Automatic alignment of brain MR scout scans using data-adaptive multi-structural model

  • Authors:

  • Ting Chen;Yiqiang Zhan;Shaoting Zhang;Maneesh Dewan

  • Affiliations:

  • Department of CISE, University of Florida, Gainesville, FL;SYNGO US R&D, Siemens Healthcare, Malvern, PA;Department of Computer Science, Rutgers University, Piscataway, NJ;SYNGO US R&D, Siemens Healthcare, Malvern, PA

  • Venue:
  • MICCAI'11 Proceedings of the 14th international conference on Medical image computing and computer-assisted intervention - Volume Part II
  • Year:
  • 2011

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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