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Abstract #2885

Linewidth constraints in Matlab AMARES using per-metabolite T 2 and per-voxel B 0

Lucian A. B. Purvis 1 , William T. Clarke 2 , Luca Biasiolli 2 , Matthew D. Robson 2 , and Christopher T. Rodgers 2

1 Department of Chemistry, University of Oxford, Oxford, United Kingdom, 2 Department of Cardiovascular Medicine, University of Oxford, United Kingdom

The AMARES spectroscopic fitting algorithm was re-implemented in Matlab to facilitate the use of new types of prior knowledge. We demonstrate the new fitting code by implementing linewidths constraints. First, the relative linewidths were calculated for a batch of cardiac data. There were used as prior knowledge in constrained AMARES fitting, which was compared against unconstrained AMARES using Monte Carlo simulations and in the leg in vivo. We show that the linewidth constrained fitting is more accurate and more consistent in data with an SNR<30. This linewidth-constrained AMARES approach will be useful for exercise protocols and for saturation- and inversion-recovery.

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