Laser-Induced Plasma Measurements Using Nd:YAG Laser and Streak Camera: Timing Considerations

We describe a streak camera system that is capable of both spatial and spectral measurements of laser-induced plasma. The system is based on a Hamamatsu C4334 streak camera and SpectraPro 2300i spectrograph. To improve the analysis of laser-induced plasma development, it is necessary to determine the timing of laser excitation in regard to the time scale on streak images. We present several methods to determine the laser signal timing on streak images—one uses the fast photodiode, and other techniques are based on the inclusion of the laser pulse directly on the streak image. A Nd:YAG laser (λ = 1064 nm, Quantel, Brilliant B) was employed as the excitation source. The problem of synchronization of the streak camera with the Q-switched Nd:YAG laser is also analyzed. A simple modification of the spectrograph enables easy switching between the spectral and spatial measurement modes.

A robust image registration interface for large volume brain atlas

Mapping the brain structures in three-dimensional accurately is critical for an in-depth understanding of the brain functions. By using the brain atlas as a hub, mapping detected datasets into a standard brain space enables efficiently use of various datasets. However, because of the heterogeneous and non-uniform characteristics of the brain structures at cellular level brought with the recently developed high-resolution whole-brain microscopes, traditional registration methods are difficult to apply to the robust mapping of various large volume datasets. Here, we proposed a robust Brain Spatial Mapping Interface (BrainsMapi) to address the registration of large volume datasets at cellular level by introducing the extract regional features of the anatomically invariant method and a strategy of parameter acquisition and large volume transformation. By performing validation on model data and biological images, BrainsMapi can not only achieve robust registration on sample tearing and streak image datasets, different individual and modality datasets accurately, but also are able to complete the registration of large volume dataset at cellular level which dataset size reaches 20 TB. Besides, it can also complete the registration of historical vectorized dataset. BrainsMapi would facilitate the comparison, reuse and integration of a variety of brain datasets.