Abstract
Magnetic resonance imaging (MRI) is an established clinical technique that measures diffusion-weighted signals, applied primarily in brain studies. Diffusion tensor imaging (DTI) is a technique that uses the diffusion-weighted signals to obtain information about tissue connectivity, which recently started to become established in clinical use. The extraction of tracts (tractography) is an issue under active research. In this work we present an algorithm for recovering tracts, based on Dijkstra’s minimum-cost path. A novel cost definition algorithm is presented that allows tract reconstruction, considering the tract’s curvature, as well as its alignment with the diffusion vector field. The proposed cost function is able to adapt to linear, planar, and spherical diffusion. Thus, it can handle issues of fiber crossing, which pose considerable problems to tractography algorithms. A simple method for generating synthetic diffusion – weighted MR signals from known fibers – is also presented and utilized in this work. Results are shown for two (2D)- and three-dimensional (3D) synthetic data, as well as for a clinical MRI-DTI brain study.
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This work has been partly supported by the University of Piraeus Research Center.
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Delibasis, Κ., Aronis, C., Fanariotis, M., Maglogiannis, I. (2020). Novel Cost Function Definition for Minimum-Cost Tractography in MR Diffusion Tensor Imaging. In: Vlamos, P. (eds) GeNeDis 2018. Advances in Experimental Medicine and Biology, vol 1194. Springer, Cham. https://doi.org/10.1007/978-3-030-32622-7_12
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DOI: https://doi.org/10.1007/978-3-030-32622-7_12
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