论著摘要 |【MR】利用影像组学的临床成像档案鉴定脑血流测量自动化方法的可靠性(双语版)

2017-08-25 14:04:31 admin 19

Leveraging Clinical Imaging Archives for Radiomics: Reliability of Automated Methods for BrainVolume Measurement.

发表日期:2017.4.27    来源:Radiology. 

作者:Adduru VR1Michael AM1Helguera M1Baum SA1Moore GJ1.

作者介绍:

    1.From the Institute for Advanced Application (V.R.A., A.M.M., G.J.M.), Autism and Developmental Medicine Institute (A.M.M.), and Department of Radiology (G.J.M.), Geisinger Health System, 100 N Academy Ave, Danville, PA 17822; and Chester F. Carlson Center for ImagingScience, Rochester Institute of Technology, Rochester, NY (V.R.A., A.M.M., M.H., S.A.B.).

摘要

为了验证使用临床通过3个广泛使用的自动化工具箱:SPM(www.fil.ion.ucl.ac.uk/spm/),FreeSurfer(surfer.nmr.mgh.harvard.edu)和FSL(FMRIB软件库;牛津功能MR成像中心大脑,牛津,英国,https://fsl.fmrib.ox.ac.uk/fsl)获取的磁共振(MR)成像数据的厚层,以估算总脑体积(TBV),灰质(GM)体积(GMV)和白质(WM)体积(WMV)体积(WMV)。使用来自临床档案的MR图像,并且确定数据。应用三种方法来估计从同38例患者(年龄范围,1-71岁;平均年龄,22岁; 11名女性)的薄层研究质量的脑MR图像和临床常规厚层MR图像获得的脑体积。通过使用这些自动化方法,估测TBV,GMV和WMV。通过使用类内相关系数(ICC),对每种方法进行薄-厚层体积进行比较。SPM表现出优异的ICC(TBV,GMV和WMV分别为0.97,0.85和0.83)。 FSL的ICC(TBV,GMV和WMV分别为0.69,0.151和0.60),但均低于SPM。 FreeSurfer在TBV中ICC仅为0.63。应用SPM基于体素的形态测量法对薄层图像和内插厚层图像的调制图像显示,对于大多数脑区域WM(88.47%[346916个中的306924个体素])和GM(80.35%[467502个中的377 282个体素])与对照组相比显示出极好的ICC(0.37-0.98)。得到的结论是厚层临床质量的MR图像可以通过使用SPM可靠计算定量TBV,GMV和WMV等脑指标。

Purpose To validate the use of thick-section clinically acquired magnetic resonance (MR) imaging data for estimating total brain volume(TBV), gray matter (GM) volume (GMV), and white matter (WM) volume (WMV) by using three widely used automated toolboxes: SPM ( www.fil.ion.ucl.ac.uk/spm/ ), FreeSurfer ( surfer.nmr.mgh.harvard.edu ), and FSL (FMRIB software library; Oxford Centre for Functional MR Imaging of the Brain, Oxford, England, https://fsl.fmrib.ox.ac.uk/fsl ). Materials and Methods MR images from a clinical archive were used and data were deidentified. The three methods were applied to estimate brain volumes from thin-section research-quality brain MR images and routine thick-section clinical MR images acquired from the same 38 patients (age range, 1-71 years; mean age, 22 years; 11 women). By using these automated methods, TBV, GMV, and WMV were estimated. Thin- versus thick-section volume comparisons were made for each method by using intraclass correlation coefficients (ICCs). Results SPM exhibited excellent ICCs (0.97, 0.85, and 0.83 for TBV, GMV, and WMV, respectively). FSL exhibited ICCs of 0.69, 0.51, and 0.60 for TBV, GMV, and WMV, respectively, but they were lower than with SPM. FreeSurfer exhibited excellent ICC of 0.63 only for TBV. Application of SPM's voxel-based morphometry on the modulated images of thin-section images and interpolated thick-section images showed fair to excellent ICCs (0.37-0.98) for the majority of brain regions (88.47% [306924 of 346916 voxels] of WM and 80.35% [377 282 of 469 502 voxels] of GM). Conclusion Thick-section clinical-quality MR images can be reliably used for computing quantitative brain metrics such as TBV, GMV, and WMV by using SPM.

阅读原文:10.1148/radiol.2017161928


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