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

The low spatial resolution of clinical 1H MRSI leads to partial volume effects. To overcome this problem, we applied independent component analysis (ICA) on a set of 1H MRSI exams of brain tumours. 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 tumours. In non-agressive tumours, the “necrotic” component was absent, confirming that only agressive tumours exhibit high levels of lipids. In conclusion, the ICA algorithm allows to find useful hidden components in tumours. 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.