Abstract #2763

Improved muscle T2 estimation by maximum-likelihood parameter estimation using an extended-phase-graph signal model with locally estimated Rician noise levels

Nick Zafeiropoulos¹, Stephen Wastling¹, Christopher Sinclair¹, Tarek Yousry¹, Enrico De Vita¹, Robert Janiczek², and John Thornton¹

¹Institute of Neurology, London, United Kingdom, ²Glaxo Smith Kline, London, United Kingdom

Maximum likelihood model parameter estimation accounting for the Rician noise distribution in MRI acquisitions, combined with the extended graph formalism and incorporating slice profile considerations, offers higher precision and less bias with regards to the predicted parameters in T2 relaxometry. In this work this was tested by simulations and validated in phantom and in vivo data from healthy volunteers.

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