Version 3 (modified by Andrew, 10 years ago)

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Background

Magnetic Resonance Spectroscopic Imaging

MR Spectroscopic Imaging (MRSI) is a technique that combines the spatial mapping capability of Magnetic Resonance Imaging (MRI) with the analytical capabilities of Nuclear Magnetic Resonance (NMR) spectroscopy. When applied to living systems, MRSI enables the distributions of a number of tissue metabolites to be detected. The most widespread applications use detection of proton resonances, and obtain signals primarily from low molecular weight compounds that are present at relatively high concentrations (e.g. >1 mM).

MRSI offers considerable potential as a clinical diagnostic imaging technique since it is able to provide information on tissue metabolism, which is complimentary to the structural information available using imaging modalities such as MRI or CT imaging.

The following illustrates how images are created with MRSI [[Image (MRSIExample1.jpg)]]

The next figure shows an example of a volumetric 1H MRSI of Human Brain [[Image (MRSIExample2.jpg)]]

The MIDAS Project

Although MRSI offers considerable potential as a diagnostic imaging technique and can be implemented on a many of the available MRI scanners, its use remains limited to a few academic research centers. A more widespread adoption of MRSI has been limited by complex requirements for data processing and analysis, as well an unfamiliarity of the clinical users to MR spectroscopy.

The quality of MRSI data is limited by the sensitivity for detection of low concentration metabolites. To improve the quality of metabolite images the data processing methods can make use of known spectral and spatial information, including MRI-derived tissue segmentation, morphological analysis, metabolite NMR characteristics, and detailed knowledge of normal tissue metabolite distributions. Much of this information can be obtained by integrating the MRSI processing analysis with information from a MRI study of the same region, and the MIDAS project aims to provide an integrated set of data processing tools that satisfies these requirements. Follow this link for an example of the multiple processing steps involved. There are two primary aims:

  • To simplify implementation of MRSI for routine diagnostic imaging studies, while also increasing the quality of the information obtained by using improved data processing and analysis methods.
  • To map normal metabolite distributions in human brain. Results from metabolite imaging studies will be converted to standardized intensity units and transformed into normalized spatial coordinates, enabling the data to be pooled to form a database of MR-measured human metabolite values as a function of acquisition, spatial, and subject parameters. This information will then be used to enhance statistical analysis of individual MRSI studies.

This effort combines multiple areas of expertise in MRSI and MRI data processing, and software tools are being developed for automated MRSI processing, tissue segmentation, brain region mapping, statistical analysis, and clinical presentation. Once developed, these tools will be evaluated for diagnostic neuroimaging applications such as cancer, epilepsy, and neurodegenerative disease.

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