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Robust Multi-site MR Data Processing: Iterative Optimization of Bias Correction, Tissue Classification, and Registration

Institution:
1Biomedical Engineering Department, University of Iowa Iowa City, IA, USA.
2Psychiatry, University of Iowa Hospital and Clinic, Iowa City, IA, USA.
Publication Date:
Nov-2013
Journal:
Front Neuroinform
Volume Number:
7
Pages:
29
Citation:
Front Neuroinform. 2013 Nov 18;7:29.
PubMed ID:
24302911
PMCID:
PMC3831347
Keywords:
segmentation, registration, inhomogeneity correction, Tissue Classification
Appears in Collections:
NA-MIC
Sponsors:
R01 NS040068/NS/NINDS NIH HHS/United States
R01 NS050568/NS/NINDS NIH HHS/United States
U54 EB005149/EB/NIBIB NIH HHS/United States
Generated Citation:
Kim E.Y., Johnson H.J. Robust Multi-site MR Data Processing: Iterative Optimization of Bias Correction, Tissue Classification, and Registration. Front Neuroinform. 2013 Nov 18;7:29. PMID: 24302911. PMCID: PMC3831347.
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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.

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