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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