surface which is wrong. To obtain a precise map, the surfaces need to be recognised and mapped reliably. Otherwise, the robot navigates into it and crashes. Further, points behind the surface should be identified and treated based on the object type. Points behind a transparent surface should remain as they represent real objects. In contrast, Points behind a specular reflective surface should be erased. To do so, the object type needs to be classified. Unfortunately, none of the current approaches is capable to fulfil these requirements. Therefore, the following thesis addresses this problem to detect transparent and specular reflective objects and to identify their influences. To give the reader a start up, the first chapters describe: the theoretical background concerning propagation of light; sensor systems applied for range measurements; mapping approaches used in this work; and the state-of-the-art concerning detection and identification of transparent and specular reflective objects. Afterwards, the Reflection-Identification-Approach, which is the core of subject thesis is presented. It describes 2D and a 3D implementation to detect and classify such objects. Both are available as ROS-nodes. In the next chapter, various experiments demonstrate the applicability and reliability of these nodes. It proves that transparent and specular reflective objects can be detected and classified. Therefore, a Pre- and Post-Filter module is required in 2D. In 3D, classification is possible solely with the Pre-Filter. This is due to the higher amount of measurements. An example shows that an updatable mapping module allows the robot navigation to rely on refined maps. Otherwise, two individual maps are build which require a fusion afterwards. Finally, the last chapter summarizes the results and proposes suggestions for future work. N2 - Fast schon wöchentlich füllen Meldungen über Erdbeben, Wirbelstürme, Tsunamis oder Wald-brände die Nachrichten. Es ist hart anzusehen, aber noch viel härter trifft es die Rettungskräfte, welche dort zum Einsatz gerufen werden. Diese müssen gut trainiert sein, um sich schnell einen Überblick verschaffen zu können und um den zerstörten Bereich nach Opfern zu durchsuchen. Zeit ist hier ein seltenes Gut, denn die Überlebenschancen sinken, je länger es dauert bis Hilfe eintrifft. Für eine effektive Teamkoordination werden alle Informationen in der Einsatzzentrale gesammelt. In Trupps wird nach Opfern gesucht. Hierfür werden die zerstörten Gebäude durchsucht und alle Hohlräume inspiziert. Dabei können die Helfer oft nicht darauf vertrauen, dass die Gebäude stabil sind und nicht noch vollständig kollabieren. Hier sind Rettungsroboter eine willkommene Hilfe. Sie sind ersetzbar und können für gefährliche Aufgaben verwendet werden. Dies macht die Arbeit der Rettungstrupps sicherer. Allerdings gibt es solche Roboter noch nicht von der Stange. Sie müssten gewisse Anforderungen erfüllen, dass sie in einem solchen Szenarien einsetztbar sind. Neben Ansprüchen an die Mechanik, müsste eine 3D-Karte des Einsatzgebietes erstellen werden. Diese ist Grundlage für eine erfolgreiche Navigation (durch unebenes Terrain), sowie zur Beeinflussung der Umgebung (z.B. Tür öffnen). Die Umgebungserfassung wird über Sen-soren am Roboter durchgeführt. Heutzutage werden bevorzugt Laserscanner dafür verwendet, da sie präzise Messdaten liefern und über einen großen Messbereich verfügen. Unglücklicherweise werden Messdaten durch transparente (z.B. Glas, transparenter Kunststoff) und reflektierende Objekte (z.B. Spiegel, glänzendes Metall) verfälscht. Eine Vorbehandlung der Umgebung (z.B. abdecken der Flächen), um diese Einflüsse zu verhindern, ist verständlicherweise nicht möglich. Zusätzliche Sensoren zu verwenden birgt ebenfalls Nachteile. Das Problem dieser Objekte liegt darin, dass sie nur teilweise sichtbar sind. Dies ist abhängig vom Einfallwinkel des Laserstrahls auf die Oberfläche und vom Typ des Objektes. Dementsprechend könnnen die Messwerte bei transparenten Flächen von der Oberfläche oder vom Objekten dahinter resultieren. Im Gegensatz dazu können die Messwerte bei reflektierenden Oberflächen von der Oberfläche selbst oder von einem gespiegelten Objekt resultieren. Gespiegelte Objekte werden dabei hinter der reflektierenden Objerfläche dargestellt, was falsch ist. Um eine präzise Kartierung zu erlangen, müssen die Oberflächen zuverlässig eingetragen werden. Andernfalls würde der Roboter in diese navigieren und kollidieren. Weiterhin sollten Punkte hinter der Oberfläche abhängig von der Oberfläche behandelt werden. Bei einer trans- parenten Oberfläche müssen die Punkte in die Karte eingetragen werden, weil sie ein reelles Objekt darstellen. Im Gegensatz dazu, müssen bei einer reflektierenden Oberfläche die Messdaten dahinter gelöscht werden. Dafür ist eine Unterscheidung der Objekte zwingend. Diese Anforderungen erfüllen die momentan verfügbaren Algorithmen jedoch nicht. Aus diesem Grund befasst sich folgende Doktorarbeit mit der Problematik der Erkennung und Identifizierung transparenter und spiegelnder Objekte, sowie deren Einflüsse. Um dem Leser einen Einstieg zu geben, beschreiben die ersten Kapitel: den theoretischen Hindergrund bezüglich des Verhaltens von Licht; Sensorsysteme für die Distanzmessung; Kartierungsalgorithmen, welche in dieser Arbeit verwendet wurden; und den Stand der Technik bezüglich der Erkennung von transparenten und spiegelndend Objekten. Danach wird der Reflection-Identification-Algorithmus, welcher Basis dieser Arbeit ist, präsentiert. Hier wird eine 2D und eine 3D Implementierung beschrieben. Beide sind als ROS-Knoten verfügbar. Das anschließende Kapitel diskutiert Experimente, welche die Anwendbarkeit und Zuverlässigkeit des Algorithmus verifizieren. Für den 2D-Fall ist ein Vor- und ein Nachfilter-Modul notwendig. Nur mittels der Nachfilterung ist eine Klassifizierung der Objekte möglich. Im Gegensatz kann im 3D-Fall die Klassifizierung bereits mit der Vorfilterung erlangt werden. Dies beruht auf der höheren Anzahl an Messdaten. Weiterhin zeigt dieses Kapitel beispielhaft eine Adaptierung des TSD-SLAM Algorithmus, so dass der Roboter auf einer aktualisierten Karte navigieren kann. Dies erspart die Erstellung von zwei unabhängigen Karten und eine anschließende Fusionierung. Im letzten Kapitel werden die Ergebnisse der Arbeit zusammengefasst und ein Ausblick mit Anregungen zur Weiterarbeit gegeben. T3 - Forschungsberichte in der Robotik = Research Notes in Robotics - 16 KW - laserscanner KW - mapping KW - robotic KW - laser scanner KW - sensor fusion KW - transparent KW - specular reflective Y1 - 2018 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-163462 SN - 978-3-945459-25-6 ER - TY - JOUR A1 - Asare-Kyei, Daniel A1 - Forkuor, Gerald A1 - Venus, Valentijn T1 - Modeling Flood Hazard Zones at the Sub-District Level with the Rational Model Integrated with GIS and Remote Sensing Approaches JF - Water N2 - Robust risk assessment requires accurate flood intensity area mapping to allow for the identification of populations and elements at risk. However, available flood maps in West Africa lack spatial variability while global datasets have resolutions too coarse to be relevant for local scale risk assessment. Consequently, local disaster managers are forced to use traditional methods such as watermarks on buildings and media reports to identify flood hazard areas. In this study, remote sensing and Geographic Information System (GIS) techniques were combined with hydrological and statistical models to delineate the spatial limits of flood hazard zones in selected communities in Ghana, Burkina Faso and Benin. The approach involves estimating peak runoff concentrations at different elevations and then applying statistical methods to develop a Flood Hazard Index (FHI). Results show that about half of the study areas fall into high intensity flood zones. Empirical validation using statistical confusion matrix and the principles of Participatory GIS show that flood hazard areas could be mapped at an accuracy ranging from 77% to 81%. This was supported with local expert knowledge which accurately classified 79% of communities deemed to be highly susceptible to flood hazard. The results will assist disaster managers to reduce the risk to flood disasters at the community level where risk outcomes are first materialized. KW - climate change KW - rational model KW - community KW - flood hazard index KW - West Africa KW - GIS KW - vulnerability KW - performance KW - impact KW - risk KW - mapping KW - runoff Y1 - 2015 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-151581 VL - 7 SP - 3531 EP - 3564 ER - TY - JOUR A1 - Elseberg, Jan A1 - Borrmann, Dorit A1 - Nüchter, Andreas T1 - Algorithmic Solutions for Computing Precise Maximum Likelihood 3D Point Clouds from Mobile Laser Scanning Platforms JF - Remote Sensing N2 - Mobile laser scanning puts high requirements on the accuracy of the positioning systems and the calibration of the measurement system. We present a novel algorithmic approach for calibration with the goal of improving the measurement accuracy of mobile laser scanners. We describe a general framework for calibrating mobile sensor platforms that estimates all configuration parameters for any arrangement of positioning sensors, including odometry. In addition, we present a novel semi-rigid Simultaneous Localization and Mapping (SLAM) algorithm that corrects the vehicle position at every point in time along its trajectory, while simultaneously improving the quality and precision of the entire acquired point cloud. Using this algorithm, the temporary failure of accurate external positioning systems or the lack thereof can be compensated for. We demonstrate the capabilities of the two newly proposed algorithms on a wide variety of datasets. KW - mapping KW - calibration KW - non-rigid registration KW - mobile laser scanning KW - algorithms Y1 - 2013 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-130478 VL - 5 IS - 11 ER -