Surgical Planning Laboratory - Brigham & Women's Hospital - Boston, Massachusetts USA - a teaching affiliate of Harvard Medical School

Surgical Planning Laboratory

The Publication Database hosted by SPL

All Publications | Upload | Advanced Search | Gallery View | Download Statistics | Help | Import | Log in

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

1Biomedical Engineering Department, University of Iowa Iowa City, IA, USA.
2Psychiatry, University of Iowa Hospital and Clinic, Iowa City, IA, USA.
Publication Date:
Front Neuroinform
Volume Number:
Front Neuroinform. 2013 Nov 18;7:29.
PubMed ID:
segmentation, registration, inhomogeneity correction, Tissue Classification
Appears in Collections:
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.
Downloaded: 1127 times. [view map]
Paper: Download, View online
Export citation:
Google Scholar: link

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.

Additional Material
1 File (206.613kB)
Kim-FrontNeuroinform2013-fig4.jpg (206.613kB)