Spatiotemporal 3D image registration for mesoscale studies of brain development.

Comparison of brain samples representing different developmental stages often necessitates registering the samples to common coordinates. Although the available software tools are successful in registering 3D images of adult brains, registration of perinatal brains remains challenging due to rapid growth-dependent morphological changes and variations in developmental pace between animals. To address these challenges, we propose a multi-step algorithm for the registration of perinatal brains. First, we optimized image preprocessing to increase the algorithm's sensitivity to mismatches in registered images. Second, we developed an attention-gated simulated annealing (Monte Carlo) procedure capable of focusing on the differences between perinatal brains. Third, we applied classical multidimensional scaling (CMDS) to align (synchronize) brain samples in time, accounting for individual development paces. We tested this multi-step algorithm on 28 samples of whole-mounted perinatal mouse brains (P0 - P9) and observed accurate registration results. Our computational pipeline offers a runtime of several minutes per brain on a personal computer and automates brain registration tasks including mapping brain data to atlases, comparison of averaged experimental groups, and monitoring brain development dynamics.

Automatic 3D image registration for nano-resolution chemical mapping using synchrotron spectro-tomography

Nano-resolution synchrotron X-ray spectro-tomography has been demonstrated as a powerful tool for probing the three-dimensional (3D) structural and chemical heterogeneity of a sample. By reconstructing a number of tomographic data sets recorded at different X-ray energy levels, the energy-dependent intensity variation in every given voxel fingerprints the corresponding local chemistry. The resolution and accuracy of this method, however, could be jeopardized by non-ideal experimental conditions, e.g. instability in the hardware system and/or in the sample itself. Herein is presented one such case, in which unanticipated sample deformation severely degrades the data quality. To address this issue, an automatic 3D image registration method is implemented to evaluate and correct this effect. The method allows the redox heterogeneity in partially delithiated Li x Ta0.3Mn0.4O2 battery cathode particles to be revealed with significantly improved fidelity.

A Kinematic-Based Unmarked Augmented Reality Method for Large Scene Industrial Workshops

In the present study, a mobile camera positioning method based on kinematics of robot is proposed, capable of achieving far point positioning of imaging position and attitude tracking in large-scene enhancement. Orbit precision motion via the framework overhead cameras and combined with the ground system of sensor array object (e.g., mobile robot platform of various sensors) is capable of achieving the effective 3D image registration, solving any artifact that refers to a mobile robot in large-space position initialization, as well as effectively implementing the large-space no marks augmented reality, human-computer interaction and information summary. Lastly, the feasibility of the method is verified experimentally.