Journal article
Robust Multi-site MR Data Processing: Iterative Optimization of Bias Correction, Tissue Classification, and Registration
Frontiers in Neuroinformatics, Vol.7, pp.29-29
11/01/2013
DOI: 10.3389/fninf.2013.00029
PMCID: PMC3831347
PMID: 24302911
Abstract
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 dramatic improvement in accuracy and robustness for large-scale heterogeneous MRI processing.
Details
- Title: Subtitle
- Robust Multi-site MR Data Processing: Iterative Optimization of Bias Correction, Tissue Classification, and Registration
- Creators
- Hans J Johnson
- Resource Type
- Journal article
- Publication Details
- Frontiers in Neuroinformatics, Vol.7, pp.29-29
- DOI
- 10.3389/fninf.2013.00029
- PMID
- 24302911
- PMCID
- PMC3831347
- NLM abbreviation
- Front Neuroinform
- ISSN
- 1662-5196
- Publisher
- Frontiers Media S.A
- Language
- English
- Date published
- 11/01/2013
- Academic Unit
- Roy J. Carver Department of Biomedical Engineering; Electrical and Computer Engineering; Psychiatry
- Record Identifier
- 9984185466802771
Metrics
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