Solution structure of an oligodeoxynucleotide containing the human N-ras codon 12 sequence refined from 1H NMR using molecular dynamics restrained by nuclear Overhauser effects

A general model is developed for segmenting magnetic resonance images using vector decomposition and probability techniques. Each voxel is assigned fractional volumes of q tissues from p differently weighted images (q < or = p + 1) in the presence of partial-volume mixing, random noise, and other tissues. Compared with the eigenimage method, fewer differently weighted images are needed for segmenting the q tissues, and the contrast-to-noise ratio in the calculated fractional volumes is improved. The model can produce composite tissue-type images similar to that of the probability methods, by comparing the fractional volumes assigned to different tissues on each voxel. A three-tissue (p = 2, q = 3) model is illustrated for segmenting three tissues from dual-echo images. It provides statistical analysis to the algebraic method. A three-compartment phantom is segmented for validation. Two clinical examples are presented.