Automated Quantitation of Proton Magnetic Resonance Spectroscopic Imaging

Sajja B. Rao, Renjie He, Meghana Mehta, Ponnada A. Narayana

Research output: Contribution to journalConference articlepeer-review


A toolbox for accurate and automatic analysis and visualization of MRSI is described. The toolbox is made up of two independent modules; automated spectroscopic image processing and visualization. The first module automatically analyzes and processes MRSI data. Spectral quantitation is realized by fitting the phased spectrum to approximated Voigt lineshape function using the Levenberg-Marquardt algorithm. The absolute metabolite concentrations are calculated relative to brain tissue water after correcting for the relaxation effects. The second module links the spectroscopic analysis data from the first module to the tissue segmentation, based on the high resolution MRI to follow metabolic changes in individual tissues such as gray matter, white matter and lesions. The visualization tools allow the display of metabolic concentrations and individual tissue volumes that contribute to a given MRSI voxel by a single mouse click. The image manager allows the display of multiple images that are linked by a common cursor for accurate identification of the spectral regions. This software tool has been tested on a large number of normal volunteers and is currently used for the analysis of multicenter proton MRSI data on multiple sclerosis patients.

Original languageEnglish (US)
Pages (from-to)513-516
Number of pages4
JournalAnnual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings
StatePublished - 2003
Externally publishedYes
EventA New Beginning for Human Health: Proceedings of the 25th Annual International Conference of the IEEE Engineering in Medicine and Biology Society - Cancun, Mexico
Duration: Sep 17 2003Sep 21 2003


  • Absolute concentrations
  • CSI
  • Cerebral metabolites
  • MRSI

ASJC Scopus subject areas

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


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