title: Cortical correspondence with probabilistic fiber connectivity.
abstract: This paper presents a novel method of optimizing point-based correspondence among populations of human cortical surfaces by combining structural cues with probabilistic connectivity maps. The proposed method establishes a tradeoff between an even sampling of the cortical surfaces (a low surface entropy) and the similarity of corresponding points across the population (a low ensemble entropy). The similarity metric, however, isn't constrained to be just spatial proximity, but uses local sulcal depth measurements as well as probabilistic connectivity maps, computed from DWI scans via a stochastic tractography algorithm, to enhance the correspondence definition. We propose a novel method for projecting this fiber connectivity information on the cortical surface, using a surface evolution technique. Our cortical correspondence method does not require a spherical parameterization. Experimental results are presented, showing improved correspondence quality demonstrated by a cortical thickness analysis, as compared to correspondence methods using spatial metrics as the sole correspondence criterion.
authors.affiliation: Department of Computer Science, University of North Carolina, Chapel Hill, NC, USA. ipek@cs.unc.edu
authors.forename: Ipek
authors.initials: I
authors.lastname: Oguz
authors.forename: Marc
authors.initials: M
authors.lastname: Niethammer
authors.forename: Josh
authors.initials: J
authors.lastname: Cates
authors.forename: Ross
authors.initials: R
authors.lastname: Whitaker
authors.forename: Thomas
authors.initials: T
authors.lastname: Fletcher
authors.forename: Clement
authors.initials: C
authors.lastname: Vachet
authors.forename: Martin
authors.initials: M
authors.lastname: Styner
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D001921:Brain / Q000033:anatomy & histology*; D003627:Data Interpretation, Statistical; D038524:Diffusion Magnetic Resonance Imaging / Q000379:methods*; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods*; D009413:Nerve Fibers, Myelinated / Q000648:ultrastructure*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity
references.citation: Magn Reson Med. 2005 May;53(5):1143-9
references.pmid: 15844149
references.citation: Inf Process Med Imaging. 2007;20:333-45
references.pmid: 17633711
references.citation: Med Image Anal. 2004 Sep;8(3):295-309
references.pmid: 15450224
references.citation: Hum Brain Mapp. 1999;8(4):272-84
references.pmid: 10619420
references.citation: Med Image Comput Comput Assist Interv. 2005;8(Pt 1):107-14
references.pmid: 16685835
references.citation: Med Image Anal. 2005 Oct;9(5):457-66
references.pmid: 16019252
references.citation: Magn Reson Med. 2000 Oct;44(4):625-32
references.pmid: 11025519
references.citation: Neuroimage. 1999 Feb;9(2):195-207
references.pmid: 9931269
references.citation: Med Image Anal. 1998 Dec;2(4):303-14
references.pmid: 10072198
references.citation: IEEE Trans Med Imaging. 2002 May;21(5):525-37
references.pmid: 12071623
references.citation: Magn Reson Med. 2003 Nov;50(5):1077-88
references.pmid: 14587019
references.citation: Inf Process Med Imaging. 2005;19:480-92
references.pmid: 17354719
references.citation: Inf Process Med Imaging. 2003 Jul;18:63-75
references.pmid: 15344447