Tirer parti des archives d'imagerie clinique pour Radiomics: fiabilité des méthodes automatisées pour la mesure du volume du cerveau

Aperçu: G.M.

En utilisant ces méthodes automatisées, le volume total du cerveau (TBV), le volume de la matière grise (GM) et le volume de la matière blanche (WM) ont été estimés en utilisant trois Boîtes à outils automatiques largement utilisées: SPM , FreeSurfer  et FSL.

Des comparaisons de volume en couches minces contre épaisseur ont été faites pour chaque méthode en utilisant des coefficients de corrélation intraclasse (ICC). Les résultats SPM ont montré d'excellents ICC (0,97, 0,85 et 0,83 pour TBV, GMV et WMV, respectivement).

Des images IRM de qualité clinique pour l'épaisseur peuvent être utilisées avec fiabilité pour calculer des paramètres quantitatifs du cerveau tels que TBV, GMV et WMV en utilisant SPM.  

Radiology. 2017 Apr 27:161928. doi: 10.1148/radiol.2017161928.

Leveraging Clinical Imaging Archives for Radiomics: Reliability of Automated Methods for Brain Volume Measurement

Adduru VR¹, Michael AM¹, Helguera M¹, Baum SA¹, Moore GJ¹.

Author information

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 Imaging Science, Rochester Institute of Technology, Rochester, NY (V.R.A., A.M.M., M.H., S.A.B.).

Abstract

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. ^© RSNA, 2017 Online supplemental material is available for this article.

PMID: 28448234

DOI: 10.1148/radiol.2017161928