Identifying rodent olfactory bulb structures with micro-DTI

X. G. Zhao, E. S. Hui, K. C. Chan, K. X. Cai, H. Guo, P. T. Lai, E. X. Wu

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

5 Citations (Scopus)

Abstract

Olfactory bulb (OB) is one of the most developed systems in rodent models with complex neuronal organization and anatomical structures. MR diffusion tensor imaging (DTI) is a non-invasive technique to probe tissue microstructures by examining the diffusion characteristics of water molecules. This paper presents how different OB layers can be identified and quantitatively characterized by micro-DTI using a specially constructed micro-imaging radio frequency (RF) coil. High spatial resolution and high signal to noise ratio (SNR) DTI images of ex vivo rat OBs were obtained. Distinct contrasts were observed between various olfactory bulb layers in trace map, fractional anisotropy (FA) map and FA color map, all in consistence with the known OB neuroanatomy. These experimental results demonstrate the utility of micro-DTI in investigation of complex OB organization.

Original languageEnglish
Title of host publicationProceedings of the 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS'08
PublisherIEEE Computer Society
Pages2028-2031
Number of pages4
ISBN (Print)9781424418152
DOIs
Publication statusPublished - Oct 2008
Externally publishedYes
Event30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS'08 - Vancouver, BC, Canada
Duration: 20 Aug 200825 Aug 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"

Conference

Conference30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS'08
CountryCanada
CityVancouver, BC
Period20/08/0825/08/08

ASJC Scopus subject areas

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

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