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Abstract #0162

Metabolite Quantification of 1H-MRSI spectra in Multiple Sclerosis: A Machine Learning Approach

Dhritiman Das1,2,3, Mike E Davies2, Jeremy Chataway4, Siddharthan Chandran3, Bjoern H Menze1, and Ian Marshall3

1Department of Computer Science, Technical University of Munich, Munich, Germany, 2Institute for Digital Communications, University of Edinburgh, Edinburgh, Scotland, 3Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, Scotland, 4Queen Square Multiple Sclerosis Centre, Department of Neuroinflammation, University College London, London, United Kingdom

As an alternative to model-based spectral fitting tools, we introduce a machine-learning framework for estimating metabolite concentrations in MR spectra acquired from a homogeneous cohort of 42 patients with Secondary Progressive Multiple Sclerosis. Our framework based on random-forest regression performs a 42-fold cross validation on this dataset which involves (1) learning the spectral features from this cohort; (2) estimating concentrations and calculating relative error over the LCModel estimates. Compared to the LCModel, our method, after training, gives a low estimation error and a 60-fold improvement in estimation speed per patient.

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