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Early adolescence is a time of rapid change in neuroanatomy and sexual development. Precision in tracking changes in brain morphology with structural MRI requires image segmentation with minimal error. Here, we compared two approaches to achieve segmentation by image registration with an atlas to quantify regional brain structural development over a 7-month interval in normal, early adolescent boys and girls. Adolescents were scanned twice (average interval=7.3 months), yielding adequate data for analysis in 16 boys (baseline age 10.9 to 13.9 years; Tanner Stage=1 to 4) and 12 girls (baseline age=11.2 to 13.7 years; Tanner Stage=3 to 4). Brain volumes were derived from T1-weighted (SPGR) images and dual-echo Fast Spin-Echo (FSE) images collected on a GE 3T scanner with an 8-channel phased-array head coil and analyzed by registration-based parcellation using the SRI24 atlas. The "independent" method required two inter-subject registrations: both baseline (MRI 1) to atlas and follow-up (MRI 2) to the atlas. The "sequential" method required one inter-subject registration, which was MRI 1 to the atlas, and one intra-subject registration, which was MRI 2 to MRI 1. Gray matter/white matter/CSF were segmented in both MRI-1 and MRI-2 using FSL FAST with tissue priors also based on the SRI24 atlas. Gray matter volumes were derived for 10 cortical regions, gray+white matter volumes for 5 subcortical structures, and CSF volumes for 4 ventricular regions and the cortical sulci. Across the 15 tissue regions, the coefficient of variation (CV) of change scores across individuals was significantly lower for the sequential method (CV=3.02), requiring only one inter-subject registration, than for the independent method (CV=9.43), requiring two inter-subject registrations. Volume change based on the sequential method revealed that total supratentorial and CSF volumes increased, while cortical gray matter volumes declined significantly (p<0.01) in anterior (lateral and medial frontal, anterior cingulate, precuneus, and parietal) but not posterior (occipital, calcarine) cortical regions. These volume changes occurred in all boys and girls who advanced a step in Tanner staging. Subcortical structures did not show consistent changes. Thus, longitudinal MRI assessment using robust registration methods is sufficiently sensitive to identify significant regional brain changes over a 7-month interval in boys and girls in early adolescence. Increasing the temporal resolution of the retest interval in longitudinal developmental studies could increase accuracy in timing of peak growth of regional brain tissue and refine our understanding of the neural mechanisms underlying the dynamic changes in brain structure throughout adolescence.
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PMID:Developmental change in regional brain structure over 7 months in early adolescence: comparison of approaches for longitudinal atlas-based parcellation. 2151 Oct 39

The emerging era of ultra-high-field MRI using 7T MRI scanners dramatically improved sensitivity, image resolution, and tissue contrast when compared to 3T MRI scanners in examining various anatomical structures. The advantages of these high-resolution MR images include higher segmentation accuracy of MRI brain tissues. However, currently, accessibility to 7T MRI scanners remains much more limited than 3T MRI scanners due to technological and economical constraints. Hence, we propose in this work the first learning-based model that improves the segmentation of an input 3T MR image with any conventional segmentation method, through the reconstruction of a higher-quality 7T-like MR image, without actually acquiring an ultra-high-field 7T MRI. Our proposed framework comprises two main steps. First, we estimate a non-linear mapping from 3T MRI to 7T MRI space, using random forest regression model with novel weighting and ensembling schemes, to reconstruct initial 7T-like MR images. Second, we use a group sparse representation with a new pre-selection approach to further refine the 7T-like MR image reconstruction. We evaluated our 7T MRI reconstruction results along with their segmentation results using 13 subjects acquired with both 3T and 7T MR images. For tissue segmentation, we applied two widely used segmentation methods (FAST and SPM) to perform the experiments. Our results showed (1) the improvement of WM, GM and CSF brain tissues segmentation results when guided by reconstructed 7T-like images compared to 3T MR images, and (2) the outperformance of the proposed 7T MRI reconstruction method when compared to other state-of-the-art methods.
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PMID:7T-Guided Learning Framework for Improving the Segmentation of 3T MR Images. 2814 68