@KulikovaSofya The load_trk API drastically change recently. This function does not return a tuple (streamlines, header) anymore but an object called StatefulTractogram
Use it as follow:
sft = load_trk('../data/tracts.trk', 'same')
streamlines = sft.streamlines
affine, dimensions, voxel_sizes, voxel_order = sft.space_attributes
OR
affine = sft.affine
dimensions = sft.dimensions
voxel_sizes = sft.voxel_sizes
voxel_order = sft.voxel_order
(Some example available here:https://dipy.org/documentation/1.0.0./examples_built/streamline_formats/)
This object contains numerous function related to spatial manipulation (to_vox(), to_rasmm(), to_voxmm(), etc.)
But you can manipulate streamlines 'by hand' like before if you choose to.
But you can manipulate streamlines 'by hand' like before if you choose to.
Actually there is another pull request patch 2 dipy/dipy#2094 @Garyfallidis @ShreyasFadnavis @skoudoro . It would be really nice if you check that out. It's a proposal to add tutorials in a beginner friendly manner in Github DIPY repo.
I suppose there is a most recent version @salomaaa. It should work too! It is ok as soon as you have a compiler in your machine
yes @ShreyaKapoor18, I recommend you to have a look on the tutorial, tractography / Fiber tracking section : https://dipy.org/tutorials .
Strange @arnaudbore, if you look this link https://pypi.org/project/dipy/#files dipy 1.1.1 is present for many OS
What is your environment ?
Hi there, I have questions about the usage of the word “reconstruction” in dipy’s documentation. e.g. for tensor model, the tutorial’s title is “Reconstruction of the diffusion signal with the Tensor model“. Is it really “reconstruction”? Or it’s some “modeling”. Because I saw in MRI field, reconstruction usually refers to reconstruct the MR image from k-space data, but here we actually already have the images, which makes me confused.
@arokem Hi. I'm trying to apply pyAFQ in these days. I do not apply it using the cli, but I use directly some of the defined functions. In my case I have available the tractogram coregistered to MNI space, but not the DWI data, and I m trying to apply only AFQ segmentation to the tractogram. Now, because of the lack of the dwi I m calling the function Segmentation.segment by providing reg_tamplate, img_affine, and mapping where reg_template is MNI152_T1_1mm, img_affine is the affine of this mni_t1, and mapping is the indentity mapping (since tractogram and template are already in the same space). After this premise I explain the problem: The segmentation seems to work in the first step, waypoints rois filtering, but it produces a strange behaviour in the second step, endpoints filtering, where some tracts like ATR have a huge drop in terms of number of streamlines. I looked into detail on the reason of this drop, by saving the endpoints AAL rois corresponding to the ATR_L, and I overlap it with the segmentation produced by AFQ without endpoints filtering.
here what I obtain: the endpoint roi is in the wrong hemisphere.
@pietroastolfi : thanks for reporting. Could you please post an issue on the pyAFQ repo: https://github.com/yeatmanlab/pyAFQ/issues ?
That's a great find and might be related to things that others have seen: yeatmanlab/pyAFQ#235
My main conclusion is that this part of the processing is not yet robust enough. Works sometimes, but not bullet proof yet
So, we need to investigate
@tecork : I believe the examples here: https://dipy.org/documentation/1.1.1./examples_built/affine_registration_3d/#example-affine-registration-3d
should work in 2d as well
If not, in a pinch I might try adding a singleton dimension to your data just to get it in there
from dipy.tracking.local import LocalTracking, ThresholdTissueClassifier
the error : No module named 'dipy.tracking.local'
from dipy.tracking.local_tracking import LocalTracking
According to the api documentation https://dipy.org/documentation/1.1.0./api_changes/ , ThresholdTissueClassifier -> ThresholdStoppingCriterion
Posted a question on GitHub (dipy/dipy#2164) but I'll throw it here for visibility too. Ran DKI reconstruction and got some weird outputs. The DTI scalars look ok but the DKI scalars don't resemble white matter paths. Any help would be great.
Hi everyone, I am moving over from ANTsPy to Dipy and was wondering how I can apply a diffeomorphic transform to points after the registration is done. That is, I am looking for a function that takes a set of points and a registration transformation as input and outputs the transformed points moved along the velocity field of the transform, like ants.apply_transforms_to_points in the AntsPy package. Many thanks for any help!
Hi @Garyfallidis , how are you doing? A question to the guru: once I perform clustering with QuickBundles, how do I get access to the streamline that is the closest to the centroid in its cluster? In my application, I cannot simply take the resulting centroid, as it has a lower number of points. Any help would be appreciated! ;-)
In general, once I obtain a cluster, I'd like to extract the streamline (from the input set, i.e., exactly the same geometry, nb of points etc) that best represents the cluster. We implemented a way, by recomputing the distance of each streamline from the centroid, but I was wondering whether there was a more direct way from the output of QuickBundles. For instance, without the need to recompute the distances
Good morning.hope everyone is fine.I have a small problem.. I would like to minimize the test_function. basically, test_function() takes 3 variables - m, n and p. The goal is to find such values of these 3 variables that the function returns the minimal possible value.I am using nelder-mead minimization problem. while running my script for x0 = [25.0, 45.0, 10.0] i am getting error like Maximum number of function evaluations has been exceeded. Anyway I followed the stackoverflow link to write my code. https://stackoverflow.com/questions/55751317/minimize-multivariable-function please help me on this.Thanks.My data and script is attached here https://i.fluffy.cc/HLR1jCJLLV8lX4NfjSqbjRKG6DsB4bwS.html stackoverflow link is blocked in my area.
Hi. I'm new to dipy and would like to set up an analysis pipeline using the free water elimination model. As I'm working through the preprocessing examples, I did not find information for motion and geometric correction. This will be an important step for me. Can you point me in the right direction? Should I use another tool to generate motion/eddy current - corrected data first? Thanks.
Hi @CherylMcC I'd suggest trying QSIPrep: https://qsiprep.readthedocs.io/en/latest/. It will do both of these corrections with a single interpolation