Reliability of longitudinal brain volume loss measurements between 2 sites in patients with multiple sclerosis: comparison of 7 quantification techniques.

BACKGROUND AND PURPOSE: Brain volume loss is currently a MR imaging marker of neurodegeneration in MS. Available quantification algorithms perform either direct (segmentation-based techniques) or indirect (registration-based techniques) measurements. Because there is no reference standard technique, the assessment of their accuracy and reliability remains a difficult goal. Therefore, the purpose of this work was to assess the robustness of 7 different postprocessing algorithms applied to images acquired from different MR imaging systems. MATERIALS AND METHODS: Nine patients with MS were followed longitudinally over 1 year (3 time points) on two 1.5T MR imaging systems. Brain volume change measures were assessed using 7 segmentation algorithms: a segmentation-classification algorithm, FreeSurfer, BBSI, KN-BSI, SIENA, SIENAX, and JI algorithm. RESULTS: Intersite variability showed that segmentation-based techniques and SIENAX provided large and heterogeneous values of brain volume changes. A Bland-Altman analysis showed a mean difference of 1.8%, 0.07%, and 0.79% between the 2 sites, and a wide length agreement interval of 11.66%, 7.92%, and 11.94% for the segmentation-classification algorithm, FreeSurfer, and SIENAX, respectively. In contrast, registration-based algorithms showed better reproducibility, with a low mean difference of 0.45% for BBSI, KN-BSI and JI, and a mean length agreement interval of 1.55%. If SIENA obtained a lower mean difference of 0.12%, its agreement interval of 3.29% was wider. CONCLUSIONS: If brain atrophy estimation remains an open issue, future investigations of the accuracy and reliability of the brain volume quantification algorithms are needed to measure the slow and small brain volume changes occurring in MS.