Accurate slice positioning of diagnostic MR brain images is clinically important due to their inherent anisotropic resolution. Recently, a low-res fast 3D "scout" scan has become popular as a prerequisite localizer for the positioning of these diagnostic high-res images on relevant anatomies. Automation of this "scout" scan alignment needs to be highly robust, accurate and reproducible, which can not be achieved by existing methods such as voxel-based registration. Although recently proposed "Learning Ensembles of Anatomical Patterns (LEAP)" framework [4] paves the way to high robustness through redundant anatomy feature detections, the "somewhat conflicting" accuracy and reproducibility goals can not be satisfied simultaneously from the single model-based alignment perspective. Hence, we present a data adaptive multi-structural model based registration algorithm to achieve these joint goals. We validate our system on a large number of clinical data sets (731 adult and 100 pediatric brain MRI scans). Our algorithm demonstrates > 99.5% robustness with high accuracy. The reproducibility is < 0.32° for rotation and < 0.27mm for translation on average within multiple follow-up scans for the same patient.
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