Discriminant analysis of background noise in extremity magnetic resonance images

Abstract

Since the creation of the first magnetic resonance imaging (MRI) equipment in 1974, experts have been studying the continuous improvement of image quality. This work aims to study the types of background noise in images from extremity MRI system of high-field, mainly caused by Faraday Cage problems. Phantom images of 1T equipment were investigated for this study. For the acquisition of these images, a protocol called DQA (Daily Quality Assurance) was used. For this work, 45 MRI images were acquired, which were pre-classified by an expert, and analyzed by SNR, an index that quantifies the ratio between signal and image noise, and by the multivariate statistical methods PCA + MLDA. PCA served as a statistical filter, which considerably decreased the amount of input information for MLDA. When all main components were used, MLDA showed an accuracy of 93.33% and results that allowed to discriminate background noise from these images in complementarity with SNR.