Abstract #4272

A Novel Hybrid Method for Applying Independent Component Analysis to in Vivo Paediatric Brain Tumour 1H Magnetic Resonance Spectra

Jie Hao¹, M P. Wilson², N P. Davies^2,3, Y Sun^2,4, K Natarajan^3,4, L MacPherson⁴, A C. Peet^2,4, T N. Arvanitis^1,4

¹School of Electronic, Electrical & Computer Engineering, University of Birmingham, Birmingham, UK; ²School of Cancer Sciences, University of Birmingham, Birmingham, UK; ³Department of Imaging and Medical Physics, University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK; ⁴Birmingham Childrens Hospital NHS Foundation Trust, Birmingham, UK

Independent Component Analysis (ICA) has shown the possibility to identify the individual components, and reveal hidden biochemical information about the tissues in MRS. A hybrid ICA approach incorporating the Blind Source Separation (BSS) and Feature Extraction (FE) techniques for automated decomposition of MR spectra is developed and applied to an in vivo paediatric brain tumour MRS dataset. The hybrid method of ICA has the advantages of both BSS and FE, and provides more realistic individual metabolite and MMLip components. It is superior to the well established IC techniques in determining individual metabolite components from brain tumour MRS.