Resolution and SNR effects on carotid plaque classification

Abstract

Multicontrast-weighted MRI, which is increasingly being used in combination with automatic classification algorithms, has the potential to become a powerful tool for assessing plaque composition. The current literature, however, does not address the relationship between imaging conditions and segmentation viability well. In this study 13 carotid endarterectomy samples were imaged with a 156-μm in-plane resolution and high signal-to-noise ratio (SNR) using proton density (PD), T1, T2, and diffusion weightings. The maximum likelihood (ML) algorithm was used to classify plaque components, with sets of three contrast weighting intensities used as features. The resolution and SNR of the images were then degraded. Classification accuracy was found to be independent of in-plane resolution between 156 μm and 1250 μm, but dependent on SNR. Accuracy decreased less than 10% for degradation in SMR down to 25% of original values, and decreased sharply thereafter. The robustness of automatic classifiers makes them applicable to a wide range of imaging conditions, including standard in vivo carotid imaging scenarios. © 2006 Wiley-Liss, Inc.