skoudoro on master
NF: add unbiased groupwise bund… TEST: add tests for groupwise s… DOC: add groupwise slr example and 12 more (compare)
my_sphere = Sphere(xyz=gtab.bvecs[~gtab.b0s_mask])
voxel2streamlineto see which streamlines pass through which voxels. I feed in the streamlines and the inverse of the DWI affine to map from streamline to voxel. I am having trouble interpreting the output, as I do not know how to map voxel index to location. Any guidance would be appreciated, thanks!
Hi Everyone! We are very happy to announce that a new educational course is available online: https://youtube.com/playlist?list=PLRZ9VSqV-6soOC0rUEAOV-QiSa_Qxk8JM :rocket: This is a complete course on Diffusion MRI with Theory and Practice :snake: This is your chance to go from zero-to-hero in using :star: DIPY :star: Topics include -
:white_check_mark: dMRI Basics
:white_check_mark: Microstructure Modeling
:white_check_mark: Dictionary Learning
:white_check_mark: Advanced Multidimensional Diffusion Encodings
:white_check_mark: Tissue Classification
:white_check_mark: Deep Learning methodologies in MRI
:white_check_mark: High Field MRI
:white_check_mark: GPU Acceleration
:white_check_mark: Data Harmonization
:white_check_mark: ML in the Clinic and much more.....
Do not miss out on this golden opportunity! If you do like and use DIPY please do give us a star and cite the piece of software that you are using! It encourages us to keep going! May your data live a new life!!
data, affine, img = load_nifti(file2_path, return_img=True) bvals, bvecs = read_bvals_bvecs(fbval, fbvec) print(bvals) gtab = gradient_table(bvals, bvecs) print(gtab) tenmodel = TensorModel(gtab) print(len(bvals)) new_img = img.get_fdata() tenfit = tenmodel.fit(new_img)
dipy.align._public.SymmetricDiffeomorphicRegistration. Using the
.update()method, I have interpolated the displacement to a new 3D shape of (S,R,C,3). I would now like to split this 3D array into a list of 2D arrays and then apply each deformation field on a new 2D image. Is there a way of doing that?