Image synthesis methods are based on the hypothesis that a magnetic resonance (MR) image with optimized contrast can be reproduced by synthesis from three calculated basic images of T1, T2 and spin density. This method, however, is limited by noise due to uncertainties in the initial 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 eigenimages, show morphology by condensing the structural information from the source images. Eigenimages have also been shown to improve contrast-to-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 has been carried out to find the best fit between the model and a synthetic image calculated from the Bloch equations. The method has been applied to MR images obtained from a group of patients with intracranial lesions. The images derived from the flexible model show increased lesion conspicuity, reduced artefact and comparable CNR to the directly acquired images while maintaining the MR characteristic information for diagnosis.