<div dir="ltr"><div><div>Hi Ping,<br><br></div><div>All I can say is that dipy's RTOP is in 1/mm^3, I don't know about TORTOISE.<br><br></div>Looking at the images it looks like TORTOISE's RTOP estimation has some areas that produced negative values (black patches in the middle of the bright white areas in the middle of the left image). This indicates their regularization is failing.<br></div>On the other hand, I think maybe you can tweak dipy's mapmri settings to make the RTOP estimation look even better. Try the following setting:<br><br>mapmod = mapmri.MapmriModel(gtab,<br> laplacian_regularization=True,<wbr> # this regularization enhances reproducibility of estimated q-space indices by imposing smoothness<br>
laplacian_weighting="GCV", # this makes it use generalized
cross-validation to find the best regularization weight<br> positivity_constraint=True) # this ensures the estimated PDF is positive<br><br><div>MAPL is just a name for imposing signal smoothness during MAP-MRI's signal fitting, which is in fact the default setting when you fit dipy's mapmri ( so you've already been using it).<br></div><div>The general idea is that imposing a bit of smoothness in MAP-MRI's signal reconstruction will make estimation of q-space indices (RTOP RTAP NG etc.) more robust. The "GCV" option makes the weight selection of the laplacian-regularization data dependent using generalized cross-validation.<br></div><div>Setting positivity_constraint=True then also forces the solution to have a positive PDF, which was the original approach by Ozarslan et al (2013). <br></div><div>Using both MAPL and positivity should then give you the best possible reconstruction of RTOP and others.<br><br></div><div>The relevant citation for the positivity constraint is [1], while for MAPL is [2]. Just cite both those papers when you use dipy's implementation :-)<br><br>Best,<br>Rutger<br><br>[1] Özarslan, Evren, et al. "Mean apparent propagator (MAP) MRI: a novel
diffusion imaging method for mapping tissue microstructure." <i>NeuroImage</i> 78 (2013): 16-32.<br>[2] Fick, Rutger HJ, et al. "MAPL: Tissue microstructure estimation using
Laplacian-regularized MAP-MRI and its application to HCP data." <i>NeuroImage</i> 134 (2016): 365-385.</div></div><div class="gmail_extra"><br><div class="gmail_quote">On 8 January 2018 at 17:15, Ping-Hong Yeh <span dir="ltr"><<a href="mailto:pinghongyeh@gmail.com" target="_blank">pinghongyeh@gmail.com</a>></span> wrote:<br><blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex"><div dir="ltr">Hi <span style="font-size:12.8px">Mauro, Rutger, </span><div><span style="font-size:12.8px"><br></span></div><div><span style="font-size:12.8px"> Thank you both for the instructive reply.</span></div><div><span style="font-size:12.8px"><br></span></div><div><span style="font-size:12.8px">We used SMS sequence (GE 3T) to acquire multi-shell DWI (three shells b= 1000, 2000, and 3000 with 90 directions for each shell and an additional 18 volumes of b0) with an inter-slice acceleration factor of 3 and an in-plain acceleration of 2 (ASSET). The original data has N=130* 130 with an in-plain resolution of 1.7 *1.7 mm, and DWI data was registered to T2W with a final resolution of 1*1*1 mm using ANTS nonlinear warping </span><span style="font-size:12.8px">implemented in TORTOISE </span>toolkit for<span style="font-size:12.8px"> <wbr>distortion correction, after running some preprocessing steps such as noise reduction, bias correction. </span></div><div><span style="font-size:12.8px"><br></span></div><div><span style="font-size:12.8px">I have not calculated the mean SNR for each shell, i think there is below 5 in b=3000. </span></div><div><span style="font-size:12.8px"><br></span></div><div><span style="font-size:12.8px">I have attached two examples of RTOP snapshots, one was created by TORTOISE, and one by DIPY using "</span><span style="font-size:12.8px">map_model.fit" of the same data. There is a difference of a difference of scaling around 10^6 between the two, I think this is due to the difference of unit used for the DTI fitting between two tools? </span></div><div><span style="font-size:12.8px"><br></span></div><div><span style="font-size:12.8px">I am concerned about high-intensity values over the subcortical regions, such as brainstem, and inhomogeneity over the main white matter tracts such as corpus callosum. Are those seemed normal looking to you? If not, what can cause those artifacts? Are suggestions in correcting those? </span></div><div><span style="font-size:12.8px"><br></span></div><div><span style="font-size:12.8px">What even puzzles me is that i found there is a significant reduction of Principal Anisotropy in the disease group than the control group (w</span><span style="font-size:12.8px">hich is not what </span>i<span style="font-size:12.8px"> expected) </span><span style="font-size:12.8px">over the frontal white matter and gray matter of insular region using voxel-wise analysis after correcting multiple comparisons. This is done by using the outputs from the TORTOISE toolkit. I am still working on the DIPY ones. </span></div><div><span style="font-size:12.8px"><br></span></div><div><span style="font-size:12.8px"><br></span></div><div><span style="font-size:12.8px">Rutger, </span></div><div><span style="font-size:12.8px"><br></span></div><div><span style="font-size:12.8px">I have not tried the MAPL method yet, but i will definitely give it a shot later. What additional information or aspects does MAPL provide us, comparing to the conventional MAP-MRI method? BTW, can DIPY output </span><span style="font-size:12.8px"> </span><span style="font-size:12.8px">Principal Anisotropy</span><span style="font-size:12.8px"> map and Non-Gaussianity map as well? If so, what is the syntax for making those? </span></div><div><span style="font-size:12.8px"><br></span></div><div><span style="font-size:12.8px">Thank you, </span></div><span class="HOEnZb"><font color="#888888"><div><span style="font-size:12.8px"><br></span></div><div><span style="font-size:12.8px">Ping</span></div><div><span style="font-size:12.8px"><br></span></div></font></span></div><div class="HOEnZb"><div class="h5"><div class="gmail_extra"><br><div class="gmail_quote">On Fri, Jan 5, 2018 at 5:53 AM, Rutger Fick <span dir="ltr"><<a href="mailto:fick.rutger@gmail.com" target="_blank">fick.rutger@gmail.com</a>></span> wrote:<br><blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex"><div dir="ltr">Hello Ping,<br><div><br>It is hard to debug what you're doing
without any other information about your model settings or what
data you're fitting.<br>I will just describe some possible issues that you could be running into:<br><ul><li>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 <i>are </i>reproducible between subjects (with the same scheme).</li><li>The scaling factor is calculated internally using DTI, so that should not be a user issue. It is worth looking at the <a href="http://fitted_mapmri_model.mu" target="_blank">fitted_mapmri_model.mu</a>
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 fit properly).</li><li>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.</li><ul><li>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 everything flat).</li><li>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).<br></li><li>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 distorted DWI, then remove it
from from the data set.<br></li></ul></ul>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
otherwise.</div><div><br></div><div>Best,<br></div><div>Rutger</div><div><br></div><div>[1] Avram, Alexandru V., et al. "Clinical feasibility of using mean apparent
propagator (MAP) MRI to characterize brain tissue microstructure." <i>NeuroImage</i> 127 (2016): 422-434.<br>[2] Fick, Rutger HJ, et al. "MAPL: Tissue microstructure estimation using
Laplacian-regularized MAP-MRI and its application to HCP data." <i>NeuroImage</i> 134 (2016): 365-385.</div><div><br></div><div><br></div></div><div class="m_-8688643574756624689HOEnZb"><div class="m_-8688643574756624689h5"><div class="gmail_extra"><br><div class="gmail_quote">On 5 January 2018 at 11:46, Mauro Zucchelli <span dir="ltr"><<a href="mailto:mauro.zucchelli88@gmail.com" target="_blank">mauro.zucchelli88@gmail.com</a>></span> wrote:<br><blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex"><div dir="ltr"><div><div> 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. <br></div><div>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.<br><br></div>Kind regards,<br><br></div>Mauro <br></div><div class="gmail_extra"><br><div class="gmail_quote">On Wed, Jan 3, 2018 at 8:43 PM, Ping-Hong Yeh <span dir="ltr"><<a href="mailto:pinghongyeh@gmail.com" target="_blank">pinghongyeh@gmail.com</a>></span> wrote:<br><blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex"><div dir="ltr">Hi Dipy users, <div><br></div><div> I am wondering if the <span style="color:rgb(0,0,0);font-family:"Lucida Grande","Lucida Sans Unicode",Geneva,Verdana,sans-serif;font-size:14px;letter-spacing:-0.14px">MAP-MRI</span><span style="color:rgb(0,0,0);font-family:"Lucida Grande","Lucida Sans Unicode",Geneva,Verdana,sans-serif;font-size:14px;letter-spacing:-0.14px"> </span>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. </div><div><br></div><div>I've noticed that MAP-MRI measures are very susceptible to artifacts. </div><div><br></div><div><br></div><div>Thank you. </div><span class="m_-8688643574756624689m_8046919105294349040m_6613390763879646256HOEnZb"><font color="#888888"><div><br></div><div>Ping</div><div><br></div><div><br></div><div><br></div></font></span></div>
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