Performance Evaluation of Image Characteristics by Changing Parameters in Brain T2 Weighted Neuroimages using MRiLab Simulation

Abstract

Research has been conducted on techniques for adjusting magnetic resonance imaging (MRI) parameters to acquire images with better characteristics. In this study, we aim to confirm the optimal parameter values through quantitative evaluation and tendency analysis of T2 weighted images of the human brain acquired by adjusting the flip angle, number of excitations (NEX), and bandwidth (BW) using the user-friendly MRiLab simulation program. We also considered noise level and similarity evaluation parameters. According to the results, as NEX increases and BW decreases for a flip angle of 90°, the noise level and similarity evaluation improved, whereas they became less optimal beyond a 90° flip angle. Excessive parameter variation was found to cause additional noise and artifacts in such images, resulting in a deterioration of image clarity. Therefore, we confirmed that setting appropriate parameters is critical in magnetic resonance image acquisition.