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Examining mcDESPOT

Examining mcDESPOT. Mar 12, 2013 Jason Su. MRM 2012 : Lankford and Does. On the Inherent Precision of mcDESPOT . Results. Summary. Good A well done analysis of the unconstrained situation Bad

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Examining mcDESPOT

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  1. Examining mcDESPOT Mar 12, 2013 Jason Su

  2. MRM 2012:Lankford and Does. On the Inherent Precision of mcDESPOT.

  3. Results

  4. Summary • Good • A well done analysis of the unconstrained situation • Bad • Very different constraint scenario from the one used in practice with Stochastic Region Contraction (SRC) • Some doubts about step size and forward finite difference • Take-home message • Exchange rate and MWF could not both be estimated well • Additional phase cycles may provide benefit

  5. SRC vs. Unbiased Estimator • SRC produces a biased estimate but the coefficient of variation is well under Lankford’s 10% cut-off

  6. pcMCDESPOT.c • We have access to an old version of Sean’s source code • Results produced with both the binary provided to us (though this itself is old) match those produced from this source, so it has likely the same core fitting • However, there are some bugs in the code: • DESPOT2-FM implements an incorrect signal equation, the off-resonance estimate from this is used in mcDESPOT fits • The mcDESPOT SSFP signal equation models the magnetization before RF excitation, which is not measured what is in experiment

  7. Problem: Model is Before RF Fit w/ Data Before RF Fit w/ Data After RF

  8. Problem: “Gaussian” Sampling • The code uses a Taylor approximation of the Gaussian CDF which is fairly inaccurate • In addition, discrete uniform samples are drawn from a set of 999 bins • Not well understood how the sampling affects SRC convergence but this is definitely not Gaussian

  9. Problem: Cyclic Phase • SRC needs to be properly adapted to handle cyclic parameters, i.e. off-resonance/phase

  10. Problem: Mean Normalization • Mean normalization of SSFP data is used to reduce the fitting problem, but produces a fundamental ambiguity in the phase • At cross-over points, phase0 = phase180: the most important information is the amplitude • But this is thrown away with mean normalization

  11. Mean Normalization -> Ambiguity With Mean Normalization No Mean Normalization

  12. Idea: 3 Phase Cycles • We can still do mean normalization as long as the collected data provides a unique “signature” • With 3 phase cycles, all signals will never be equal at the same time, so the combined set of data is not degenerate after normalization

  13. 3-phase mcDESPOT • Is 3 phase cycles the future? • We can use some CRLB theory to examine how it would benefit an unbiased estimator • There is a huge improvement in estimating the off-resonance • There is some but little improvement elsewhere • SNR is matched here for constant acquisition time

  14. Current & Future Work • 3pc could be critical in scenarios with high banding • Acquiring a phase90 SSFP may not be a common option on all scanners • Re-implementing mcDESPOT fitting code in Python/Cython • Fix implementation bugs • Nearly eliminate the cost in processing addition phase cycles by taking advantage of redundancies in the signal equations • A general, open source, parameter fitting framework • What is the optimal way to sample the free parameter space? • Flip angle, TR, phase cycle, etc.

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