%A Kim,Eun Young %A Johnson,Hans %D 2013 %J Frontiers in Neuroinformatics %C %F %G English %K segmentation,Registration,Inhomogeneity Correction,tissue classification,multi-center studies %Q %R 10.3389/fninf.2013.00029 %W %L %M %P %7 %8 2013-November-18 %9 Methods %+ Miss Eun Young Kim,University of Iowa,Biomedical Engineering,W278 GH,Iowa city,52242,IA,United States,eunyoung-kim@uiowa.edu %# %! Robust Multi-site MR Data Processing %* %< %T Robust multi-site MR data processing: iterative optimization of bias correction, tissue classification, and registration %U https://www.frontiersin.org/articles/10.3389/fninf.2013.00029 %V 7 %0 JOURNAL ARTICLE %@ 1662-5196 %X A robust multi-modal tool, for automated registration, bias correction, and tissue classification, has been implemented for large-scale heterogeneous multi-site longitudinal MR data analysis. This work focused on improving the an iterative optimization framework between bias-correction, registration, and tissue classification inspired from previous work. The primary contributions are robustness improvements from incorporation of following four elements: (1) utilize multi-modal and repeated scans, (2) incorporate high-deformable registration, (3) use extended set of tissue definitions, and (4) use of multi-modal aware intensity-context priors. The benefits of these enhancements were investigated by a series of experiments with both simulated brain data set (BrainWeb) and by applying to highly-heterogeneous data from a 32 site imaging study with quality assessments through the expert visual inspection. The implementation of this tool is tailored for, but not limited to, large-scale data processing with great data variation with a flexible interface. In this paper, we describe enhancements to a joint registration, bias correction, and the tissue classification, that improve the generalizability and robustness for processing multi-modal longitudinal MR scans collected at multi-sites. The tool was evaluated by using both simulated and simulated and human subject MRI images. With these enhancements, the results showed improved robustness for large-scale heterogeneous MRI processing.