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PT  - JOURNAL ARTICLE
AU  - Fielden, S.W.
AU  - Beiler, D.
AU  - Cauley, K.A.
AU  - Troiani, V.
TI  - A Comparison of Global Brain Volumetrics Obtained from CT versus MRI Using 2 Publicly Available Software Packages
AID  - 10.3174/ajnr.A7403
DP  - 2022 Feb 01
TA  - American Journal of Neuroradiology
PG  - 245--250
VI  - 43
IP  - 2
4099  - http://www.ajnr.org/content/43/2/245.short
4100  - http://www.ajnr.org/content/43/2/245.full
SO  - Am. J. Neuroradiol.2022 Feb 01; 43
AB  - BACKGROUND AND PURPOSE: Brain volumetrics have historically been obtained from MR imaging data. However, advances in CT, along with refined publicly available software packages, may support tissue-level segmentations of clinical CT images. Here, brain volumetrics obtained by applying two publicly available software packages to paired CT-MR data are compared.MATERIALS AND METHODS: In a group of patients (n = 69; 35 men) who underwent both MR imaging and CT brain scans within 12 months of one another, brain tissue was segmented into WM, GM, and CSF compartments using 2 publicly available software packages: Statistical Parametric Mapping and FMRIB Software Library. A subset of patients with repeat imaging sessions was used to assess the repeatability of each segmentation. Regression analysis and Bland-Altman limits of agreement were used to determine the level of agreement between segmented volumes.RESULTS: Regression analysis showed good agreement between volumes derived from MR images versus those from CT. The correlation coefficients between the 2 methods were 0.93 and 0.98 for Statistical Parametric Mapping and FMRIB Software Library, respectively. Differences between global volumes were significant (P &amp;lt; .05) for all volumes compared within a given segmentation pipeline. WM bias was 36% (SD, 38%) and 18% (SD, 18%) for Statistical Parametric Mapping and FMRIB Software Library, respectively, and 10% (SD, 30%) and 6% (SD, 20%) for GM (bias ± limits of agreement), with CT overestimating WM and underestimating GM compared with MR imaging. Repeatability was good for all segmentations, with coefficients of variation of &amp;lt;10% for all volumes.CONCLUSIONS: The repeatability of CT segmentations using publicly available software is good, with good correlation with MR imaging. With careful study design and acknowledgment of measurement biases, CT may be a viable alternative to MR imaging in certain settings.BVbrain volumeCNRcontrast-to-noise ratioDARTELDiffeomorphic Anatomical Registration Through Exponentiated Lie AlgebraICVintercranial volumeLoAlimits of agreementSPMStatistical Parametric Mapping