Open AccessDiffusion tensor imaging of spinal microstructure in healthy adults: improved resolution with the readout segmentation of long variable echo-trains
Author(s) -
Bu-Tian Zhang,
Meng Li,
Lili Yu,
Yimeng Dai,
Shiying Yu,
Jinlan Jiang
Publication year - 2017
Publication title -
neural regeneration research/neural regeneration research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.93
H-Index - 38
eISSN - 1876-7958
pISSN - 1673-5374
DOI - 10.4103/1673-5374.221166
Subject(s) - diffusion mri , echo (communications protocol) , variable (mathematics) , segmentation , medicine , tensor (intrinsic definition) , neuroscience , nuclear magnetic resonance , magnetic resonance imaging , artificial intelligence , computer science , physics , psychology , radiology , mathematics , geometry , mathematical analysis , computer network
Diffusion tensor imaging plays an important role in the accurate diagnosis and prognosis of spinal cord diseases. However, because of technical limitations, the imaging sequences used in this technique cannot reveal the fine structure of the spinal cord with precision. We used the readout segmentation of long variable echo-trains (RESOLVE) sequence in this cross-sectional study of 45 healthy volunteers aged 20 to 63 years. We found that the RESOLVE sequence significantly increased the resolution of the diffusion images and improved the median signal-to-noise ratio of the middle (C 4-6 ) and lower (C 7 -T 1 ) cervical segments to the level of the upper cervical segment. In addition, the values of fractional anisotropy and radial diffusivity were significantly higher in white matter than in gray matter. Our study verified that the RESOLVE sequence could improve resolution of diffusion tensor imaging in clinical applications and provide accurate baseline data for the diagnosis and treatment of cervical spinal cord diseases.