Application of independent component analysis to H-1 MR spectroscopic imaging exams of brain tumours

Abstract

The low spatial resolution of clinical H-1 MRSI leads to partial volume effects. To overcome this problem, we applied independent component analysis (ICA) on a set of H-1 MRSI exams of brain turnours. With this method, tissue types that yield statistically independent spectra can be separated. Up to three components, corresponding to necrosis, tumoral tissue and healthy tissue have been detected inside turnours. In nonagressive turnours, the "necrotic" component was absent, confirming that only agressive turnours exhibit high levels of lipids. In conclusion, the ICA algorithm allows to find useful hidden components in turnours. The reliability and robustness of the results have also been investigated by means of bootstrapping combined with unsupervised clustering. A comparison of ICA with a method of curve resolution, MCR-ALS, has also been performed. (c) 2005 Elsevier B.V. All rights reserved.