@ShreyasFadnavis My first effort multiprocessing failed with recursion error. It does not resolve even after increasing the recursion limit to 10000.
vol_shape = dwi.shape[:-1]
n_voxels = np.prod(vol_shape)
voxel_by_dir = dwi.reshape(n_voxels, dwi.shape[-1])
voxel_array = [voxel_by_dir[i:i + 1, :] for i in range(voxel_by_dir.shape[0])]
def msmt_csd_pool(vox):
return mcsd_model.fit(vox)
if name == 'main':
with Pool() as p:
mcsd_fit = p.map(msmt_csd_pool, voxel_array)
p.close()
p.join()
results = [executor.map(msmt_csd_pool, slice_array)]
@kodiweera : if
maskis a binary 3d array (True/False for within/outside the region of interest) anddatais a 4D array containing diffusion data, thendata[np.where(mask)]should be a 2D array with each row being a voxel and each column being a direction of measurement.
@arokem Thanks. Also for 3D : fslmaths <input> -mas <mask> <ouput>
Hello @/all ,
Dr. Leevi Kerkelä (UCL) will discuss today (Thursday 15 April at 1pm EST / 7pm CET / 10am PT) his project on integrating Q-Space Trajectory Imaging in DIPY.
The zoom link for the open meeting is https://iu.zoom.us/j/84926066336.
We hope to see you online!
Cheers !
Hi DIPY team. Wanted to ask about a memory error I am getting when running dipy_track on a linux server.
File "~/cowenswalton/conda-envs/envs/buan_env/lib/python3.8/site-packages/dipy/tracking/utils.py", line 881, in transform_tracking_output yield np.dot(sl, lin_T) + offset MemoryError
Super grateful for any advice, or where i can find some.
Patch2Self (standalone) should do as good as Patch2Self + MPPCA. Any noise that persists due to MPPCA will anyways be suppressed by P2S.
Does anyone have experience tracking using DSI reconstructed data? I have a dataset of 145x174x145, so I can't fit all the data since that uses too much RAM. I tried using a memmap, but this still needs too much memory (134 GB).
I can construct the model and fit the data to the model using nindex and saving to a memmap, but using that memmap in e.g. the ClosestPeakDirectionGetter causes a memory error. Does anyone know a solution to this? Thank you very much in advance!
I have a theoretical question about DKI minimum number of independent directions. Indeed, we are acquiring neonatal SC diffusion images at hospital but we are limited by acquisition time constraints: the clinical protocol is already quite long and we have to put the spine DKI sequence at the end since it is for research. We set up a protocol made up of 6b=0; 13 b=700; 13 b=2100 b values for a duration of 4 min 30 s thanks to multi band technique. I know it is low as a protocol for DKI but I tried increasing directions to 15 (minimum theoretically required for DKT estimation) and it lasts 1 minute more , and it often happens the neonate wakes up from the slight sedation just during the end of that sequence, so the clinicians opted for the 13-direction sequence.
Now, I am here to ask you a suggestion about which choice to take. I have read about protocol using just 9 directions per b-value for fast DKI protocol. If I use 13 directions, would DKI measures I compute in dipy be still reliable ? Or should I use another estimation method in case of so low directions ?
thank you very much in advance
Hello @/all ,
Quick reminder for the meeting today at 1pm EST / 7pm CET / 10am PT!
@gabknight will talk about the new Tractography competition.
We will have a brief talk about our Tracking framework
@skoudoro will make a quick overview of the future Release. Let us know if there is any request.
Cheers!
ps: same link as usual: https://iu.zoom.us/j/84926066336
Hi @smeisler
Currently, dipy_labelsbundles does not take reference files as input. However, you can write a simple python script to bring bundles into the native space. Here's the bundle segmentation tutorial https://dipy.org/documentation/1.4.0./examples_built/bundle_extraction/#example-bundle-extraction
at the end of this tutorial after extracting the bundle, we are saving the bundle in the native space. Something like this:
`reco_af_l = StatefulTractogram(target[af_l_labels], target_header,
Space.RASMM) `
`save_trk(reco_af_l, "AF_L.trk", bbox_valid_check=False) `You can use the labels.npy file saved by RecoBundles command line.
When trying to apply cross-validation for dki model on 7T adult HCP data the following error arises:
(```
base) synapsi@charlie:/media/synapsi/dkeTest/new_version/code$ python goodness_of_fit.py
/home/synapsi/anaconda3/lib/python3.8/site-packages/dipy/core/gradients.py:295: UserWarning: b0_threshold (value: 50) is too low, increase your b0_threshold. It should be higher than the lowest b0 value (55.0).
warn("b0_threshold (value: {0}) is too low, increase your \
Data & Mask Loaded!
Traceback (most recent call last):
File "goodness_of_fit.py", line 21, in <module>
dki_cc = xval.kfold_xval(dki_model, cc_vox, 2)
File "/home/synapsi/anaconda3/lib/python3.8/site-packages/dipy/reconst/cross_validation.py", line 107, in kfold_xval
raise ValueError(msg)
ValueError: np.mod(143, 2) is 1
I'm computing affine transforms for co-registering anatomical MRI scans.
Sometimes I would like to re-use those transforms, so in a case similar to the example here
https://dipy.org/documentation/1.2.0./examples_built/affine_registration_3d/#example-affine-registration-3d
my final transform is the result of this command:
rigid = affreg.optimize ( static, moving, transform, params0,
static_affine, moving_affine,
starting_affine = translation.affine );
final = rigid;
after first having aligned the centres of gravity.
After this command I can call
resampled = final.transform ( moving );
and then I get the desired result.
What I don't understand is how to store the transform and re-use it later.
I have tried to just save the object 'final' with
np.savez ( 'transformation.npz', final );
and then load it later with
with np.load ( 'transformation.npz', allow_pickle = True ) as npzfile:
final = npzfile [ 'arr_0' ];
and then re-run
resampled = final.transform ( moving );
but that returns the following error:
resampled = final.transform ( moving );
AttributeError: 'numpy.ndarray' object has no attribute 'transform'
So what I understand from this is that the output 'rigid' from the affreg.optimize() command has an attribute
'transform', but when I save it and then load it again it does not any more.
Is there a(nother) way to save and (re)load transforms?
Hi, all. I have some problems when fit the free water components. Does anyone know how to fix this? Thanks a lot!
I having using the fwdtimodel and fwdtifit to map the free water compartment.
The test data is download from HCP, with 288 directions and 2 b-values:
The code is as follows:
fwdtimodel = fwdti.FreeWaterTensorModel(gtab)
fwdtifit = fwdtimodel.fit(data, mask=mask)
fwvolume = fwdtifit.f
But there are some warning information:
/usr/local/lib64/python3.6/site-packages/scipy/optimize/minpack.py:475: RuntimeWarning: Number of calls to function has reached maxfev = 1800.
warnings.warn(errors[info][0], RuntimeWarning)
/usr/local/lib64/python3.6/site-packages/dipy/reconst/fwdti.py:311: RuntimeWarning: overflow encountered in exp
SIpred = (1-FS)np.exp(np.dot(W, all_new_params)) + FSS0*SFW.T
/usr/local/lib64/python3.6/site-packages/dipy/reconst/fwdti.py:312: RuntimeWarning: overflow encountered in square
F2 = np.sum(np.square(SI - SIpred), axis=0)
/usr/local/lib64/python3.6/site-packages/dipy/reconst/fwdti.py:458: RuntimeWarning: overflow encountered in exp
y = (1-f) * np.exp(np.dot(design_matrix, tensor[:7])) + \
I wonder whether these warnings have influenced the output images and anyway to fix them?
Many thanks for your help!
To save/load the transformation object you can try using pickle instead of numpy:
import pickle
filehandler = open('transform.obj', 'wb')
pickle.dump(final, filehandler)
filehandler = open('transform.obj', 'rb')
final = pickle.load(filehandler)