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Abstract #1967

Nonlinear Laplacian Eigenmaps Dimension Reduction of in-vivo Magnetic Resonance Spectroscopic Imaging Analysis

Guang Yang1, Felix Raschke2, Thomas Richard Barrick2, Franklyn A. Howe2

1Division of Clinical Sciences, , St. Georges University of London, London, United Kingdom; 2Division of Clinical Sciences,, St. Georges University of London, London, United Kingdom

MRSI has demonstrated great clinical potential as a supplement to standard imaging for non-invasive diagnosis of brain tumours. Pattern recognition(PR) techniques are used to assist MRSI tumour identification and characterisation, and they can be applied to MRSI data with suspected gliomas with an aim to segment regions relating to tumour core, tumour infiltration and normal brain. Dimensionality reduction(DR) is an important prerequisite in any real case of PR. In this work, we advocate the spectral manifold learning method of Laplacian eigenmaps as a DR technique suitable for MRSI datasets, with correlation to standard MRI to aid confirmation of our results.