Background Magnetic resonance imaging (MRI) is a well-developed technique in neuroscience. Limitations in applying MRI to rodent models of neuropsychiatric disorders include the large number of animals required to achieve statistical significance, and the paucity of automation tools for the critical early step in processing, brain extraction, which prepares brain images for alignment and voxel-wise statistics. New method This novel timesaving automation of template-based brain extraction (“skull-stripping”) is capable of quickly and reliably extracting the brain from large numbers of whole head images in a single step. The method is simple to install and requires minimal user interaction. Results This method is equally applicable to different types of MR images. Results were evaluated with Dice and Jacquard similarity indices and compared in 3D surface projections with other stripping approaches. Statistical comparisons demonstrate that individual variation of brain volumes are preserved. Comparison with existing methods A downloadable software package not otherwise available for extraction of brains from whole head images is included here. This software tool increases speed, can be used with an atlas or a template from within the dataset, and produces masks that need little further refinement. Conclusions Our new automation can be applied to any MR dataset, since the starting point is a template mask generated specifically for that dataset. The method reliably and rapidly extracts brain images from whole head images, rendering them useable for subsequent analytical processing. This software tool will accelerate the exploitation of mouse models for the investigation of human brain disorders by MRI

No abstract

No abstract

No abstract

This paper presents experimental results from a cognitive radar system. In the cognitive approach used, radar parameters are constantly self-monitored and self-adjusted so that radar performance remains at a level specified by the user. This is in stark contrast to traditional radar where parameters are fixed and the performance varies according to the encountered scenario. The experiments were conducted with a specially designed system, the "Cognitive Radar Experimental Workspace" or "CREW". The CREW was designed so that it can incorporate cognitive processing concepts that require adaptive feedback, memory generation, and memory exploitation. These operations can be accomplished at pulse repetition frequencies (PRFs) of up to 30 kHz, enabling real-time time adaptation. The experiment results demonstrate that a cognitive approach can improve radar performance, evaluated through tracking error, over and above that achievable with a conventional fixed parameter radar system. Although the demonstration provided is based upon a tracking example, the lessons learned apply across a wide range of radar applications.