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- W4223467609 abstract "Abstract Non-rigid whole-brain fiber streamline matching is a highly challenging technical problem in brain white matter analysis, largely due to the lack of reasonable vector-space representation and efficacious streamline distance measurement in the original curve space is computationally intractable. To address the issue of fast and accurate streamline distance measurement, we propose a fast fiber k-NN algorithm based on a strong relationship between point-wise K-NN and fiber-wise k-NN. Additionally , we propose a distance-preserving vector-space representation of the fiber streamlines based on a novel group-wise multi-dimensional scaling (MDS) technique. Based on the fast fiber k-NN and group-wise MDS, we propose a novel computationally tractable framework for non-rigid whole-brain white matter fiber matching. In this framework , our fast fiber k-NN algorithm is used to initialize the point-cloud matching in the vector space of the streamlines computed by the group-wise MDS. In our experiments, we show that our fast fiber k-NN algorithm reasonably approximates the exhaustive fiber k-NN search at a significantly reduced computational cost, the group-wise MDS effectively computes the vector representation in the aligned vector space, and our fast fiber matching method achieves higher accuracy for large displacement non-rigid whole-brain streamline matching compared to conventional image registration based streamline matching." @default.
- W4223467609 created "2022-04-15" @default.
- W4223467609 creator A5083147079 @default.
- W4223467609 date "2022-04-11" @default.
- W4223467609 modified "2023-09-29" @default.
- W4223467609 title "Fast Non-rigid Whole Brain Streamline Matching based on Fast Fiber k-NN and Group-wise MDS" @default.
- W4223467609 doi "https://doi.org/10.21203/rs.3.rs-1505524/v1" @default.
- W4223467609 hasPublicationYear "2022" @default.
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