Search

Introduction: In the study of neurodegenerative diseases, the possibility to follow the fate of specific cells or molecules within the whole body would be a milestone to better understand the complex evolution of disease mechanisms and to monitor the effects of therapies. The techniques available today do not allow the visualization of disease-relevant cells within the whole tridimensional biological context at high spatial resolution.Methods: Here we show the results from the first validation steps of a novel approach: by combining the conjugate nanobodies anti-glial fibrillary acidic protein (GFAP) and metal-nanoparticles (i.e. 2 nm gold NP) with X-ray phase contrast tomography (XPCT) we would be able to obtain a tridimensional visualization and identification of cells of interest together with the surrounding tissue and the vascular and neuronal networks.Results: By exploiting the X-ray attenuation properties of metal nanoparticles and the specific targeting capabilities of nanobodies, we could give XPCT the specificity it presently lacks, making it no longer a pure morphological but a molecular and targeted imaging technique. In our case, we synthesized and characterized Gold-NP/GFAP nanobody to target the astrocytes of mouse brain.Discussion: The results of the first tests presented in this paper have provided us with information on the feasibility of the approach, encouraging us to carry out further experiments in order to achieve the ultimate goal of setting up this new imaging technique.

Adding supplementary texture and 2D image-based annotations to 3D surface models is a useful next step for domain specialists to make use of photorealistic products of laser scanning and photogrammetry. This requires a registration between the new camera imagery and the model geometry to be solved, which can be a time-consuming task without appropriate automation. The increasing availability of photorealistic models, coupled with the proliferation of mobile devices, gives users the possibility to complement their models in real time. Modern mobile devices deliver digital photographs of increasing quality, as well as on-board sensor data, which can be used as input for practical and automatic camera registration procedures. Their familiar user interface also improves manual registration procedures. This paper introduces a fully automatic pose estimation method using the on-board sensor data for initial exterior orientation, and feature matching between an acquired photograph and a synthesised rendering of the orientated 3D scene as input for fine alignment. The paper also introduces a user-friendly manual camera registration- and pose estimation interface for mobile devices, based on existing surface geometry and numerical optimisation methods. The article further assesses the automatic algorithm’s accuracy compared to traditional methods, and the impact of computational- and environmental parameters. Experiments using urban and geological case studies show a significant sensitivity of the automatic procedure to the quality of the initial mobile sensor values. Changing natural lighting conditions remain a challenge for automatic pose estimation techniques, although progress is presented here. Finally, the automatically-registered mobile images are used as the basis for adding user annotations to the input textured model.

Texture mapping is a common method for combining surface geometry with image data, with the resulting photorealistic 3D models being suitable not only for visualization purposes but also for interpretation and spatiameasurement, in many application fields, such as cultural heritage and the earth sciences. When acquiring images for creation of photorealistic models, it is usual to collect more data than is finally necessary for the texturing process. Images may be collected from multiple locations, sometimes with different cameras or lens configurations and large amounts of overlap may exist. Consequently, much redundancy may be present, requiring sorting to choose the most suitable images to texture the model triangles. This paper presents a framework for visualization and analysis of the geometric relations between triangles of the terrain model and covering image sets. The application provides decision support for selection of an image subset optimized for 3D model texturing purposes, for non-specialists. It aims to improve the communication of geometrical dependencies between model triangles and the available digital images, through the use of static and interactive information visualization methods. The tool was used for computer-aided selection of image subsets optimized for texturing of 3D geological outcrop models. The resulting textured models were of high quality and with a minimum of missing texture, and the time spent in time-consuming reprocessing was reduced. Anecdotal evidence indicated that an increased user confidence in the final textured model quality and completeness makes the framework highly beneficial.