Voxel-wise comparisons of cellular microstructure and diffusion-MRI in mouse hippocampus using 3D Bridging of Optically-clear histology with Neuroimaging Data (3D-BOND)

A key challenge in medical imaging is determining a precise correspondence between image properties and tissue microstructure. This comparison is hindered by disparate scales and resolutions between medical imaging and histology. We present a new technique, 3D Bridging of Optically-clear histology with Neuroimaging Data (3D-BOND), for registering medical images with 3D histology to overcome these limitations. Ex vivo 120 × 120 × 200 μm resolution diffusion-MRI (dMRI) data was acquired at 7 T from adult C57Bl/6 mouse hippocampus. Tissue was then optically cleared using CLARITY and stained with cellular markers and confocal microscopy used to produce high-resolution images of the 3D-tissue microstructure. For each sample, a dense array of hippocampal landmarks was used to drive registration between upsampled dMRI data and the corresponding confocal images. The cell population in each MRI voxel was determined within hippocampal subregions and compared to MRI-derived metrics. 3D-BOND provided robust voxel-wise, cellular correlates of dMRI data. CA1 pyramidal and dentate gyrus granular layers had significantly different mean diffusivity (p > 0.001), which was related to microstructural features. Overall, mean and radial diffusivity correlated with cell and axon density and fractional anisotropy with astrocyte density, while apparent fibre density correlated negatively with axon density. Astrocytes, axons and blood vessels correlated to tensor orientation

Sparse Registration - 3D Reconstruction from Pairs of 2D Line Scans

We address a new registration problem: Using a coupled pair of 2d scanners, we capture range data by freely moving the system through the scene. The registration with regard to six degrees of freedom becomes solvable due to the fact that rst, the pair of line scanners has dierent orientation, and second, we use a volume-oriented algorithm instead of commonly used surface-oriented approaches. We present a method that is based on the idea of preserving the free space represented in each of the scans. The proposed algorithm is evaluated with real range data associated with orientation estimates from an inertia sensor. Additionally, we provide quantitative results with simulated data. In both cases, the algorithm is capable to recover from large translational and moderate rotational errors in the initial conguration

Virtual reconstruction from scan to VR of architecture and landscape of a monumental park

The Monza Park, with its more than 7 square meters of green area divided between lawn and woods, its 110,000 tall trees, its 13 farmhouses, 3 historic villas, 13 m of fences and 90,000 visitors on spring Sundays, represents an irreplaceable source of wellness and sustainability for those who live near it. The pandemic situation of the 20s and 21s by reducing the movements and the possibility of coexistence of a large public in an open space has suggested the possibility of new forms of use and interaction of the same, even remotely, reproducing Virtual and Augmented Reality experiences. With this paper, the authors intend to illustrate a workflow from Scan to VR and AR applications, taking advantage of the opportunity to explore digital acquisitions and additional materials available and functional to convey the values and importance of open space and historical monuments immersed in them. The VR/AR experiences have been structured for navigation from the scale of architectural detail to the environmental one, which effectively ensures the fruition of one of the most significant and large historical walled parks in Europe. An unprecedented and still unique park made up of woods, meadows, cultivated fields, the Lambro, the farmhouses and villas, the mills inserted in an apparently natural but carefully designed environment

A Study of Projections for Key Point Based Registration of Panoramic Terrestrial 3D Laser Scans

Abstract This paper surveys state of the art image features and descriptors for the task of 3D scan registration based on panoramic reflectance images. As modern terrestrial laser scanners digitize their environment in a spherical way, the sphere has to be projected to a two-dimensional image. To this end, we evaluate the equirectangular, the cylindrical, the Mercator, the rectilinear, the Pannini, the stereographic, and the z-axis projection. We show that the Mercator and the Pannini projection outperform the other projection methods

A multisensor SLAM for dense maps of large scale environments under poor lighting conditions

This thesis describes the development and implementation of a multisensor large scale autonomous mapping system for surveying tasks in underground mines. The hazardous nature of the underground mining industry has resulted in a push towards autonomous solutions to the most dangerous operations, including surveying tasks. Many existing autonomous mapping techniques rely on approaches to the Simultaneous Localization and Mapping (SLAM) problem which are not suited to the extreme characteristics of active underground mining environments. Our proposed multisensor system has been designed from the outset to address the unique challenges associated with underground SLAM. The robustness, self-containment and portability of the system maximize the potential applications.The multisensor mapping solution proposed as a result of this work is based on a fusion of omnidirectional bearing-only vision-based localization and 3D laser point cloud registration. By combining these two SLAM techniques it is possible to achieve some of the advantages of both approaches – the real-time attributes of vision-based SLAM and the dense, high precision maps obtained through 3D lasers. The result is a viable autonomous mapping solution suitable for application in challenging underground mining environments.A further improvement to the robustness of the proposed multisensor SLAM system is a consequence of incorporating colour information into vision-based localization. Underground mining environments are often dominated by dynamic sources of illumination which can cause inconsistent feature motion during localization. Colour information is utilized to identify and remove features resulting from illumination artefacts and to improve the monochrome based feature matching between frames.Finally, the proposed multisensor mapping system is implemented and evaluated in both above ground and underground scenarios. The resulting large scale maps contained a maximum offset error of ±30mm for mapping tasks with lengths over 100m