<div dir="ltr">Hi Rutger, <div><br></div><div>We have some bad PA maps created using default settings, and I would like to hear your opinions on improving the fitting. </div><div><br></div><div>Attached are the screenshots of PA_GCV, norm_laplacian, L_opt and PA_laplacian_weighted0.2 maps. </div><div>I am currently running the fitting using 0.05 for the<span style="color:rgb(34,34,34);font-family:arial,sans-serif;font-size:12.8px;font-style:normal;font-variant-ligatures:normal;font-variant-caps:normal;font-weight:400;letter-spacing:normal;text-align:start;text-indent:0px;text-transform:none;white-space:normal;word-spacing:0px;background-color:rgb(255,255,255);text-decoration-style:initial;text-decoration-color:initial;float:none;display:inline"> minimum bound of the GCV, but I am not sure if that would help. </span></div><div><br></div><div><span style="color:rgb(34,34,34);font-family:arial,sans-serif;font-size:small;font-style:normal;font-variant-ligatures:normal;font-variant-caps:normal;font-weight:400;letter-spacing:normal;text-align:start;text-indent:0px;text-transform:none;white-space:normal;word-spacing:0px;text-decoration-style:initial;text-decoration-color:initial;background-color:rgb(255,255,255);float:none;display:inline">In order to do comparisons between controls and disease population, </span>we need to make sure that the same fitting parameters are applied for the MAPMRI fitting for avoiding any biases. Do you have suggestions regarding this matter? </div><div><br></div><div>Thank you. </div><div><br></div><div>Ping</div></div><div class="gmail_extra"><br><div class="gmail_quote">On Tue, Jan 23, 2018 at 7:42 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="auto">Hi Ping,<div dir="auto"><br></div><div dir="auto">Salt and pepper noise is not a good sign (I just didn't see it so much on the second set of slices you sent). To spot badly estimated voxels is typically pretty easy - RTOP and many others can have negative or huge values, which typically come from oscillations in the signs extrapolation. You can often see these as bright spots in the laplacian norm.</div><div dir="auto"><br></div><div dir="auto">If you go through the data and see that salt and pepper noise corresponds to unusually high norms, Increasing the laplacian minimum weight in the code as i told you wil usually resolve it (or fixing it to a value like 0.05 or 0.1 or something, see what works without overdoing it).<br></div><div dir="auto"><br></div><div dir="auto">Best,</div><div dir="auto">Rutger</div><div><div class="h5"><div dir="auto"><br></div><div dir="auto"><br></div><br><div class="gmail_extra" dir="auto"><br><div class="gmail_quote">On 23 Jan 2018 03:06, "Ping-Hong Yeh" <<a href="mailto:pinghongyeh@gmail.com" target="_blank">pinghongyeh@gmail.com</a>> wrote:<br type="attribution"><blockquote class="m_1630399494073191729m_918830375935540537quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex"><div dir="ltr"><div dir="auto">Hi <span style="font-size:12.8px">Rutger, </span></div><div dir="auto"><span style="font-size:12.8px"><br></span></div><div><span style="font-size:12.8px">Thank you very much for the detailed reply. </span></div><div><br></div><div>I guess i do not need to worry about those salt-pepper dots? </div><div><br></div><div>Would you recommend output l<span style="font-size:12.8px">aplacian norm</span><span style="font-size:12.8px"> and laplacian_weighted maps and go through the images for each data set? Any tips for realizing </span><span style="font-size:12.8px">something really goes wrong when </span><span style="font-size:12.8px">looking at the propagator anisotropy map?</span></div><div><span style="font-size:12.8px"><br></span></div><div><span style="font-size:12.8px">Best, </span></div><font color="#888888"><div><span style="font-size:12.8px"><br></span></div><div><span style="font-size:12.8px">Ping</span></div></font><div class="m_1630399494073191729m_918830375935540537elided-text"><div><br></div><div><br></div><div>On Jan 22, 2018 6:55 PM, "Rutger Fick" <<a href="mailto:fick.rutger@gmail.com" target="_blank">fick.rutger@gmail.com</a>> wrote:</div><div class="gmail_extra"><div class="gmail_quote"><blockquote class="gmail_quote" style="margin:0px 0px 0px 0.8ex;border-left:1px solid rgb(204,204,204);padding-left:1ex"><div dir="ltr"><div><div><div><div><div>Hi Ping,<br><br></div>In my experience, badly estimated voxels typically have super high laplacian norm and very low estimated laplacian weight (lopt).<br></div>Looking at these results I would say things actually look pretty good!<br><br></div><div>Getting the best results is always tricky finding a balance of optimally regularizing: not fitting the noise but also not over-regularizing, which is why the GCV option is nice.<br></div><div>But, in rare cases it does mess up. So, if you want to give the GCV a bit less freedom to go low (to be on the safe side) you can increase the minimum bound of the GCV optimization in line 2272 of the code.<br></div><br></div>There's many ways to speed up the code I gave you if you want to put in the effort ;-) Using parallel processing is not standardly implemented in dipy, but maybe you can hack it somehow.<br></div><div>You can also set the laplacian_weight = 0.1 or something to avoid GCV, but it won't make a huge difference. I only ever used this code to do research - so speed was not really a concern.<br><br></div><div>Anyway, hope this all helped! Let me know if everything works out,<br><br></div><div>Best,<br></div><div>Rutger<br></div></div><div class="gmail_extra"><br><div class="gmail_quote">On 19 January 2018 at 22:03, 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:0px 0px 0px 0.8ex;border-left:1px solid rgb(204,204,204);padding-left:1ex"><div dir="ltr">Hi Rutger, <div><br></div><div> Attached please find the snapshot of <span style="font-size:12.8px">norm_of_laplacian_</span><span style="font-size:12.8px">signal, lopt, and pa maps of the same data set i used earlier.</span></div><div><span style="font-size:12.8px"><br></span></div><div><span style="font-size:12.8px">BTW, is there a way to speed up the mapmri_pa processing? Will the OpenMP help? </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="m_1630399494073191729m_918830375935540537m_4894884932956072082gmail-m_-6564103145536102577m_-3752295078917012961HOEnZb"><font color="#888888"><div><span style="font-size:12.8px"><br></span></div><div><span style="font-size:12.8px">ping</span></div></font></span></div><div class="m_1630399494073191729m_918830375935540537m_4894884932956072082gmail-m_-6564103145536102577m_-3752295078917012961HOEnZb"><div class="m_1630399494073191729m_918830375935540537m_4894884932956072082gmail-m_-6564103145536102577m_-3752295078917012961h5"><div class="gmail_extra"><br><div class="gmail_quote">On Fri, Jan 19, 2018 at 1:25 PM, 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:0px 0px 0px 0.8ex;border-left:1px solid rgb(204,204,204);padding-left:1ex"><div dir="ltr"><div><div><div><div><div>Hi Ping,<br><br></div>So far, so good.<br></div>In my opinion the TORTOISE PA reconstruction looks a bit flat/overregularized - but then again I don't know what kind of regularization they implemented for themselves.<br></div>The PA of the implementation I gave you seems to give more consistent contrast for different tissue configurations - which is a good - but looks like it under-regularizes in some individual voxels (the salt-pepper noise in the PA/RTOP).<br><br></div>To check if this is the case, can you show me the mapfit_L.norm_of_laplacian_sig<wbr>nal() and mapfit_L.lopt maps?<span class="m_1630399494073191729m_918830375935540537m_4894884932956072082gmail-m_-6564103145536102577m_-3752295078917012961m_-4948517209037813307HOEnZb"><font color="#888888"><br><br></font></span></div><span class="m_1630399494073191729m_918830375935540537m_4894884932956072082gmail-m_-6564103145536102577m_-3752295078917012961m_-4948517209037813307HOEnZb"><font color="#888888">Rutger<br><div><div><br><br> <br></div></div></font></span></div><div class="m_1630399494073191729m_918830375935540537m_4894884932956072082gmail-m_-6564103145536102577m_-3752295078917012961m_-4948517209037813307HOEnZb"><div class="m_1630399494073191729m_918830375935540537m_4894884932956072082gmail-m_-6564103145536102577m_-3752295078917012961m_-4948517209037813307h5"><div class="gmail_extra"><br><div class="gmail_quote">On 19 January 2018 at 17:43, 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:0px 0px 0px 0.8ex;border-left:1px solid rgb(204,204,204);padding-left:1ex"><div dir="ltr">Hi Rutger, <div><br></div><div> Just give you an update of the results (see the attached snapshots) using GCV weighted and L<span style="font-size:12.8px">aplacian regularization for MAPMRI estimation. </span></div><div><span style="font-size:12.8px"><br></span></div><div><span style="font-size:12.8px">The other PA mapping was calculated using TORTOISE. I have also attached RTOP mapping calculated using DIPY with and without </span>GCV weighted and L<span style="font-size:12.8px">aplacian regularization. </span></div><div><span style="font-size:12.8px"><br></span></div><div><span style="font-size:12.8px">Comparing to the TORTOISE, PA values in the one using </span>GCV weighted and L<span style="font-size:12.8px">aplacian regularization method are relatively smaller, particularly over the regions with the less dense white matter. </span></div><div><br></div><div>For RTOP images, I am not sure whether GCV weighted and L<span style="font-size:12.8px">aplacian regularization method outperforms the one without using </span>GCV weighted and L<span style="font-size:12.8px">aplacian regularization. </span></div><div><span style="font-size:12.8px"><br></span></div><div><span style="font-size:12.8px">Any comments?</span></div><div><span style="font-size:12.8px">Thank you, </span></div><span class="m_1630399494073191729m_918830375935540537m_4894884932956072082gmail-m_-6564103145536102577m_-3752295078917012961m_-4948517209037813307m_1147697769893715591HOEnZb"><font color="#888888"><div><span style="font-size:12.8px"><br></span></div><div><span style="font-size:12.8px">ping</span></div></font></span></div><div class="m_1630399494073191729m_918830375935540537m_4894884932956072082gmail-m_-6564103145536102577m_-3752295078917012961m_-4948517209037813307m_1147697769893715591HOEnZb"><div class="m_1630399494073191729m_918830375935540537m_4894884932956072082gmail-m_-6564103145536102577m_-3752295078917012961m_-4948517209037813307m_1147697769893715591h5"><div class="gmail_extra"><br><div class="gmail_quote">On Wed, Jan 17, 2018 at 7:48 PM, 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:0px 0px 0px 0.8ex;border-left:1px solid rgb(204,204,204);padding-left:1ex"><div dir="auto">Hi Ping,<div dir="auto"><br></div><div dir="auto">If it's still running and gave only that error then probably it was just a single voxel that failed and the rest is working. However, I recommend you first try to fit a smaller dataset (just a few voxels or a single slice) just to check the results make sense.</div><div dir="auto"><br></div><div dir="auto">I should mention that the code I gave you is slower than Dipy's public version for reasons I won't get into, so don't worry if you have to wait longer than before.</div><div dir="auto"><br></div><div dir="auto">Best,</div><div dir="auto">Rutger</div></div><div class="m_1630399494073191729m_918830375935540537m_4894884932956072082gmail-m_-6564103145536102577m_-3752295078917012961m_-4948517209037813307m_1147697769893715591m_3827841414103749949HOEnZb"><div class="m_1630399494073191729m_918830375935540537m_4894884932956072082gmail-m_-6564103145536102577m_-3752295078917012961m_-4948517209037813307m_1147697769893715591m_3827841414103749949h5"><div class="gmail_extra"><br><div class="gmail_quote">On 18 Jan 2018 00:58, "Ping-Hong Yeh" <<a href="mailto:pinghongyeh@gmail.com" target="_blank">pinghongyeh@gmail.com</a>> wrote:<br type="attribution"><blockquote class="gmail_quote" style="margin:0px 0px 0px 0.8ex;border-left:1px solid rgb(204,204,204);padding-left:1ex"><div dir="ltr">Hi Rutger, <div><br></div><div>Thanks again for the prompt reply. </div><div><br></div><div>Adding "mask" to mapmri have fixed the error; however, another error shows up, </div><div><br></div><div><div>mapfit_L = map_model_L.fit(data,mask=data<wbr>[..., 0]>0)</div><div>dipy/core/geometry.py:129: RuntimeWarning: invalid value encountered in true_divide</div><div> theta = np.arccos(z / r)</div><div>dipy/reconst/mapmri_pa.py:364: UserWarning: The MAPMRI positivity constraint depends on CVXOPT (<a href="http://cvxopt.org/" target="_blank">http://cvxopt.org/</a>). CVXOPT is licensed under the GPL (see: <a href="http://cvxopt.org/copyright.html" target="_blank">http://cvxopt.org/copyright.ht<wbr>ml</a>) and you may be subject to this license when using the positivity constraint.</div><div> warn(w_s)</div><div>dipy/reconst/mapmri_pa.py:413: UserWarning: Optimization did not find a solution</div><div> warn('Optimization did not find a solution')</div><div>Error: Couldn't find per display information</div></div><div><br></div><div><br></div><div>It is still running though. Should i stop the running? </div><div><br></div><div>Thank you. </div><div>ping</div></div><div class="gmail_extra"><br><div class="gmail_quote">On Tue, Jan 16, 2018 at 7:18 PM, 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:0px 0px 0px 0.8ex;border-left:1px solid rgb(204,204,204);padding-left:1ex"><div dir="ltr"><div>Hi Ping,<br><br></div>Reading the error messages, it looks like you're fitting a masked voxel. The following error:<span><br><br><div>/Library/Python/2.7/site-packa<wbr>ges/dipy/reconst/mapmri_pa.py:<wbr>389: RuntimeWarning: invalid value encountered in divide</div></span><div><span> data = np.asarray(data / data[self.gtab.b0s_mask].mean(<wbr>))<br><br></span> says you're dividing by either zero or NaN, which means your b0 value of that voxel was zero (or you had no b0 values possibly). Note that mapmri needs at least one b0 measurement.<br></div><div>I recommend you check if it works when you fit a voxel that you know for sure is in white matter. If it works, you can do something like <span style="font-size:12.8px">map_model_L</span>.fit(data, mask=data[..., 0]>0) to use a mask that only fits if the first measured DWI is positive (assuming your first measurement is a b0).<br><br></div><div>Best,<br></div><div>Rutger<br></div><div><br></div><br></div><div class="m_1630399494073191729m_918830375935540537m_4894884932956072082gmail-m_-6564103145536102577m_-3752295078917012961m_-4948517209037813307m_1147697769893715591m_3827841414103749949m_-1069436651337774449m_-627939652751856750HOEnZb"><div class="m_1630399494073191729m_918830375935540537m_4894884932956072082gmail-m_-6564103145536102577m_-3752295078917012961m_-4948517209037813307m_1147697769893715591m_3827841414103749949m_-1069436651337774449m_-627939652751856750h5"><div class="gmail_extra"><br><div class="gmail_quote">On 16 January 2018 at 23:46, 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:0px 0px 0px 0.8ex;border-left:1px solid rgb(204,204,204);padding-left:1ex"><div dir="ltr">Hi <span style="font-size:12.8px">Rutger, </span><div><span style="font-size:12.8px"><br></span></div><div><span style="font-size:12.8px">I got an error running the map_model.fit using mapmri_pa. Here is the scripts i used, </span></div><div><span style="font-size:12.8px"><br></span></div><div><span style="font-size:12.8px"><br></span></div><div><div><span style="font-size:12.8px">map_model_L = mapmri_pa.MapmriModel(gtab, radial_order=radial_order,</span></div><span><div><span style="font-size:12.8px"> laplacian_regularization=True<wbr>, # this regularization enhances reproducibility of estimated q-space indices by imposing smoothness</span></div><div><span style="font-size:12.8px"> laplacian_weighting="GCV", # this makes it use generalized cross-validation to find the best regularization weight</span></div><div><span style="font-size:12.8px"> positivity_constraint=True) # this ensures the estimated PDF is positive</span></div></span></div><div><br></div><div>mapfit_L = map_model_L.fit(data)</div><div><br></div><div>, and the error message, </div><div><br></div><div><br></div><div><div>/Library/Python/2.7/site-packa<wbr>ges/dipy/core/geometry.py:129: RuntimeWarning: invalid value encountered in true_divide</div><div> theta = np.arccos(z / r)</div><div>/Library/Python/2.7/site-packa<wbr>ges/dipy/reconst/mapmri_pa.py:<wbr>364: UserWarning: The MAPMRI positivity constraint depends on CVXOPT (http:<a href="http://xopt.org/" target="_blank">xopt.org/</a>). CVXOPT is licensed under the GPL (see: <a href="http://cvxopt.org/copyright.html" target="_blank">http://cvxopt.org/copyright.ht<wbr>ml</a>) and you may be subject to this license when using positivity constraint.</div><div> warn(w_s)</div><div>/Library/Python/2.7/site-packa<wbr>ges/dipy/reconst/mapmri_pa.py:<wbr>389: RuntimeWarning: invalid value encountered in divide</div><div> data = np.asarray(data / data[self.gtab.b0s_mask].mean(<wbr>))</div><div>/Library/Python/2.7/site-packa<wbr>ges/dipy/reconst/mapmri_pa.py:<wbr>413: UserWarning: Optimization did not find a solution</div><div> warn('Optimization did not find a solution')</div><div>/Library/Python/2.7/site-packa<wbr>ges/dipy/reconst/mapmri_pa.py:<wbr>444: UserWarning: Optimization did not find a solution</div><div> warn('Optimization did not find a solution')</div><div>Traceback (most recent call last):</div><div> File "<stdin>", line 1, in <module></div><div> File "/Library/Python/2.7/site-pack<wbr>ages/dipy/reconst/multi_voxel.<wbr>py", line 33, in new_fit</div><div> fit_array[ijk] = single_voxel_fit(self, data[ijk])</div><div> File "/Library/Python/2.7/site-pack<wbr>ages/dipy/reconst/mapmri_pa.py<wbr>", line 465, in fit</div><div> coef_iso = coef_iso / sum(coef_iso * self.Bm_iso)</div><div>UnboundLocalError: local variable 'coef_iso' referenced before assignment</div></div><div><br></div><div><br></div><div>Any suggestions? </div><div><br></div><div>Thank you. </div><span class="m_1630399494073191729m_918830375935540537m_4894884932956072082gmail-m_-6564103145536102577m_-3752295078917012961m_-4948517209037813307m_1147697769893715591m_3827841414103749949m_-1069436651337774449m_-627939652751856750m_-7714582144892354334HOEnZb"><font color="#888888"><div><br></div><div>ping</div></font></span></div><div class="m_1630399494073191729m_918830375935540537m_4894884932956072082gmail-m_-6564103145536102577m_-3752295078917012961m_-4948517209037813307m_1147697769893715591m_3827841414103749949m_-1069436651337774449m_-627939652751856750m_-7714582144892354334HOEnZb"><div class="m_1630399494073191729m_918830375935540537m_4894884932956072082gmail-m_-6564103145536102577m_-3752295078917012961m_-4948517209037813307m_1147697769893715591m_3827841414103749949m_-1069436651337774449m_-627939652751856750m_-7714582144892354334h5"><div class="gmail_extra"><br><div class="gmail_quote">On Fri, Jan 12, 2018 at 6:24 PM, 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:0px 0px 0px 0.8ex;border-left:1px solid rgb(204,204,204);padding-left:1ex"><div dir="ltr"><div><div><div><div><div>Hi Ping,<br><br></div>Attached is the mapmri code that also has PA, just put it in the dipy/reconst/ folder (where also the current mapmri.py file is) and run "python setup.py install" from dipy's main folder. That should make it usable in the same way as the current mapmri module.<br>Note that its based on an old implementation that still works with the "cvxopt" optimizer package, so you'll have to install cvxopt to make it run.<br><br></div>I recommend you use the model with both laplacian regularization and positivity constraint, this give the best results in general.<br><br>from dipy.reconst import mapmri_pa<br>mapmod = mapmri_pa.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>mapfit = mapmod.fit(data)<br></div><div>pa = <a href="http://mapfit.pa" target="_blank">mapfit.pa</a>()<br></div><div><br></div>Aside from the original MAPMRI citation for Ozarslan et al. (2013), note that the relevant citation for dipy's laplacian-regularized MAP-MRI implementation is [1].<br>[1] 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.<br><br></div>Hope it helps and let me know if you need anything else,<br></div>Rutger<br><div><div><br> </div></div></div><div class="m_1630399494073191729m_918830375935540537m_4894884932956072082gmail-m_-6564103145536102577m_-3752295078917012961m_-4948517209037813307m_1147697769893715591m_3827841414103749949m_-1069436651337774449m_-627939652751856750m_-7714582144892354334m_523379519876886715HOEnZb"><div class="m_1630399494073191729m_918830375935540537m_4894884932956072082gmail-m_-6564103145536102577m_-3752295078917012961m_-4948517209037813307m_1147697769893715591m_3827841414103749949m_-1069436651337774449m_-627939652751856750m_-7714582144892354334m_523379519876886715h5"><div class="gmail_extra"><br><div class="gmail_quote">On 12 January 2018 at 21:48, 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:0px 0px 0px 0.8ex;border-left:1px solid rgb(204,204,204);padding-left:1ex"><div dir="auto">Hi Roger,<div dir="auto"><br></div><div dir="auto">Thanks for the prompt reply.</div><div dir="auto">May I have the code for estimating PA?</div><span class="m_1630399494073191729m_918830375935540537m_4894884932956072082gmail-m_-6564103145536102577m_-3752295078917012961m_-4948517209037813307m_1147697769893715591m_3827841414103749949m_-1069436651337774449m_-627939652751856750m_-7714582144892354334m_523379519876886715m_8545430226008300247HOEnZb"><font color="#888888"><div dir="auto"><br></div><div dir="auto">Ping</div></font></span></div><div class="m_1630399494073191729m_918830375935540537m_4894884932956072082gmail-m_-6564103145536102577m_-3752295078917012961m_-4948517209037813307m_1147697769893715591m_3827841414103749949m_-1069436651337774449m_-627939652751856750m_-7714582144892354334m_523379519876886715m_8545430226008300247HOEnZb"><div class="m_1630399494073191729m_918830375935540537m_4894884932956072082gmail-m_-6564103145536102577m_-3752295078917012961m_-4948517209037813307m_1147697769893715591m_3827841414103749949m_-1069436651337774449m_-627939652751856750m_-7714582144892354334m_523379519876886715m_8545430226008300247h5"><div class="gmail_extra"><br><div class="gmail_quote">On Jan 12, 2018 3:21 PM, "Rutger Fick" <<a href="mailto:fick.rutger@gmail.com" target="_blank">fick.rutger@gmail.com</a>> wrote:<br type="attribution"><blockquote class="gmail_quote" style="margin:0px 0px 0px 0.8ex;border-left:1px solid rgb(204,204,204);padding-left:1ex"><div dir="ltr"><div><div><div><div><div>Hi Ping,<br><br></div>MAPL is just a name for using laplacian-regularized MAP-MRI. If you're using the dipy mapmri implementation then you're using MAPL by default.<br></div>From a fitted mapmri model you can estimate overall non-gaussianity using fitted_model.ng(), and parallel and perpendicular non-Gaussianity using <span class="m_1630399494073191729m_918830375935540537m_4894884932956072082gmail-m_-6564103145536102577m_-3752295078917012961m_-4948517209037813307m_1147697769893715591m_3827841414103749949m_-1069436651337774449m_-627939652751856750m_-7714582144892354334m_523379519876886715m_8545430226008300247m_-3715064038548378120m_4670068251918390360gmail-pl-en">ng_parallel</span>(<span class="m_1630399494073191729m_918830375935540537m_4894884932956072082gmail-m_-6564103145536102577m_-3752295078917012961m_-4948517209037813307m_1147697769893715591m_3827841414103749949m_-1069436651337774449m_-627939652751856750m_-7714582144892354334m_523379519876886715m_8545430226008300247m_-3715064038548378120m_4670068251918390360gmail-pl-smi"><span class="m_1630399494073191729m_918830375935540537m_4894884932956072082gmail-m_-6564103145536102577m_-3752295078917012961m_-4948517209037813307m_1147697769893715591m_3827841414103749949m_-1069436651337774449m_-627939652751856750m_-7714582144892354334m_523379519876886715m_8545430226008300247m_-3715064038548378120m_4670068251918390360gmail-pl-smi">) and ng_perpendic</span></span><span class="m_1630399494073191729m_918830375935540537m_4894884932956072082gmail-m_-6564103145536102577m_-3752295078917012961m_-4948517209037813307m_1147697769893715591m_3827841414103749949m_-1069436651337774449m_-627939652751856750m_-7714582144892354334m_523379519876886715m_8545430226008300247m_-3715064038548378120m_4670068251918390360gmail-pl-smi"><span class="m_1630399494073191729m_918830375935540537m_4894884932956072082gmail-m_-6564103145536102577m_-3752295078917012961m_-4948517209037813307m_1147697769893715591m_3827841414103749949m_-1069436651337774449m_-627939652751856750m_-7714582144892354334m_523379519876886715m_8545430226008300247m_-3715064038548378120m_4670068251918390360gmail-pl-smi"><span class="m_1630399494073191729m_918830375935540537m_4894884932956072082gmail-m_-6564103145536102577m_-3752295078917012961m_-4948517209037813307m_1147697769893715591m_3827841414103749949m_-1069436651337774449m_-627939652751856750m_-7714582144892354334m_523379519876886715m_8545430226008300247m_-3715064038548378120m_4670068251918390360gmail-pl-en">perpendicular</span>ular(<wbr>).<br></span></span></div><span class="m_1630399494073191729m_918830375935540537m_4894884932956072082gmail-m_-6564103145536102577m_-3752295078917012961m_-4948517209037813307m_1147697769893715591m_3827841414103749949m_-1069436651337774449m_-627939652751856750m_-7714582144892354334m_523379519876886715m_8545430226008300247m_-3715064038548378120m_4670068251918390360gmail-pl-smi"><span class="m_1630399494073191729m_918830375935540537m_4894884932956072082gmail-m_-6564103145536102577m_-3752295078917012961m_-4948517209037813307m_1147697769893715591m_3827841414103749949m_-1069436651337774449m_-627939652751856750m_-7714582144892354334m_523379519876886715m_8545430226008300247m_-3715064038548378120m_4670068251918390360gmail-pl-smi">Propagator Anisotropic is not included in the current dipy implementation. However, I do have a personal version of dipy's mapmri implementation that includes it, if you're interested.<br><br></span></span></div><span class="m_1630399494073191729m_918830375935540537m_4894884932956072082gmail-m_-6564103145536102577m_-3752295078917012961m_-4948517209037813307m_1147697769893715591m_3827841414103749949m_-1069436651337774449m_-627939652751856750m_-7714582144892354334m_523379519876886715m_8545430226008300247m_-3715064038548378120m_4670068251918390360gmail-pl-smi"><span class="m_1630399494073191729m_918830375935540537m_4894884932956072082gmail-m_-6564103145536102577m_-3752295078917012961m_-4948517209037813307m_1147697769893715591m_3827841414103749949m_-1069436651337774449m_-627939652751856750m_-7714582144892354334m_523379519876886715m_8545430226008300247m_-3715064038548378120m_4670068251918390360gmail-pl-smi">Best,<br></span></span></div><span class="m_1630399494073191729m_918830375935540537m_4894884932956072082gmail-m_-6564103145536102577m_-3752295078917012961m_-4948517209037813307m_1147697769893715591m_3827841414103749949m_-1069436651337774449m_-627939652751856750m_-7714582144892354334m_523379519876886715m_8545430226008300247m_-3715064038548378120m_4670068251918390360gmail-pl-smi"><span class="m_1630399494073191729m_918830375935540537m_4894884932956072082gmail-m_-6564103145536102577m_-3752295078917012961m_-4948517209037813307m_1147697769893715591m_3827841414103749949m_-1069436651337774449m_-627939652751856750m_-7714582144892354334m_523379519876886715m_8545430226008300247m_-3715064038548378120m_4670068251918390360gmail-pl-smi">Rutger<br></span></span></div><div class="gmail_extra"><br><div class="gmail_quote">On 12 January 2018 at 16:49, 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:0px 0px 0px 0.8ex;border-left:1px solid rgb(204,204,204);padding-left:1ex"><div dir="ltr">Hi DIPY users, <div><br></div><div>I would like to know the way of estimating non-Gaussian and PA, mentioned in 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">Avram et al. “Clinical feasibility of using mean apparent propagator (MAP) MRI to characterize brain tissue microstructure” paper, using MAPMRI or MAPL model. </span></div><div><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"><br></span></div><div><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">Thank you. </span></div><span class="m_1630399494073191729m_918830375935540537m_4894884932956072082gmail-m_-6564103145536102577m_-3752295078917012961m_-4948517209037813307m_1147697769893715591m_3827841414103749949m_-1069436651337774449m_-627939652751856750m_-7714582144892354334m_523379519876886715m_8545430226008300247m_-3715064038548378120m_4670068251918390360HOEnZb"><font color="#888888"><div><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"><br></span></div><div><font color="#000000" face="Lucida Grande, Lucida Sans Unicode, Geneva, Verdana, sans-serif"><span style="font-size:14px;letter-spacing:-0.14px">Ping</span></font></div></font></span></div>
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