Myelin bilayer mapping in the human brain in vivo

PURPOSE To quantitatively map the myelin lipid-protein bilayer in the live human brain. METHODS This goal was pursued by integrating a multi-TE acquisition approach targeting ultrashort T$_{2}$ signals with voxel-wise fitting to a three-component signal model. Imaging was performed at 3 T in two healthy volunteers using high-performance RF and gradient hardware and the HYFI sequence. The design of a suitable imaging protocol faced substantial constraints concerning SNR, imaging volume, scan time, and RF power deposition. Model fitting to data acquired using the proposed protocol was made feasible through simulation-based optimization, and filtering was used to condition noise presentation and overall depiction fidelity. RESULTS A multi-TE protocol (11 TEs of 20-780 μs) for in vivo brain imaging was developed in adherence with applicable safety regulations and practical scan time limits. Data acquired using this protocol produced accurate model fitting results, validating the suitability of the protocol for this purpose. Structured, grainy texture of myelin bilayer maps was observed and determined to be a manifestation of correlated image noise resulting from the employed acquisition strategy. Map quality was significantly improved by filtering to uniformize the k-space noise distribution and simultaneously extending the k-space support. The final myelin bilayer maps provided selective depiction of myelin, reconciling competitive resolution (1.4 mm) with adequate SNR and benign noise texture. CONCLUSION Using the proposed technique, quantitative maps of the myelin bilayer can be obtained in vivo. These maps offer unique information content with potential applications in basic research, diagnosis, disease monitoring, and drug development

Five Years of SMARTnet: Data, Processing, and Improvements

SMARTnet, operated by the Astronomical Institute of the University of Bern (AIUB) and the German Aerospace Center (DLR), went online and open to the public in 2017 with two telescope stations located in Zimmerwald, Switzerland, and Sutherland, South Africa. Over its five-year operational timespan, new partners have joined while one partner left, leaving telescope stations distributed today in Australia, South Africa, and Europe. All stations combined, 9 telescopes are actively providing data to the network. New contributors are currently in the applicant phase and will, together with further stations planned by DLR, enhance the network’s capabilities. The retrieved data is used for research, collision warnings, catalogue maintenance or for deriving data products, which can be sold to third parties. For the aforementioned points, the Backbone Catalogue of Relational Debris Information (BACARDI) was developed at DLR. BACARDI processes input data received from SMARTnet to data products such as ephemerides and orbit information for telescope observation planning, and attempts to detect new objects where an association of observations to already known objects is unsuccessful. To better operate the telescope stations, a dedicated software, called SMARTies, is under development in a joint project by AIUB and DLR. With this software, the telescope stations operations can be optimized to increase the daily data acquisition. It is planned to release SMARTies as Open Source software. To avoid deteriorating accuracy of the orbital information, ephemerides forecasted by BACARDI are combined with the planning tool “Optimal Catalog Maintenance and Survey Tasking” (OMST), which will help keeping all resident space objects in the data base. Furthermore, OMST will allow to search for new objects in the vicinity of the telescopes’ field of view in so-called “dead-times”. A short introduction to SMARTnet and its requirements is given, followed by some products retrieved from SMARTnet data. Also, the complete end-to-end chain from observations to processing, forecast, and its feed-back loop to observations is presented. Lastly, selected campaigns of some of the telescopes are presented. As part of non-regular observations, supporting observations can be acquired in case of special events (e.g. DART)

Detection of Satellite Manoeuvres Using Non-Linear Kalman Filters on Passive-Optical Measurements

As part of an ongoing effort to build and maintain a data base for Space Situational Awareness, we have been developing an algorithm that employs a non-linear Kalman Filter to detect satellite manoeuvres. This methodology works directly on the astrometric angle measurements derived from passive-optical telescope observations without the need to run an orbit determination step first. In this study, we analyze the performance of this algorithm using large data sets of synthetic observations, and use it to detect and characterize several manoeuvres performed by geostationary satellites. In order to assess the capabilities and limitations of this method, we first created a large set of synthetic observations based on precisely known orbits of Galileo satellites. With that in hand, we study the effects of varying different properties of the data, such as the noise level, manoeuvre magnitude, and manoeuvre direction. As a second step, we apply the manoeuvre detection algorithm to real astrometric observations of two geostationary satellites obtained with optical telescopes of the SMARTnet sensor network, including observations during the launch and early orbit phase. Like our simulated observations, our real-world data set comprises manoeuvres of very different magnitudes and directions. In some cases, the true manoeuvre details were known to us, so we could verify our findings. We detect a number of manoeuvres in our observations, and in one case, we also accurately determine the manoeuvre epochs and ∆v-components. This is done by means of a conjunction analysis, during which we calculate the collision probability between two tracks propagated forward and backward, respectively. We then determine the manoeuvre epoch and ∆v-components at the time of maximum collision probability. We show that with this method we can determine the manoeuvre epoch to within a few seconds, and the ∆v-components to an accuracy at the cm/s-level

Myelin bilayer mapping in the human brain in vivo

PurposeTo quantitatively map the myelin lipid-protein bilayer in the live human brain.MethodsThis goal was pursued by integrating a multi-TE acquisition approach targeting ultrashort T2 signals with voxel-wise fitting to a three-component signal model. Imaging was performed at 3 T in two healthy volunteers using high-performance RF and gradient hardware and the HYFI sequence. The design of a suitable imaging protocol faced substantial constraints concerning SNR, imaging volume, scan time, and RF power deposition. Model fitting to data acquired using the proposed protocol was made feasible through simulation-based optimization, and filtering was used to condition noise presentation and overall depiction fidelity.ResultsA multi-TE protocol (11 TEs of 20-780 mu s) for in vivo brain imaging was developed in adherence with applicable safety regulations and practical scan time limits. Data acquired using this protocol produced accurate model fitting results, validating the suitability of the protocol for this purpose. Structured, grainy texture of myelin bilayer maps was observed and determined to be a manifestation of correlated image noise resulting from the employed acquisition strategy. Map quality was significantly improved by filtering to uniformize the k-space noise distribution and simultaneously extending the k-space support. The final myelin bilayer maps provided selective depiction of myelin, reconciling competitive resolution (1.4 mm) with adequate SNR and benign noise texture.ConclusionUsing the proposed technique, quantitative maps of the myelin bilayer can be obtained in vivo. These maps offer unique information content with potential applications in basic research, diagnosis, disease monitoring, and drug development.ISSN:0740-3194ISSN:1522-259