MAGNETIC-RESONANCE IMAGE SYNTHESIS USING A FLEXIBLE MODEL

Image synthesis methods are based on the hypothesis that a magnetic re sonance (MR) image with optimized contrast can be reproduced by synthe sis from three calculated basic images of T-1, T-2 and spin density. T his method, however, is limited by noise due to uncertainties in the i nitial measurements. The principal component analysis (PCA) method is based on an information theory approach that decomposes MR images into a small set of characteristic feature images. PCA images, or eigenima ges, show morphology by condensing the structural information from the source images. Eigenimages have also been shown to improve contrast-t o-noise ratio (CNR) compared with source images. In this study we have developed a method of synthesizing MR images using a flexible model, comprising a set of eigenimages derived from PCA. A matching process h as been carried out to find the best fit between the model and a synth etic image calculated from the Bloch equations. The method has been ap plied to MR images obtained from a group of patients with intracranial lesions. The images derived from the flexible model show increased le sion conspicuity, reduced artefact and comparable CNR to the directly acquired images while maintaining the MR characteristic information fo r diagnosis.