Numerous reports have shown that fiber tractography suffers from a difficult sensitivity-specificity tradeoff. We present an approach that leverages knowledge about certain well studied tracts (anchor tracts) in a tractogram to quantitatively assess and score the remaining tracts (candidate tracts) according to their plausibility in conjunction with this context information. We show that our approach has the potential for greatly reducing the number of false positive tracts in fiber tractography while maintaining high sensitivities.
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