Novel application of chemical shift gradient echo in- and opposed-phase sequences in 3 T MRI for the detection of H-MRS visible lipids and grading of glioma

化学位移梯度回波同相和反相序列在3T磁共振成像中用于检测H-MRS可见脂质和胶质瘤分级的新应用

阅读:1

Abstract

OBJECTIVES: We evaluated the feasibility of using chemical shift gradient-echo (GE) in- and opposed-phase (IOP) imaging to grade glioma. METHODS: A phantom study was performed to investigate the correlation of (1)H MRS-visible lipids with the signal loss ratio (SLR) obtained using IOP imaging. A cross-sectional study approved by the institutional review board was carried out in 22 patients with different glioma grades. The patients underwent scanning using IOP imaging and single-voxel spectroscopy (SVS) using 3T MRI. The brain spectra acquisitions from solid and cystic components were obtained and correlated with the SLR for different grades. RESULTS: The phantom study showed a positive linear correlation between lipid quantification at 0.9 parts per million (ppm) and 1.3 ppm with SLR (r = 0.79-0.99, p < 0.05). In the clinical study, we found that SLR at the solid portions was the best measure for differentiating glioma grades using optimal cut-points of 0.064 and 0.086 with classification probabilities for grade II (SII = 1), grade III (SIII = 0.50) and grade IV (SIV = 0.89). CONCLUSIONS: The results underscore the lipid quantification differences in grades of glioma and provide a more comprehensive characterization by using SLR in chemical shift GE IOP imaging. SLR in IOP sequence demonstrates good performance in glioma grading. KEY POINTS: • Strong correlation was seen between lipid concentration and SLR obtained using IOP • IOP sequence demonstrates significant differences in signal loss within the glioma grades • SLR at solid tumour portions was the best measure for differentiation • This sequence is applicable in a research capacity for glioma staging armamentarium.

特别声明

1、本页面内容包含部分的内容是基于公开信息的合理引用;引用内容仅为补充信息,不代表本站立场。

2、若认为本页面引用内容涉及侵权,请及时与本站联系,我们将第一时间处理。

3、其他媒体/个人如需使用本页面原创内容,需注明“来源:[生知库]”并获得授权;使用引用内容的,需自行联系原作者获得许可。

4、投稿及合作请联系:info@biocloudy.com。