ISBI'10 Proceedings of the 2010 IEEE international conference on Biomedical imaging: from nano to Macro
Joint analysis of fiber shape and cortical folding patterns
ISBI'10 Proceedings of the 2010 IEEE international conference on Biomedical imaging: from nano to Macro
Predicting functional brain ROIs via fiber shape models
MICCAI'11 Proceedings of the 14th international conference on Medical image computing and computer-assisted intervention - Volume Part II
| Hi-index | 0.00 |
Fiber clustering is a prerequisite step towards tract-based analysis of white mater integrity via diffusion tensor imaging (DTI) in various clinical neuroscience applications. Many methods reported in the literature used geometric or anatomic information for fiber clustering. This paper proposes a novel method that uses functional coherence as the criterion to guide the clustering of fibers derived from DTI tractography. Specifically, we represent the functional identity of a white matter fiber by two resting state fMRI (rsfMRI) time series extracted from the two gray matter voxels to which the fiber connects. Then, the functional coherence or similarity between two white matter fibers is defined as their rsfMRI time series' correlations, and the data-driven affinity propagation (AP) algorithm is used to cluster fibers into bundles. At current stage, we use the corpus callosum (CC) fibers that are the largest fiber bundle in the brain as an example. Experimental results show that the proposed fiber clustering method can achieve meaningful bundles that are reasonably consistent across different brains, and part of the clustered bundles was validated via the benchmark data provided by task-based fMRI data.