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  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 degrees for rotation and < 0.27mm for translation on average within multiple follow-up scans for the same patient.
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