Research Interests:
Our major research programs, which focus on PET and fMRI data analysis,
scientific visualization, and computational anatomy, are described
on the INC website at www.neurovia.umn.edu/incweb
Two of my major research interests involve surface-based anatomy
and automated feature-based image registration:
WHY SURFACE-BASED ANALYSES?
Since the cerebral/cerebellar cortex is topologically equivalent
to a 2D sheet, surface representations of the cortex facilitate
the visualization and analysis of functional activation data by
preserving important geometrical and topological relationships;
moreover, surface representations are compact, provide excellent
"visibility," and can be parameterized using 2D coordinate
systems which respect the topology of the cortical sheet. Various
approaches to flat-mapping the cerebral/cerebellar cortex have been
described by us and others, and the virtues of each approach have
been lauded. Broadly speaking, three factors may contribute to the
improved accuracy of surface-based analyses:
(i) intrasubject spatial localization, (ii) intersubject registration,
and
(iii) data analysis.
WHY STUDY FEATURE BASED REGISTRATION ALGORITHMS?
Currently, two of our research projects (Consensus Patterns in Functional
Neuroimaging and Computational Anatomy and Visualization) highlight
our central focus on modeling and visualization of spatial and temporal
patterns of functional activation in the living human brain. However,
both of these projects require high-quality image registration in
order to successfully address the research questions being investigated.
While numerous feature-based 3D registration algorithms for inter-
and intrasubject registration of PET, MRI, and fMRI brain volumes
have been proposed, the performance of such algorithms has yet to
be optimized with regard to feature hierarchy and selection. In
addition, "goodness-of-warp" criteria may vary depending
upon the research question being addressed or upon the type and
quality of MRI/fMRI data.
Selected Publications:
Kao CY, Hofer M, Sapiro G, Stem J, Rehm K, Rottenberg DA. A geometric method for automatic extraction of sulcal fundi. IEEE Trans Med Imaging. 2007 Apr;26(4):530-40.
Bernat JL, Rottenberg DA. Conscious awareness in PVS and MCS: the borderlands of neurology. Neurology. 2007 Mar 20;68(12):885-6. No abstract available.
Liang L, Rehm K, Woods RP, Rottenberg DA. Automatic segmentation of left and right cerebral hemispheres from MRI brain volumes using the graph cuts algorithm.
Neuroimage. 2007 Feb 1;34(3):1160-70.
Clark KA, Woods RP, Rottenberg DA , Toga AW, Mazziotta JC. Impact of acquisition protocols and processing streams on tissue segmentation of T1 weighted MR images. NeuroImage 29:185-202, 2006.
Sidtis JJ, Gomez C, Naoum A, Strother SC, Rottenberg DA . Mapping cerebral blood flow during speech production in hereditary ataxia. NeuroImage 31:246-254, 2006.
Ju L, Hurdal M, Stern J, Rehm K, Schaper K, Rottenberg, DA . Quantitative Evaluation of Three Cortical Surface Flattening Methods. NeuroImage 28:869-880, 2005.
Boesen K, Rehm K, Schaper K, Stoltzner S, Woods R, Lüders E, Rottenberg D . Quantitative comparison of four brain extraction algorithms. NeuroImage 22(3): 1255-1261, 2004.
Rehm K, Schaper K, Anderson J, Woods R, Stoltzner S, Rottenberg D . Putting our heads together: a consensus approach to brain/non-brain segmentation in T1-weighted MR volumes. NeuroImage 22(3): 1262-1270, 2004.
Strother S, LaConte S, Hansen LK, Anderson J, Zhang J, Pulapura S, Rottenberg D . Optimizing the fMRI data-processing pipeline using prediction and reproducibility performance metrics. NeuroImage 23, S196-S207, 2004
Sidtis JJ, Strother SC, Rottenberg DA . The effect of set on the resting state in functional neuroimaging: a role for the striatum? NeuroImage 22:1407-1413, 2004.
Rex DE, Shattuck DW, Woods RP, Narr KL, Luders E, Rehm K, Stolzner SE, Rottenberg DA , Toga AW. A meta-algorithm for brain extraction in MRI. NeuroImage 23:625-637, 2004.
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