[Neuroimaging] [Dipy] MAPMRI between subject comparisons
fick.rutger at gmail.com
Fri Jan 5 05:53:41 EST 2018
It is hard to debug what you're doing without any other information about
your model settings or what data you're fitting.
I will just describe some possible issues that you could be running into:
- MAP-MRI metrics will not be reproducible across subjects if the
gradient tables of these subjects are different. Reason is that MAP-MRI is
basically a non-parametric inter/extra-polator and will just smoothly
attenuate to zero after the last b-value point in acquisition scheme. If
this point changes between schemes, then the extrapolation begins at
different points, which results in different q-space index values (because
they are based on the extrapolation to infinity). Relevant references would
be [1, 2], where is shown that MAPMRI qspace indices *are *reproducible
between subjects (with the same scheme).
- The scaling factor is calculated internally using DTI, so that should
not be a user issue. It is worth looking at the fitted_mapmri_model.mu
property, which contain the estimated scaling factors (ux, uy, uz). If all
these factors are the same (or have been truncated to their minimum allowed
value), this means there is a problem with the data itself (DTI failed to
- With respect to artifacts, I'm not sure what kind of artifacts you're
seeing, but depending on the problem several things could be happening.
- If you're using laplacian regularization and the data is very
anisotropic, then the automatic regularization estimation using GCV could
be giving a too low value. Try setting it to a fixed value (0.2 for
example), or if it was already set to a fixed value, try increasing it to
see what happens (but not too much because you'll just be making
- If you're not using positivity constraint as well -> use it as
well. The best results are typically found when both GCV and positivity
constraint are used (but it also takes the longest to fit).
- Of course,if the data itself is bad (very noisy or some crazy
distortion) then MAPMRI cannot do much about it. As I said above, it will
just smoothly fit the data it is given. It is always important to look at
the data itself you are fitting, and if you see a very badly
then remove it from from the data set.
Let us know what kind of acquisition schemes you're using, and explain what
kind of artifacts you're seeing. Hard to make a concrete judgement
 Avram, Alexandru V., et al. "Clinical feasibility of using mean
apparent propagator (MAP) MRI to characterize brain tissue microstructure."
*NeuroImage* 127 (2016): 422-434.
 Fick, Rutger HJ, et al. "MAPL: Tissue microstructure estimation using
Laplacian-regularized MAP-MRI and its application to HCP data." *NeuroImage*
134 (2016): 365-385.
On 5 January 2018 at 11:46, Mauro Zucchelli <mauro.zucchelli88 at gmail.com>
> Hi! Low SNR in multi-shell data with high b-values are a problem for all
> the higher order models, including MAP-MRI and many compartmental models.
> Moreover, MAPMRI presents numerous parameters tha you can adjust in order
> to maximize its performances. Can you give us more information on your
> dataset? E.g. SNR, number of samples, number of b-values, etc.
> Kind regards,
> On Wed, Jan 3, 2018 at 8:43 PM, Ping-Hong Yeh <pinghongyeh at gmail.com>
>> Hi Dipy users,
>> I am wondering if the MAP-MRI measures such as RTOP, RTAP, RTPP, NG etc
>> are ready to use for between-subject comparisons. Are there any scaling
>> factor that needs to be applied beforehand.
>> I've noticed that MAP-MRI measures are very susceptible to artifacts.
>> Thank you.
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>> Neuroimaging at python.org
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