Suppr超能文献

通过来自两个不同带宽的图像校正化学位移配准错误。

Correction of chemical shift misregistration by images from two different bandwidths.

机构信息

Department of Nuclear Engineering and Management, the University of Tokyo, Tokyo 113-0033, Japan.

出版信息

Magn Reson Imaging. 2012 May;30(4):583-8. doi: 10.1016/j.mri.2011.12.009. Epub 2012 Feb 6.

Abstract

One major effect caused by the different chemical shift frequencies of water and fat is the misregistration between the two components in MR images. Methods to correct misregistration are required in clinical MRI for accurate localization and artifact reduction. One of the methods uses the images scanned at opposite readout gradients to separate water and fat signal in the k-space. Its signal-to-noise ratio (SNR) achieves maximum when misregistration is around 0.9 pixels and deteriorates rapidly as the misregistration gets larger. In this work, we proposed a method to correct the chemical shift misregistration by using two data sets acquired at two different bandwidths. It is more generalized and flexible than the former method of opposite readout gradients and covers the former one as a special case. In both simulation and experiment, the new method is proved to be capable of correcting large chemical shift misregistration and maintain a good SNR.

摘要

水和脂肪的化学位移频率不同会导致磁共振成像(MRI)中两种成分的配准错误。为了在临床 MRI 中实现准确的定位和减少伪影,需要校正配准错误。一种方法是使用在相反读出梯度下扫描的图像来分离 k 空间中的水和脂肪信号。当配准误差约为 0.9 像素时,其信噪比(SNR)达到最大值,随着配准误差的增大,SNR 迅速恶化。在这项工作中,我们提出了一种使用两个不同带宽采集的数据集来校正化学位移配准的方法。与使用相反读出梯度的前一种方法相比,它更具通用性和灵活性,并涵盖了前一种方法作为特殊情况。在模拟和实验中,新方法都被证明能够校正大的化学位移配准错误,并保持良好的 SNR。

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验