Automatic detection of active and inactive multiple sclerosis plaques using the Bayesian approach in susceptibility-weighted imaging

Abstract

BACKGROUND: Susceptibility-weighted imaging (SWI) is efficient in detecting multiple sclerosis (MS) plaques and evaluating the level of disease activity. PURPOSE: To automatically detect active and inactive MS plaques in SWI images using a Bayesian approach. MATERIAL AND METHODS: A 1.5-T scanner was used to evaluate 147 patients with MS. The area of the plaques along with their active or inactive status were automatically identified using a Bayesian approach. Plaques were given an orange color if they were active and a blue color if they were inactive, based on the preset signal intensity. RESULTS: Experimental findings show that the proposed method has a high accuracy rate of 91 and a sensitivity rate of 76 for identifying the type and area of plaques. Inactive plaques were properly identified in 87 of cases, and active plaques in 76 of cases. The Kappa analysis revealed an 80 agreement between expert diagnoses based on contrast-enhanced and FLAIR images and Bayesian inferences in SWI. CONCLUSION: The results of our study demonstrated that the proposed method has good accuracy for identifying the MS plaque area as well as for identifying the types of active or inactive plaques in SWI. Therefore, it might be helpful to use the proposed method as a supplemental tool to accelerate the specialist's diagnosis.