Cortical Correspondence with Probabilistic Fiber Connectivity

Abstract. This paper presents a novel method of optimizing pointbased 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 cor...

• Type: Conference
• Year: 2009
• Where: IPMI
• Authors: Ipek Oguz, Marc Niethammer, Joshua E. Cates, Ross T. Whitaker, P. Thomas Fletcher, Clement Vachet, Martin Styner
• Keywords: Cortical Correspondence Method, Cortical Thickness Analysis, Human Cortical Surfaces, IPMI 2009, Medical Imaging