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Abstract #5478

Quality Control of MRSI data using efficient data labelling

Nuno Pedrosa de Barros1, Richard Iain McKinley1, Roland Wiest1, and Johannes Slotboom1

1SCAN / Neuroradiology, University Hospital Bern (Inselspital), Bern, Switzerland

MRSI-data frequently contains bad-quality spectra, what can prevent proper quantification and consequently lead to data misinterpretation. Machine-learning based methods have been proposed for automatic quality control of MRSI-data with performance levels identical to expert’s-manual-checking and that can classify thousands of spectra in a matter of a few seconds. Besides this, a considerable amount of time needs to be spent labelling data required to train these algorithms. Here we present a method that allows to actively select those spectra that carry the most information for the classification, allowing to reduce drastically the amount of time needed for labelling.

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