Automatic Segmentation of Human Cortical Layer-Complexes and Architectural Areas Using Ex vivo Diffusion MRI and Its Validation

Recently, several magnetic resonance imaging contrast mechanisms have been shown to distinguish cortical substructure corresponding to selected cortical layers. Here, we investigate cortical layer and area differentiation by automatized unsupervised clustering of high-resolution diffusion MRI data. Several groups of adjacent layers could be distinguished in human primary motor and premotor cortex. We then used the signature of diffusion MRI signals along cortical depth as a criterion to detect area boundaries and find borders at which the signature changes abruptly. We validate our clustering results by histological analysis of the same tissue. These results confirm earlier studies which show that diffusion MRI can probe layer-specific intracortical fiber organization and, moreover, suggests that it contains enough information to automatically classify architecturally distinct cortical areas. We discuss the strengths and weaknesses of the automatic clustering approach and its appeal for MR-based cortical histology

Observing glacier elevation changes from spaceborne optical and radar sensors – an inter-comparison experiment using ASTER and TanDEM-X data

Observations of glacier mass changes are key to understanding the response of glaciers to climate change and related impacts, such as regional runoff, ecosystem changes, and global sea-level rise. Spaceborne optical and radar sensors make it possible to quantify glacier elevation changes, and thus multi-annual mass changes, on a regional and global scale. However, estimates from a growing number of studies show a wide range of results with differences often beyond uncertainty bounds. Here, we present the outcome of a community-based inter-comparison experiment using spaceborne optical stereo (ASTER) and synthetic aperture radar interferometry (TanDEM-X) data to estimate elevation changes for defined glaciers and target periods that pose different assessment challenges. Using provided or self-processed digital elevation models (DEMs) for five test sites, 12 research groups provided a total of 97 spaceborne elevation-change datasets using various processing strategies. Validation with airborne data showed that using an ensemble estimate is promising to reduce random errors from different instruments and processing methods, but still requires a more comprehensive investigation and correction of systematic errors. We found that scene selection, DEM processing, and co-registration have the biggest impact on the results. Other processing steps, such as treating spatial data voids, differences in survey periods, or radar penetration, can still be important for individual cases. Future research should focus on testing different implementations of individual processing steps (e.g. co-registration) and addressing issues related to temporal corrections, radar penetration, glacier area changes, and density conversion. Finally, there is a clear need for our community to develop best practices, use open, reproducible software, and assess overall uncertainty in order to enhance inter-comparison and empower physical process insights across glacier elevation-change studies

Re-Processing of SAR data for derivation of glaciological parameters on the Antarctic Peninsula: First results of a study at Wordie Ice Shelf

The Antarctic Peninsula is one of the world`s most affected regions by Climate Change. Long-term remote sensing time series enable to study changes and to reveal information on the underlying processes of the cryosphere as well as the interlinkages with the atmosphere. The German Antarctic Recieving Station (GARS) at O'Higgins operated by the German Remote Sensing Data Center (DFD/DLR) has acquired data from the two European Space Agency (ESA) European Remote Sensing satellite mission (ERS-)1/2 between 1991 and 2011. Data of other space borne SAR sensors such as ESA`s ENVISAT ASAR, JAXA`s (Japan Aerospace Exploration Agency) ALOS PALSAR, DLR`s TerraSAR-X and TanDEM-X or the European mission Sentinel-1 will complement to a dense time series of SAR measurements from the 1990s until today for several regions of the Antarctic Peninsula. Differential interferometric synthetic radar (DInSAR) methods and intensity tracking are applied inorder to derive important glaciological parameters such as grounding line positions, glacier velocities, surface elevations, ice mass fluxes and glacier mass balances. Additionally, calibrated SAR amplitude images as well as images taken by optical sensors (e.g. Landsat) are used to map glacier extends and to compute changes of glacier areas. We represent first results of a case study at the Wordie Ice Shelf, located at the south-western side of the Antarctic Peninsula. This ice shelf disintegrated in a series of events during the 1970s and 1980s, so that already in the beginning of the 1990s only disconnected and retreating tidewater glaciers remained. Due to the loss of the buttressing effect of the ice shelf, an increased ice mass discharge has been observed. An increase of flow speeds and elevation decrease have been reported by previous studies – mainly on a bi-temporal basis. However, how long and how exactly in time this process of adaption to the new boundary conditions will last as well as how much ice mass loss and sea level rise is caused by this process is yet not well known. Thus we use dense SAR time series in conjunction with data on surface elevation from photogrammetry and laser/radar altimetry, ground penetrating radar as well as surface mass balance simulations to target more precise estimates as well as data sets that can be better compared with large-scale observations by the GRACE gravimetry mission

Histological validation of high-resolution DTI in human post mortem tissue

Diffusion tensor imaging (DTI) is amongst the simplest mathematical models available for diffusion magnetic resonance imaging, yet still by far the most used one. Despite the success of DTI as an imaging tool for white matter fibers, its anatomical underpinnings on a microstructural basis remain unclear. In this study, we used 65 myelin-stained sections of human premotor cortex to validate modeled fiber orientations and oft used microstructure-sensitive scalar measures of DTI on the level of individual voxels. We performed this validation on high spatial resolution diffusion MRI acquisitions investigating both white and gray matter. We found a very good agreement between DTI and myelin orientations with the majority of voxels showing angular differences less than 10°. The agreement was strongest in white matter, particularly in unidirectional fiber pathways. In gray matter, the agreement was good in the deeper layers highlighting radial fiber directions even at lower fractional anisotropy (FA) compared to white matter. This result has potentially important implications for tractography algorithms applied to high resolution diffusion MRI data if the aim is to move across the gray/white matter boundary. We found strong relationships between myelin microstructure and DTI-based microstructure-sensitive measures. High FA values were linked to high myelin density and a sharply tuned histological orientation profile. Conversely, high values of mean diffusivity (MD) were linked to bimodal or diffuse orientation distributions and low myelin density. At high spatial resolution, DTI-based measures can be highly sensitive to white and gray matter microstructure despite being relatively unspecific to concrete microarchitectural aspects

Automatic segmentation of human cortical layer-complexes and architectural areas using ex vivo diffusion MRI and its validation

Recently, several magnetic resonance imaging contrast mechanisms have been shown to distinguish cortical substructure corresponding to selected cortical layers. Here, we investigate cortical layer and area differentiation by automatized unsupervised clustering of high-resolution diffusion MRI data. Several groups of adjacent layers could be distinguished in human primary motor and premotor cortex. We then used the signature of diffusion MRI signals along cortical depth as a criterion to detect area boundaries and find borders at which the signature changes abruptly. We validate our clustering results by histological analysis of the same tissue. These results confirm earlier studies which show that diffusion MRI can probe layer-specific intracortical fiber organization and, moreover, suggests that it contains enough information to automatically classify architecturally distinct cortical areas. We discuss the strengths and weaknesses of the automatic clustering approach and its appeal for MR-based cortical histology