Simulation of MRI cluster plots and application to neurological segmentation

Abstract

The advent of magnetic resonance imaging has provided new opportunities for volume measurement of tissues, with applications increasing dramatically in recent years. Cluster classification techniques have proved the most popular for volume measurement, yet little attention has been paid to how the choice of images for analysis affects the quality and ease of segmentation. To address this issue, we have developed a system to simulate MRI cluster plots using multicompartmental anthropomorphic software models of anatomy, and components for image contrast, signal-to-noise ratio, image nonuniformity, tissue heterogeneity, imager field strength, the partial volume effect, correlation between proton density, T1 and T2, and a variety of data preprocessing techniques. The effect of these components on tissue cluster size, shape, orientation, and separation is demonstrated. The simulation allows an informed choice of pulse sequence, acquisition parameters, and data preprocessing for cluster classification to be made as well as providing an aid to interpretation of acquired data cluster plots and a valuable educational tool. The system has been used to choose suitable images for neurological segmentation of grey matter, white matter, CSF, and multiple sclerosis lesions using spin-echo, inversion recovery, and gradient-echo pulse sequences. Constraints on image selection are discussed.