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Application of Segmentation in Localized MR Chemical Shift Imaging and MR Spectroscopy

  • Rakesh Sharma
Part of the Advances in Computer Vision and Pattern Recognition book series (ACVPR)

Abstract

Since the discovery of NMR (Nuclear Magnetic Resonance) half a century ago, biophysical NMR approaches to brain imaging have shifted to non-invasive methods. Mainly brain segmentation, metabolic mapping and steady-state biochemical approaches are now being explored for normal and developmental neurochemistry with an exposure to common disorders of brain functions. Basically, NMR detects frequency dependent signals from individual odd numbered atomic nuclei. MRI (Magnetic Resonance Imaging) detects signals from populations of these nuclei at different locations in the tissues. Major advancements have been made in non-invasive MR imaging in two directions. Spatial information with good resolution in different tissue locations was achieved primarily by segmentation. Spatial information of metabolites and the peak sensitivity of metabolites were achieved by Chemical Shift Imaging (CSI). There exists a trade-off between these two informations. The highest quality of spatial chemical information by the MR technique is affected by the trade-off due to several physical and chemical factors. Localized MR Spectroscopy still remains a powerful tool to identify neuro-chemicals and metabolite concentrations precisely in relative or absolute terms in different locations in the brain. The latest major emphasis was concentrated on neurochemicals and their spectral peaks referenced with abundant tissue water at very short intervals. MR Spectroscopy (MRS) and MR Spectroscopic Imaging (MRSI) methods are used to define regional differences of peaks within the tissue.

Keywords

Radio Frequency Pulse Chemical Shift Image Primary Progressive Multiple Sclerosis Multiple Scle Primary Progressive Multiple Sclerosis 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag London 2002

Authors and Affiliations

  • Rakesh Sharma

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