Tractography is a powerful tool to study brain connectivity but it suffers from an intrinsic trade-off between sensitivity and specificity. The former can be increased by constructing more streamlines, while filtering techniques can improve the latter. However, creating many streamlines may introduce redundancy in the tractograms and negatively affect the performances of filtering methods, especially those based on linear optimization. Here, we present the “blurred streamlines”, a novel concept based on a combination of streamline clustering and spatial blurring of their signal contributions. Preliminary results show the potential of this formulation and open new perspectives for improving tractography accuracy.
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