Resolution and SNR effects on carotid plaque classification.
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-microm 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 microm and 1250 microm, but dependent on SNR. Accuracy decreased less than 10% for degradation in SNR 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.
Ronen
Raphael R
RR
Department of Medical Biophysics, University of Western Ontario, London, Ontario, Canada.
Clarke
Sharon E
SE
Hammond
Robert R
RR
Rutt
Brian K
BK
D000465:Algorithms; D050197:Atherosclerosis / Q000473:pathology*; D001499:Bayes Theorem; D002340:Carotid Artery Diseases / Q000473:pathology*; D006801:Humans; D066298:In Vitro Techniques; D016013:Likelihood Functions; D008279:Magnetic Resonance Imaging / Q000379:methods*