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
– 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
SRC vs. Unbiased Estimator
• SRC produces a biased estimate but the coefficient of
variation is well under Lankford’s 10% cut-off
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
Problem: Model is Before RF
Fit w/ Data Before RF
Fit w/ Data After RF
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
Problem: Cyclic Phase
• SRC needs to be properly adapted to handle
cyclic parameters, i.e. off-resonance/phase
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
Mean Normalization -> Ambiguity
With Mean Normalization
No Mean Normalization
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
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
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.