Subject motion in a large number of diffusion weighted images (DWIs) for <i>q</i>-space analysis was detected and corrected by using a simple protocol to add multiple interleaved b0 images between each DWI set and at the very end of data acquisition. The realignment matrix was determined from each <i>b</i>0 image with respect to the first <i>b</i>0 image and the matrix was used to realign not only the <i>b</i>0 image itself but also its subsequent DWI set. Degree of improvement in <i>q</i>-space analysis was estimated by calculating total residual sum of squares (RSS) in bi-exponential curve fitting process and also on the fractional anisotropy (FA) of zero displacement (ZDP). The large RSS regions were considerably diminished by realignment at the edges between cerebral gyri and sulci and at the ventricle boundaries in the original images. The large RSS regions around basal ganglia and near the ventricles were kept even by realignment but considerably suppressed with the averaged <i>b</i>0 image for decay-curve estimation. The volume average of RSS was reduced by the maximum of 77% in four volunteers’ results with both the realignment and the averaged <i>b</i>0 images. The FA-ZDP images revealed the improvement by realignment such as the contrast of corpus callosum and suppression of abnormal FA at cerebral sulcus. The number of additional <i>b</i>0 images accounted for 3% of the total number of DWIs, which suggests its feasibility for future clinical application.
The paper presents a simple graph cuts algorithm based edges features to object segmentation problems. The user gives some scribbles to background and foreground of an image. Gaussian mixture models(GMMs) are built based on the scribbles. The pixel without scribble belongs to the background or the foreground depending on the relative probability of each pixel. The contribution of our paper is to add edges features to GMMs. The approach is applied with images from the Grab cuts segmentation database. The approach is suitable for images with noise and in the foreground and background with similar colors.