Improvements to the quality of MRI cluster analysis

Cluster analysis techniques are gaining widespread use for segmentation of MRI data, especially for volume measurement and 3-D display purposes. This paper describes four improvements to such techniques: (1) The use of intensity simulations to model cluster plots; (2) Correction of image nonuniformity; (3) Anisotropic smoothing of data; and (4) Automatic isolation of tissues of interest. Simulation of cluster plots allows an informed choice of pulse sequence(s) and acquisition parameters to be made. Correction of image nonuniformity and anisotropic smoothing reduce the spread of signal intensity from a single tissue thus producing significantly more compact clusters, whilst the isolation of tissues of interest prevents overlap of clusters from the tissues of interest with those not under consideration. These techniques may be used to improve the results of cluster analysis or traded off, for example to allow lower signal-to-noise images, shorter repetition time images, or fewer images to be used for segmentation.