AMICO Maps histograms general question- Not an issue

[NeuroImagingWorld]

Hello Dr. Daducci,

This is not an issue but general question in regard to histograms for the AMICO maps that are generated. There are spikes showing up at certain intervals for FICVF and OD histograms as attached. When we threshold the image for these values the data seem to be spread across the brain.

Can you please advice if this is expected.

Thanks,
Prasanna

[daducci]

Dear Prasanna,

I think this is an effect of the discretization in the dictionary. When you apply the l1 norm to promote sparsity in the solution, only some atoms are retained from all possible combinations that are considered.
Did you try to play with the regularization?

I had observed similar spikes also in the original NODDI fitting, especially in the OD parameter, where the values attributed to CSF is always the same.

Best,
Alessandro

[NeuroImagingWorld]

Hello,

Attached are the comparison of NODDI and AMICO histograms.

Here are the voxels associated with corresponding spikes for ICVF from AMICO results

We have tried adjusting the regularization parameters and following are the results.

Can you please share your thoughts.

Thanks,
Prasanna

[daducci]

Dear Prasanna,

sorry for my delay, I am at the moment on kind of "paternity leave" and have had not much time to work. Sorry for that; I'm trying to catch up with work as fast as I can!

What you highlight seems to me related to the discretization AMICO uses in the dictionary. I think the spikes are generated by this discretization that somehow limits the possible output values for the parameters. These values seem in fact widely spread in the brain, and not concentrated in any specific location. Surely there are differences between the original model and the aMICO approximation (as I previously described), but this is another topic.

Do you think this could badly influence your analyses?

On a side note, we have recently developed an alternative way to fit models within AMICO that can enforce, for free, spatial regularization of the estimations in each voxel; this might help also with the issue you describe here. We have not tested this method yet, but I will speak with @ejcanalesr (the actual person who developed this new solver) and I will discuss with him about its applicability to mitigate the discretization issue you have described.

I will keep you posted!
Best,
Ale

[NeuroImagingWorld]

Hello Dr. Daducci,

Thank you for looking into this. We are comparing the response from both NODDI and AMICO to determine if current NODDI processing can be replaced with AMICO as this is the faster version of NODDI or if there are areas that we need to be cautious about in analyzing the results in tumor regions in addition to normal brain processing.

Will keep you posted once we do quantitative analysis in tumor regions. Appreciate your feedback.

Thanks,
Prasanna

[daducci]

Dear Prasanna,

did you finally got any useful information from the comparison between AMICO and NODDI implementations?

[NeuroImagingWorld]

Hello Dr. Daducci,

Thank you for following up on this item. Based on our preliminary analysis we did notice that FICVF values are similar between NODDI and AMICO in both tumor and white mater regions. However, in some cases, ODI and FISO values from AMICO seem to be higher in tumor region compared to NODDI results thus effecting the correlations with histopathology.

Can you please suggest if there are any parameters we could adjust to make these two results match.

Thanks,
Prasanna

[steelec]

To follow up on this, I have run into the same issue with discretization in the ICVF and ODI (running the python version of AMICO). Has there been any progress on optimal paramteres or the method by @ejcanalesr?
Chris
Screenshot of AMICO calculated OD versus that calculated by the NODDI matlab toolbox. The discretizations appear at values very close to .19,.29,.39...

same comparison, with mean gaussian smoothing of the AMICO OD ouptut, kernel size from left to right = 0.75, 1, 2mm (data resolution = 2x2x2)

[NeuroImagingWorld]

Hello Dr. Dadduci,

Can you please point us to the method proposed by @ejcanalesr, we can plan to include it in further analysis.

Thanks,
Prasanna

[ejcanalesr]

Hello Dr. Prasanna,
The method is not ready yet, however, I will have time to work on it in
july.
I will try to have a final solution as soon as possible.
Thanks,
Regards,

Erick J. Canales-Rodríguez
FIDMAG Germanes Hospitalàries
CIBERSAM
Telf.: 93 652 99 99 ext. 1493

www.fidmag.org http://www.fidmag.org/

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2016-06-12 18:45 GMT+02:00 goals2008 notifications@github.com:

Hello Dr. Dadduci,

Can you please point us to the method proposed by @ejcanalesr
https://github.com/ejcanalesr, we can plan to include it in further
analysis.

Thanks,
Prasanna

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[daducci]

Dear Prasanna,

we have started looking into this issue and we have a question for you. We noticed that in your plots the OD index is about 0.7 whereas in the maps released with the tutorial data OD is about 1. Which version of the code and matlab are you using?

Thanks,
Ale

[NeuroImagingWorld]

Hello,

Thank you for looking into this issue. We are using following Matlab
2013b..and default version of AMICO. Also ODI is about 1 in all the maps. I
think 0.7 maps that were showing up is with L2 norm comparison, screen seem
to have been cut off.

Please let me know if you need more information.

Thanks,
Prasanna

On Thu, Jul 14, 2016 at 4:47 AM, Alessandro Daducci <
notifications@github.com> wrote:

Dear Prasanna,

we have started looking into this issue and we have a question for you. We
noticed that in your plots the OD index is about 0.7 whereas in the maps
released with the tutorial data OD is about 1. Which version of the code
and matlab are you using?

Thanks,
Ale

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[ejcanalesr]

Dear Prasanna,

We have been working on the issue you reported. Fortunately, we have found
a potential solution to obtain very similar reconstructions but without the
spikes you reported in the histograms. It involves only a small correction
in the debiasing step of the estimation, so the numerical algorithm is
totally compatible with the one reported in our article.

As we have tested this preliminar version only in our data (as well as in
synthetic data), we would like to test the method also in different
datasets. This step is important to determine whether the new approach is
robust and stable.

Please, could you send us the data you have used to create the figures
reported in the web? Or alternatively, if you prefer, we can send you this
preliminar version of the code so that you could test the method in your
computer and generate similar figures. This may be possible?

We plan to update the toolbox as soon as possible, after verify the
robustness and stability of the new version.

Thank you very much for your valuable contribution!

Regards,

P.S. Please, find attached here a preliminary result from our evaluation
with the new method. These maps were computed from the example dataset
distributed in the original NODDI toolbox, but we have carried out the
estimation in the whole brain.

Notice that after recomputing the OD maps with the original NODDI, in CSF
we obatin OD = 0.7, instead of the value OD = 1 resulting from the previous
version of the sofware. In our method we have OD=1 in CSF...
Erick J. Canales-Rodríguez
FIDMAG Germanes Hospitalàries
CIBERSAM
Telf.: 93 652 99 99 ext. 1493

www.fidmag.com http://www.fidmag.com/

[ejcanalesr]