Suppr超能文献

改进的人脑谷氨酸加权 CEST 图像中 B 不均匀性后处理校正方法。

Improved method for post-processing correction of B inhomogeneity in glutamate-weighted CEST images of the human brain.

机构信息

Center for Magnetic Resonance and Optical Imaging, Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA.

Graduate Group in Biochemistry and Molecular Biophysics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA.

出版信息

NMR Biomed. 2021 Jun;34(6):e4503. doi: 10.1002/nbm.4503. Epub 2021 Mar 21.

Abstract

Glutamate-weighted CEST (gluCEST) imaging is nearly unique in its ability to provide non-invasive, spatially resolved measurements of glutamate in vivo. In this article, we present an improved correction for B inhomogeneity of gluCEST images of the human brain. Images were obtained on a Siemens 7.0 T Terra outfitted with a single-volume transmit/32-channel receive phased array head coil. Numerical Bloch-McConnell simulations, fitting and data processing were performed using in-house code written in MATLAB and MEX (MATLAB executable). "Calibration" gluCEST data was acquired and fit with a phenomenological functional form first described here. The resulting surfaces were used to correct experimental data in accordance with a newly developed method. Healthy volunteers of varying ages were used for both fitted "calibration" data and corrected "experimental" data. Simulations allowed us to describe the dependence of CEST at 3.0 ppm (gluCEST) on saturation B using a new functional form, whose validity was confirmed by successful fitting to real human data. This functional form was used to parameterize surfaces over the space (B , T ), which could then be used to correct the signal from each pixel. The resulting images show less signal loss in areas of low B and greater contrast than those generated using the previously published method. We demonstrate that, using this method with appropriate nominal saturation B , the major limitation of correcting for B inhomogeneity becomes the effective flip angle of the acquisition module, rather than inability to correct for inhomogeneous saturation. The lower limit of our correction ability with respect to both saturation and acquisition B is about 40% of the nominal value. In summary, we demonstrate a more rigorous and successful approach to correcting gluCEST images for B inhomogeneity. Limitations of the method and further improvements to enable correction in regions with severe pathology are discussed.

摘要

谷氨酸加权化学交换饱和转移(glutamate-weighted chemical exchange saturation transfer,GluCEST)成像是目前唯一能够提供活体谷氨酸非侵入性、空间分辨测量的方法。在本文中,我们提出了一种改进的人脑 GluCEST 图像的 B 不均匀性校正方法。图像是在配备单容积发射/32 通道接收相控阵头部线圈的西门子 7.0T Terra 上获得的。数值 Bloch-McConnell 模拟、拟合和数据处理是使用 MATLAB 和 MEX(MATLAB 可执行文件)编写的内部代码完成的。首先在这里描述了一种经验性功能形式,用于获取和拟合“校准”GluCEST 数据。根据新开发的方法,使用得到的曲面来校正实验数据。不同年龄的健康志愿者用于拟合“校准”数据和校正“实验”数据。模拟允许我们使用新的功能形式来描述在 3.0 ppm(GluCEST)处 CEST 对饱和 B 的依赖性,该功能形式的有效性通过对真实人体数据的成功拟合得到了验证。该功能形式用于对(B,T)空间的曲面进行参数化,然后可以使用该曲面来校正每个像素的信号。得到的图像显示,与以前发表的方法相比,在低 B 区域的信号损失更小,对比度更高。我们证明,使用此方法和适当的标称饱和 B,可以将校正 B 不均匀性的主要限制转化为采集模块的有效翻转角,而不是无法校正不均匀的饱和。我们在饱和 B 和采集 B 方面的校正能力的下限分别约为标称值的 40%。总之,我们展示了一种更严格、更成功的方法来校正 GluCEST 图像的 B 不均匀性。讨论了该方法的局限性和进一步改进,以实现对严重病变区域的校正。

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验