Abstracts Category : Other

Automated Signal to Noise Ratio Analysis for MagneticResonance Imaging Using a Noise Distribution Model

by Abdullah M Aldokhail

Signal to noise ratio (SNR) is a fundamental index ofimage quality in Magnetic Resonance Imaging (MRI) and it hastraditionally been considered as a sensitive test to monitor theperformance of MRI systems. As part of quality assurance program,SNR measurement should have a high level of reproducibility andaccuracy. Currently accepted methods to evaluate SNR rely uponmanual analysis and are observer dependent. The goal of thisproject is to develop a robust automated method for SNR analysis toeliminate individuals variability when performing such a test.This method uses a macro file that has been written using imageJsoftware to automatically segment the phantom from the backgroundregion, measure the mean signal from an area covering 80% of thephantom and determine the noise by fitting a noise distributionmodel to the background histogram. Fifty-four phantom scans from avariety of RF coils were used to compare the automated SNR analysismethod with the manual SNR methods described in the ACR QC Manual.The automated SNR method proved to be accurate, highlyreproducible, and relatively insensitive to the existence of minorartifacts in the noise measurement region.Advisors/Committee Members: Parsai, E. Ishmael (Committee Chair).