Tensor deflection (TEND) tractography with adaptive subvoxel stepping.
OBJECTIVE
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.


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.


CONCLUSIONS
The adaptive stepping algorithm is a helpful adjunct to TEND tractography.
Chou
Ming-Chung
MC
Department of Electrical Engineering, National Taiwan University, Taipei, Taiwan, R.O.C.
Wu
Ming-Long
ML
Chen
Cheng-Yu
CY
Wang
Chao-Ying
CY
Huang
Teng-Yi
TY
Liu
Yi-Jui
YJ
Juan
Chun-Jung
CJ
Chung
Hsiao-Wen
HW
D000465:Algorithms; D001931:Brain Mapping / Q000379:methods*; D038524:Diffusion Magnetic Resonance Imaging / Q000379:methods*; D006801:Humans; D007091:Image Processing, Computer-Assisted; D009412:Nerve Fibers; D019047:Phantoms, Imaging