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Site Navigation

    • Overview
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    • Latest

    • DIPY Workshop 2024

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    • DIPY Workshop 2023
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    • DIPY Workshop 2019

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Section Navigation

  • Quick Start
    • Getting started with DIPY
    • Introduction to Basic Tracking
  • Preprocessing
    • Reslice diffusion datasets
    • Between-volumes Motion Correction on DWI datasets
    • Noise estimation using PIESNO
    • 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
    • Gradients and Spheres
    • Denoise images using Adaptive Soft Coefficient Matching (ASCM)
    • SNR estimation for Diffusion-Weighted Images
    • Denoise images using the Marcenko-Pastur PCA algorithm
    • Suppress Gibbs oscillations
  • Reconstruction
    • Applying positivity constraints to Q-space Trajectory Imaging (QTI+)
    • Reconstruct with Diffusion Spectrum Imaging (DSI)
    • Reconstruction of the diffusion signal with the correlation tensor model (CTI)
    • DSI Deconvolution (DSID) vs DSI
    • Calculate SHORE scalar maps
    • Reconstruct with Generalized Q-Sampling Imaging
    • Continuous and analytical diffusion signal modelling with 3D-SHORE
    • Reconstruct with Constant Solid Angle (Q-Ball)
    • Reconstruction with the Sparse Fascicle Model (SFM)
    • Calculate DSI-based scalar maps
    • Reconstruction of the diffusion signal with the kurtosis tensor model (DKI)
    • Reconstruct with Q-space Trajectory Imaging (QTI)
    • Reconstruction of the diffusion signal with DTI (single tensor) model
    • K-fold cross-validation for model comparison
    • Crossing invariant fiber response function with FORECAST model
    • Local reconstruction using the Histological ResDNN
    • Reconstruction of the diffusion signal with the WMTI model (DKI-MICRO)
    • Using the RESTORE algorithm for robust tensor fitting
    • Using the free water elimination model to remove DTI free water contamination
    • Signal Reconstruction Using Spherical Harmonics
    • Reconstruction with Multi-Shell Multi-Tissue CSD
    • Continuous and analytical diffusion signal modelling with MAP-MRI
    • Reconstruction with Constrained Spherical Deconvolution model (CSD)
    • Intravoxel incoherent motion (IVIM)
    • Reconstruction of Bingham Functions from ODFs
    • Mean signal diffusion kurtosis imaging (MSDKI)
    • Reconstruction with Robust and Unbiased Model-BAsed Spherical Deconvolution (RUMBA)
    • Estimating diffusion time dependent q-space indices using qt-dMRI
  • Contextual Enhancement
    • Crossing-preserving contextual enhancement
    • Fiber to bundle coherence measures
  • Fiber Tracking
    • Surface seeding for tractography
    • An introduction to the Deterministic Maximum Direction Getter
    • Parallel Transport Tractography
    • Bootstrap and Closest Peak Direction Getters Example
    • Tracking with the Sparse Fascicle Model
    • Tracking with Robust Unbiased Model-BAsed Spherical Deconvolution (RUMBA-SD)
    • Introduction to Basic Tracking
    • Particle Filtering Tractography
    • An introduction to the Probabilistic Direction Getter
    • 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
    • Calculation of Outliers with Cluster Confidence Index
    • Streamline length and size reduction
    • Calculate Path Length Map
    • Connectivity Matrices, ROI Intersections and Density Maps
  • Registration
    • Groupwise Bundle Registration
    • Direct Bundle Registration
    • Diffeomorphic Registration with binary and fuzzy images
    • Symmetric Diffeomorphic Registration in 3D
    • Symmetric Diffeomorphic Registration in 2D
    • Nonrigid Bundle Registration with BundleWarp
    • Applying image-based deformations to streamlines
    • Affine Registration with Masks
    • Affine Registration in 3D
  • Segmentation
    • Brain segmentation with median_otsu
    • Tractography Clustering with QuickBundles
    • Tissue Classification of a T1-weighted Structural Image
    • Tractography Clustering - Available Metrics
    • Fast Streamline Search
    • Enhancing QuickBundles with different metrics and features
    • Tractography Clustering - Available Features
    • Automatic Fiber Bundle Extraction with RecoBundles
  • Simulation
    • DSI Deconvolution (DSID) vs DSI
    • DKI MultiTensor Simulation
    • MultiTensor Simulation
  • Multiprocessing
    • Parallel reconstruction using Q-Ball
    • Parallel reconstruction using CSD
  • File Formats
    • Read/Write streamline files
  • Visualization
    • Visualization of ROI Surface Rendered with Streamlines
    • Visualize bundles and metrics on bundles
    • Simple volume slicing
    • Advanced interactive visualization
  • Workflows
    • Creating a new workflow.
    • Creating a new combined workflow
  • Examples
  • DIPY

Multiprocessing#

Parallel reconstruction using Q-Ball

Parallel reconstruction using Q-Ball

Parallel reconstruction using CSD

Parallel reconstruction using CSD

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MultiTensor Simulation

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Parallel reconstruction using Q-Ball

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  • The department of Intelligent Systems Engineering of Indiana University
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  • The Gordon and Betty Moore Foundation and the Alfred P. Sloan Foundation, through the University of Washington eScience Institute Data Science Environment
  • Google supported DIPY through the Google Summer of Code Program (2015-2024)
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