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 [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]<mailto:[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]<mailto:[email protected]>]
Gesendet: Donnerstag, 25. Januar 2018 07:26
An: Tabinda Sarwar
<[email protected]<mailto:[email protected]>>;
[email protected]<mailto:[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]]
Gesendet: Donnerstag, 25. Januar 2018 00:38
An: Neher, Peter <[email protected]<mailto:[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]<mailto:[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/Modules/DiffusionImaging/FiberTracking/Algorithms/TrackingHandlers/mitkTrackingHandlerRandomForest.cpp;2642ee4902f4b1e8f5c443f22b30aa896795d5da$105
The actual fitting of the spherical harmonics and the sampling is done in this
class:
https://phabricator.mitk.org/source/mitk/browse/master/Modules/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]<mailto:[email protected]>
www.dkfz.de/en/mic/<http://www.dkfz.de/en/mic/>
________________________________________
Von: tabinda
<[email protected]<mailto:[email protected]>>
Gesendet: Mittwoch, 24. Januar 2018 06:16
An: [email protected]<mailto:[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
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