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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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