High resolution reduced-FOV diffusion tensor imaging of the human pons with multi-shot variable density spiral at 3T

Dimitrios C. Karampinos, Anh T. Van, William C. Olivero, John G. Georgiadis, Bradley P. Sutton

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Diffusion Tensor Imaging (DTI) of localized anatomical regions (i.e brainstem, cervical spinal cord, and optic nerve) is challenging because of the existence of significant susceptibility differences in the surrounding tissues, their high motion sensitivity and the need for high spatial resolution to resolve the underlying complex histoarchitecture. The aim of the present methodology is to achieve high resolution DTI with motion compensating capability in localized regions of the central nervous system. We accomplish this by implementing self-navigated multi-shot variable density spiral encoding with outer volume suppression. In vivo application of the technique on the human brainstem demonstrates a clear delineation of the multiple local neural tracts. We also investigate the partial volume effect on the extracted diffusion anisotropy metrics by varying the in-plane resolution while maintaining a constant signal-to-noise ratio.

Original languageEnglish (US)
Title of host publicationProceedings of the 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS'08
Pages5761-5764
Number of pages4
StatePublished - Dec 1 2008
Event30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS'08 - Vancouver, BC, Canada
Duration: Aug 20 2008Aug 25 2008

Publication series

NameProceedings of the 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS'08 - "Personalized Healthcare through Technology"

Other

Other30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS'08
CountryCanada
CityVancouver, BC
Period8/20/088/25/08

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition
  • Signal Processing
  • Biomedical Engineering
  • Health Informatics

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