Recent developments in high field imaging have made possible the acquisition of high quality, low noise relaxographic data in reasonable imaging times. The datasets comprise a huge amount of information (>>1 million points) which makes rigorous analysis daunting. Here, the authors present results demonstrating that Principal Component Analysis (PCA) and Bayesian Decomposition (BD) provide powerful methods for relaxographic analysis of T{sub 1} recovery curves and editing of tissue type in resulting images.

Standard analysis methods for processing inversion recovery MR images traditionally have used single pixel techniques. In these techniques each pixel is independently fit to an exponential recovery, and spatial correlations in the data set are ignored. By analyzing the image as a complete dataset, improved error analysis and automatic segmentation can be achieved. Here, the authors apply principal component analysis (PCA) to a series of relaxographic images. This procedure decomposes the 3-dimensional data set into three separate images and corresponding recovery times. They attribute the 3 images to be spatial representations of gray matter (GM), white matter (WM) and cerebrospinal fluid (CSF) content.

This Environmental Baseline File summarizes and consolidates information related to the national-level transportation of commercial spent nuclear fuel. Topics address include: shipmnents of commercial spent nuclear fuel based on mostly truck and mostly rail shipping scenarios; transportation routing for commercial spent nuclear fuel sites and DOE sites; radionuclide inventories for various shipping container capacities; transportation routing; populations along transportation routes; urbanized area population densities; the impacts of historical, reasonably foreseeable, and general transportation; state-level food transfer factors; Federal Guidance Report No. 11 and 12 radionuclide dose conversion factors; and national average atmospheric conditions.