Automatic Alignment of Brain MR Scout Scans Using Data-adaptive Multi-structural Model

Abstract. 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.