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Original Research ARTICLE Provisionally accepted The full-text will be published soon. Notify me

Front. Neurosci. | doi: 10.3389/fnins.2019.00909

Linear Registration of brain MRI using knowledge-based multiple intermediator libraries

Xinyuan Zhang1, Yanqiu Feng1, Qianjin Feng1, Wufan Chen1, Xin Li2,  Andreia V. Faria2 and  Susumu Mori2, 3*
  • 1Southern Medical University, China
  • 2Johns Hopkins University, United States
  • 3Bologna Institute for Policy Research, School of Advanced International Studies, Johns Hopkins University, United States

Linear registration is often the crucial first step for various types of image analysis. Although this is mathematically simple, failure is not uncommon. When investigating the brain by magnetic resonance imaging (MRI), the brain is the target organ for registration but the existence of other tissues, in addition to a variety of fields of view, different brain locations, orientations and anatomical features, poses some serious fundamental challenges. Consequently, a number of different algorithms have been put forward to minimize potential errors. In the present study, we tested a knowledge-based approach that can be combined with any form of registration algorithm. This approach consisted of a library of intermediate images (mediators) with known transformation to the target image. Test images were first registered to all mediators and the best mediator was selected to ensure optimum registration to the target. In order to select the best mediator, we evaluated two similarity criteria: the sum of squared differences and mutual information. This approach was applied to 48 mediators and 96 test images. In order to reduce one of the main drawbacks of the approach, increased computation time, we reduced the size of the library by clustering. Our results indicated clear improvement in registration accuracy.

Keywords: Linear registration, mediator selection, T1-weighted brain image, MNI space, Dice value

Received: 25 Mar 2019; Accepted: 15 Aug 2019.

Copyright: © 2019 Zhang, Feng, Feng, Chen, Li, Faria and Mori. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Dr. Susumu Mori, Johns Hopkins University, Baltimore, 21218, Maryland, United States, smori1@jhmi.edu