Open AccessDiscriminant analysis of background noise in extremity magnetic resonance images
Author(s) -
Carlos José Andrioli,
Carlos Eduardo Thomaz
Publication year - 2021
Language(s) - English
Resource type - Conference proceedings
DOI - 10.5753/wvc.2021.18891
Subject(s) - computer science , artificial intelligence , image quality , magnetic resonance imaging , computer vision , pattern recognition (psychology) , noise (video) , linear discriminant analysis , signal to noise ratio (imaging) , image processing , multivariate statistics , imaging phantom , machine learning , image (mathematics) , medicine , nuclear medicine , radiology , telecommunications
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.