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
69120 Heidelberg
Germany
Phone: +49 6221 42-2330
[email protected]
www.dkfz.de/en/mic/<http://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
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