Thank you for the informative response :)
Tabinda
On Sat, Jan 27, 2018 at 2:22 AM, Neher, Peter <[email protected]>
wrote:
> The code is indeed a bit messy there :)
>
>
> The next step is to multiply your coefficient matrix C (variable "coeffs"
> in the code) with the spherical harmonics basis functions evaluated in the
> 100 spatial directions (variable "m_SphericalHarmonicBasisMatrix" in the
> code). The multiplication happens here:
>
> https://phabricator.mitk.org/source/mitk/browse/master/
> Modules/DiffusionImaging/DiffusionCore/include/Algorithms/Reconstruction/
> itkAnalyticalDiffusionQballReconstructionImageFilter.cpp;
> d39eb0f986efcb654fd71994928661dc9f3f85ef$386
>
>
> The evaluation of the basis functions is done here:
>
> https://phabricator.mitk.org/source/mitk/browse/master/
> Modules/DiffusionImaging/DiffusionCore/include/Algorithms/Reconstruction/
> itkAnalyticalDiffusionQballReconstructionImageFilter.cpp;
> d39eb0f986efcb654fd71994928661dc9f3f85ef$752
>
>
> Peter
>
>
> ------------------------------
> *Von:* Tabinda Sarwar <[email protected]>
> *Gesendet:* Freitag, 26. Januar 2018 07:04
> *An:* Neher, Peter
> *Cc:* [email protected]
>
> *Betreff:* Re: [mitk-users] Resampling the signal for tractography using
> machine learning
>
> I can compute the voxel-wise coefficients of the corresponding spherical
> harmonics fit of the diffusion-weighted signal using equation [image:
> Inline image 2] as given in the research paper of analytical QBI paper.
> Afterwards I am confused how to sample these coefficients to 100 directions
> equally distributed over the hemisphere? I can estimate SH basis for for
> 100 directions but how to link this information to the computed
> coefficients?
>
> Tabinda
>
> On Thu, Jan 25, 2018 at 5:29 PM, Neher, Peter <[email protected]>
> wrote:
>
>> If you go to the “Q-Balls” view in MITK Diffusion, you can also select
>> “Raw Signal Only” as reconstruction and you will simply get an
>> interpolation of the input signal, albeit with a different number of
>> sampling directions. This is maybe good for visualizing what the result
>> looks like.
>>
>>
>>
>> *Von:* Neher, Peter [mailto:[email protected]]
>> *Gesendet:* Donnerstag, 25. Januar 2018 07:26
>> *An:* Tabinda Sarwar <[email protected]>;
>> [email protected]
>>
>> *Betreff:* Re: [mitk-users] Resampling the signal for tractography using
>> machine learning
>>
>>
>>
>> Hi Tabinda,
>>
>>
>>
>> this is indeed a bit confusing. We do not calculate actual Q-balls here.
>> The analytical qball filter is also capable of only fitting the raw signal
>> with spherical harmonics. This is done by setting
>> “filter->SetNormalizationMethod(InterpolationFilterType::
>> QBAR_RAW_SIGNAL);”
>>
>>
>>
>> By the way, please answer to the list so that other users can also read
>> out conversation.
>>
>>
>>
>> Peter
>>
>>
>>
>> *Von:* Tabinda Sarwar [mailto:[email protected]
>> <[email protected]>]
>> *Gesendet:* Donnerstag, 25. Januar 2018 00:38
>> *An:* Neher, Peter <[email protected]>
>> *Betreff:* Re: [mitk-users] Resampling the signal for tractography using
>> machine learning
>>
>>
>>
>> Hi Peter,
>>
>>
>>
>> The high level details which I can understand is that first from raw
>> input dMRI ODF is computed using analytical Q-ball method which undergoes
>> sampling. If I am right, doesn't it makes the input features dependent on
>> the ODF computation technique?
>>
>>
>>
>> Tabinda
>>
>>
>>
>> On Wed, Jan 24, 2018 at 5:45 PM, Neher, Peter <[email protected]>
>> wrote:
>>
>> Dear Tabinda,
>>
>> we interpolate the original signal using spherical harmonics and then
>> sample 100 directions form this representations:
>> https://phabricator.mitk.org/source/mitk/browse/master/Modul
>> es/DiffusionImaging/FiberTracking/Algorithms/TrackingHandler
>> s/mitkTrackingHandlerRandomForest.cpp;2642ee4902f4b1e8f5c443
>> f22b30aa896795d5da$105
>>
>> The actual fitting of the spherical harmonics and the sampling is done in
>> this class: https://phabricator.mitk.org/source/mitk/browse/master/Modul
>> es/DiffusionImaging/DiffusionCore/include/Algorithms/
>> Reconstruction/itkAnalyticalDiffusionQballReconstructionImageFilter.h
>>
>> The number of directions is defined by the template parameter
>> NumberOfSignalFeatures of the TrackingHandlerRandomForest class.
>>
>> Cheers,
>> Peter
>>
>> ---------------------------------------------------------------------
>>
>> Dr. Peter F. Neher
>> Division of Medical Image Computing (MIC)
>> Scientist
>>
>> German Cancer Research Center (DKFZ)
>> Im Neuenheimer Feld 280
>> <https://maps.google.com/?q=Im+Neuenheimer+Feld+280+%0D+69120+Heidelberg+%0D+Germany&entry=gmail&source=g>
>> 69120 Heidelberg
>> Germany
>> Phone: +49 6221 42-2330
>>
>> [email protected]
>> www.dkfz.de/en/mic/
>>
>> ________________________________________
>> Von: tabinda <[email protected]>
>> Gesendet: Mittwoch, 24. Januar 2018 06:16
>> An: [email protected]
>> Betreff: [mitk-users] Resampling the signal for tractography using
>> machine learning
>>
>>
>>
>> Hi!
>>
>> I am reading the paper "Neher, P. F., Côté, M.-A., Houde, J.-C.,
>> Descoteaux,
>> M. & Maier-Hein, K. H. Fiber tractography using machine learning.
>> Neuroimage
>> 158, 417–429 (2017)" and am trying to understand the methodology using the
>> MITK source code. But I am confused how the input raw signal is resampled
>> to
>> 100 directions? I couldn't locate the source code for this particular
>> step.
>> I need to know how mathematically this is done?
>> For example if original data is (140x140x90) is acquired using 30
>> gradients
>> (which makes the dMRI 140x140x90x30), resampling causes the dMRI to be of
>> size 140x140x90x100? Guidance on the resampling will help me to understand
>> the algorithm. Any help will be highly appreciated.
>>
>> Thanks and Regards,
>> Tabinda
>>
>>
>>
>> --
>> Sent from: http://mitk-users.1123740.n5.nabble.com/
>>
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>>
>>
>
>
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