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Tensor deflection (TEND) tractography with adaptive subvoxel stepping
Author(s) -
Chou MingChung,
Wu MingLong,
Chen ChengYu,
Wang ChaoYing,
Huang TengYi,
Liu YiJui,
Juan ChunJung,
Chung HsiaoWen
Publication year - 2006
Publication title -
journal of magnetic resonance imaging
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.563
H-Index - 160
eISSN - 1522-2586
pISSN - 1053-1807
DOI - 10.1002/jmri.20652
Subject(s) - voxel , diffusion mri , computer science , stepping stone , tractography , algorithm , mathematics , computer vision , magnetic resonance imaging , medicine , radiology , economics , unemployment , economic growth
Purpose To develop an adaptive subvoxel stepping scheme, as an adjunct to tensor deflection (TEND) tractography, that automatically adjusts the stepping size by considering the tensor linearity to properly trace fiber bundles in regions with different degrees of tensor anisotropy. Materials and Methods A theoretical investigation of the TEND algorithm was performed to assess the degree of deflection of the propagation vector toward the major eigenvector. Mathematically generated phantoms (one with curved fibers and the other with crossing fibers) at wide ranges of signal‐to‐noise ratio (SNR), and human brain images obtained in vivo were used to test the performance of the adaptive stepping algorithm. Results The degree of deflection was found to be inversely related to the stepping size. A small stepping size was advantageous for tracing single curved fiber bundles, whereas a large stepping size was beneficial for passing through fiber crossing regions. The performance of the adaptive stepping algorithm was superior to fixed stepping in both situations, leading to an approximately 0.17 voxel of deviation in curved fibers and a nearly 100% successful tracking rate in crossing fibers at typical SNR. Human brain images demonstrated similar results. Conclusion The adaptive stepping algorithm is a helpful adjunct to TEND tractography. J. Magn. Reson. Imaging 2006. © 2006 Wiley‐Liss, Inc.