Examples#
Note
The examples here are some uses of the analysis and visualization functionality of DIPY, with example data from actual neuroscience experiments, or with synthetic data, which is generated as part of the example.
All the examples presented in the documentation are generated from fully functioning python scripts, which are available as part of the source distribution in the doc/examples folder.
If you want to replicate a particular analysis or visualization, copy or download the relevant .py script, edit out the body of the text of the example and alter it to your purpose.
Contents
Quick Start#
Introduction to Basic Tracking
Preprocessing#
Between-volumes Motion Correction on DWI datasets
Denoise images using Non-Local Means (NLMEANS)
Brain segmentation with median_otsu
Patch2Self: Self-Supervised Denoising via Statistical Independence
Denoise images using Local PCA via empirical thresholds
Denoise images using Adaptive Soft Coefficient Matching (ASCM)
SNR estimation for Diffusion-Weighted Images
Denoise images using the Marcenko-Pastur PCA algorithm
Reconstruction#
Below, an overview of all reconstruction models available on DIPY.
Note
Some reconstruction models do not have a tutorial yet
Method |
Single Shell |
Multi Shell |
Cartesian |
Paper Data Descriptions |
References |
---|---|---|---|---|---|
Yes |
Yes |
Yes |
Typical b-value = 1000s/mm^2, maximum b-value 1200s/mm^2 (some success up to 1500s/mm^2) |
||
Yes |
Yes |
Yes |
Typical b-value = 1000s/mm^2, maximum b-value 1200s/mm^2 (some success up to 1500s/mm^2) |
Yendiki2013, Chang2005, Chung2006 |
|
No |
Yes |
No |
DTI-style acquistion, multiple b=0, all shells should be within maximum b-value of 1000 (or 32 directions evenly distributed 500mm/s^2 and 1500mm/s^2 per Henriques 2017) |
||
No |
Yes |
No |
Dual spin echo diffusion-weighted 2D EPI images were acquired with b values of 0, 500, 1000, 1500, 2000, and 2500 s/mm^2 (max b value of 2000 suggested as sufficient in brain tissue); at least 15 directions |
||
No |
Yes |
No |
None |
||
No |
Yes |
No |
DKI-style acquisition: at least two non-zero b shells (max b value 2000), minimum of 15 directions; typically b-values in increments of 500 from 0 to 2000, 30 directions |
||
No |
Yes |
No |
b-values in increments of 500 from 0 to 2000, 30 directions |
||
Yes |
No |
No |
HARDI data (preferably 7T) with at least 200 diffusion images at b=3000 s/mm^2, or multi-shell data with high angular resolution |
||
Westins CSA |
Yes |
No |
No |
||
No |
Yes |
No |
low b-values are needed |
LeBihan 1984 |
|
No |
Yes |
No |
Fadnavis 2019 |
||
SDT |
Yes |
No |
No |
QBI-style acquisition (60-64 directions, b-value 1000mm/s^2) |
Descoteaux 2009 |
No |
No |
Yes |
515 diffusion encodings, b-values from 12,000 to 18,000 s/mm^2. Acceleration in subsequent studies with ~100 diffusion encoding directions in half sphere of the q-space with b-values = 1000, 2000, 3000s/mm2) |
||
No |
No |
Yes |
203 diffusion encodings (isotropic 3D grid points in the q-space contained within a sphere with radius 3.6), maximum b-value=4000mm/s^2 |
||
No |
Yes |
Yes |
Fits any sampling scheme with at least one non-zero b-shell, benefits from more directions. Recommended 23 b-shells ranging from 0 to 4000 in a 258 direction grid-sampling scheme |
Yeh 2010 |
|
Yes |
Yes |
No |
At least 40 directions, b-value above 1000mm/s^2 |
||
Yes |
No |
No |
At least 64 directions, maximum b-values 3000-4000mm/s^2, multi-shell, isotropic voxel size |
||
No |
Yes |
No |
Multi-shell HARDI data (500, 1000, and 2000 s/mm^2; minimum 2 non-zero b-shells) or DSI (514 images in a cube of five lattice-units, one b=0) |
Merlet 2013, Özarslan 2009, Özarslan 2008 |
|
No |
Yes |
No |
Six unit sphere shells with b = 1000, 2000, 3000, 4000, 5000, 6000 s/mm^2 along 19, 32, 56, 87, 125, and 170 directions (see Olson 2019 for candidate sub-sampling schemes) |
||
No |
Yes |
No |
`Tom Dela Haije < https://doi.org/10.1016/j.neuroimage.2019.116405>`__ |
||
MAPL |
No |
Yes |
No |
Multi-shell similar to WU-Minn HCP, with minimum of 60 samples from 2 shells b-value 1000 and 3000s/mm^2 |
|
Yes |
No |
No |
Minimum: 20 gradient directions and a b-value of 1000 s/mm^2; benefits additionally from 60 direction HARDI data with b-value = 3000s/mm^2 or multi-shell |
Tournier 2017, Descoteaux 2008, Tournier 2007 |
|
No |
Yes |
No |
5 b=0, 50 directions at 3 non-zero b-shells: b=1000, b=2000, b=3000 |
||
No |
Yes |
No |
Multi-shell 64 direction b-values of 1000, 2000s/mm^2 as in Alexander 2017. Original model used 1480 s/mm^2 with 92 directions and 36 b=0 |
Anderson 2005, Alexander 2017 |
|
Yes |
Yes |
Yes |
HARDI data with 64 directions at b = 2500s/mm^2, 3 b=0 images (full original acquisition: 256 directions on a sphere at b = 2500s/mm^2, 36 b=0 volumes) |
||
No |
Yes |
No |
Evenly distributed geometric sampling scheme of 216 measurements, 5 b-values (50, 250, 50, 1000, 200mm/s^2), measurement tensors of four shapes: stick, prolate, sphere, and plane |
||
No |
Yes |
No |
At least one b=0, minimum of 39 acquisitions with spherical and linear encoding; optimal 120 (see Morez 2023), ideal 217 see Herberthson 2021 Table 1 |
||
Ball & Stick |
Yes |
Yes |
No |
Three b=0, 60 evenly distributed directions per Jones 1999 at b-value 1000mm/s^2 |
|
No |
Yes |
No |
Minimum 200 volumes of multi-spherical dMRI (multi-shell, multi-diffusion time; varying gradient directions, gradient strengths, and diffusion times) |
Fick 2017 |
|
Power Map |
Yes |
Yes |
No |
HARDI data with 60 directions at b-value = 3000 s/mm^2, 7 b=0 (Minimum: HARDI data with at least 30 directions) |
|
No |
Yes |
No |
72 directions at each of 5 evenly spaced b-values from 0.5 to 2.5 ms/μm2, 5 b-values from 3 to 5 ms/μm2, 5 b-values from 5.5 to 7.5 ms/μm2, and 3 b-values from 8 to 9 ms/μm2 / b=0 ms/μm^-2, and along 33 directions at b-values from 0.2–3 ms/μm^-2 in steps of 0.2 ms/μm^−2 (24 point spherical design and 9 directions identified for rapid kurtosis estimation) |
||
No |
Yes |
No |
Applying positivity constraints to Q-space Trajectory Imaging (QTI+)
Reconstruct with Diffusion Spectrum Imaging
Reconstruction of the diffusion signal with the correlation tensor model
Continuous and analytical diffusion signal modelling with 3D-SHORE
Reconstruct with Generalized Q-Sampling Imaging
Reconstruct with Constant Solid Angle (Q-Ball)
Reconstruction with the Sparse Fascicle Model
Calculate DSI-based scalar maps
Reconstruction of the diffusion signal with the kurtosis tensor model
Reconstruct with Q-space Trajectory Imaging (QTI)
K-fold cross-validation for model comparison
Reconstruction of the diffusion signal with the Tensor model
Crossing invariant fiber response function with FORECAST model
Local reconstruction using the Histological ResDNN
Reconstruction of the diffusion signal with the WMTI model
Using the RESTORE algorithm for robust tensor fitting
Signal Reconstruction Using Spherical Harmonics
Using the free water elimination model to remove DTI free water contamination
Reconstruction with Constrained Spherical Deconvolution
Reconstruction with Multi-Shell Multi-Tissue CSD
Continuous and analytical diffusion signal modelling with MAP-MRI
Mean signal diffusion kurtosis imaging (MSDKI)
Reconstruction with Robust and Unbiased Model-BAsed Spherical Deconvolution
Estimating diffusion time dependent q-space indices using qt-dMRI
Contextual Enhancement#
Fiber to bundle coherence measures
Crossing-preserving contextual enhancement
Fiber Tracking#
Surface seeding for tractography
An introduction to the Deterministic Maximum Direction Getter
Parallel Transport Tractography
Tracking with Robust Unbiased Model-BAsed Spherical Deconvolution (RUMBA-SD)
Bootstrap and Closest Peak Direction Getters Example
Tracking with the Sparse Fascicle Model
Introduction to Basic Tracking
An introduction to the Probabilistic Direction Getter
Particle Filtering Tractography
Linear fascicle evaluation (LiFE)
Using Various Stopping Criterion for Tractography
Streamlines Analysis and Connectivity#
BUAN Bundle Shape Similarity Score
BUAN Bundle Assignment Maps Creation
Extracting AFQ tract profiles from segmented bundles
Streamline length and size reduction
Calculation of Outliers with Cluster Confidence Index
Connectivity Matrices, ROI Intersections and Density Maps
Registration#
Symmetric Diffeomorphic Registration in 3D
Symmetric Diffeomorphic Registration in 2D
Diffeomorphic Registration with binary and fuzzy images
Nonrigid Bundle Registration with BundleWarp
Applying image-based deformations to streamlines
Affine Registration with Masks
Segmentation#
Brain segmentation with median_otsu
Tractography Clustering with QuickBundles
Tissue Classification of a T1-weighted Structural Image
Tractography Clustering - Available Metrics
Enhancing QuickBundles with different metrics and features
Tractography Clustering - Available Features
Automatic Fiber Bundle Extraction with RecoBundles
Simulation#
Multiprocessing#
Parallel reconstruction using Q-Ball
Parallel reconstruction using CSD
File Formats#
Visualization#
Visualization of ROI Surface Rendered with Streamlines
Visualize bundles and metrics on bundles
Advanced interactive visualization
Workflows#
Creating a new combined workflow