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   -> Volume 5, Issue 8

Thesis: Pattern Recognition of in vivo MR spectra
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Author Message (Anne Rosemary Tate)

PostPosted: Thu Sep 19, 1996 9:17 am    
Subject: Thesis: Pattern Recognition of in vivo MR spectra
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#7 Thesis: Pattern Recognition of in vivo MR spectra

My DPhil thesis, entitled:

"Pattern Recognition Analysis of in vivo Magnetic Resonance Spectra"

which describes an application of the use of wavelets for feature
extraction, is now available in postscript format as a technical
report (csrp432) from the School of Cognitive and Computing Sciences,
University of Sussex.

It can be obtained by anonymous ftp to

or alternatively from


Magnetic resonance spectroscopy (MRS) provides a unique non-invasive
method for obtaining information on the biochemistry of living tissue
in situ, and therefore has great potential as a clinical
tool. However, presently in vivo MRS is used mainly for research,
rather than for clinical applications.

There are a number of reasons for this. The information may be
difficult to extract from the spectrum due to low signal-to-noise
ratio and other problems associated with obtaining a signal from
living tissue. Interpretation may be difficult due to the large number
of metabolites represented by the spectra. Another problem is that
most current methods for analysing MRS data are targeted at providing
information on specific metabolites, rather than the more general
information appropriate for clinical applications, such as the disease
stage or state of the tissue being examined.

This thesis shows how pattern recognition techniques may be used to
help overcome these problems and to provide methods for classifying in
vivo spectra according to their tissue type. A prototype system for
classifying spectra is developed using features that are extracted
automatically, using the whole spectrum, rather than selected
peaks. These features were selected purely on the basis of their power
to discriminate between different types of spectra, using no prior
knowledge of biochemistry. Among the techniques used were wavelets,
principal component analysis and linear discriminant function
analysips. These techniques were tested on two sets of in vivo data:
75 carbon spectra obtained from healthy human volunteers from three
different dietary groups of adipose tissue in the leg and 55 phos
spectra obtained from tumorous and normal tissue in rats. For both
datasets most of the spectra were assigned to their correct groups
(94% of the carbon and 86 - 100% of the phos spectra) without the need
for explicit identification or measurement of peaks.

Rosemary Tate.
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