Dipy is a diffusion MR imaging library written in Python
'Close gh-' statements refer to GitHub issues that are available at:
http://github.com/nipy/dipy/issues
The full VCS changelog is available here:
http://github.com/nipy/dipy/commits/master
The code found in Dipy was created by the people found in the AUTHOR file.
- 0.14 (Tuesday, 1 May 2018)
- RecoBundles: anatomically relevant segmentation of bundles
- New super fast clustering algorithm: QuickBundlesX
- New tracking algorithm: Particle Filtering Tracking.
- New tracking algorithm: Probabilistic Residual Bootstrap Tracking.
- Integration of the Streamlines API for reading, saving and processing tractograms.
- Fiber ORientation Estimated using Continuous Axially Symmetric Tensors (Forecast).
- New command line interfaces.
- Deprecated fvtk (old visualization framework).
- A range of new visualization improvements.
- Large documentation update.
- 0.13 (Monday, 24 October 2017)
- Faster local PCA implementation.
- Fixed different issues with OpenMP and Windows / OSX.
- Replacement of cvxopt by cvxpy.
- Replacement of Pytables by h5py.
- Updated API to support latest numpy version (1.14).
- New user interfaces for visualization.
- Large documentation update.
- 0.12 (Tuesday, 26 June 2017)
- IVIM Simultaneous modeling of perfusion and diffusion.
- MAPL, tissue microstructure estimation using Laplacian-regularized MAP-MRI.
- DKI-based microstructural modelling.
- Free water diffusion tensor imaging.
- Denoising using Local PCA.
- Streamline-based registration (SLR).
- Fiber to bundle coherence (FBC) measures.
- Bayesian MRF-based tissue classification.
- New API for integrated user interfaces.
- New hdf5 file (.pam5) for saving reconstruction results.
- Interactive slicing of images, ODFS and peaks.
- Updated API to support latest numpy versions.
- New system for automatically generating command line interfaces.
- Faster computation of Cross Correlation metric for registration.
- 0.11 (Sunday, 21 February 2016)
- New framework for contextual enhancement of ODFs.
- Compatibility with numpy (1.11).
- Compatibility with VTK 7.0 which supports Python 3.x.
- Faster PIESNO for noise estimation.
- Reorient gradient directions according to motion correction parameters.
- Supporting Python 3.3+ but not 3.2.
- Reduced memory usage in DTI.
- DSI now can use datasets with multiple b0s.
- Fixed different issues with Windows 64bit and Python 3.5.
- 0.10 (Thursday, 2 December 2015)
- Compatibility with new versions of scipy (0.16) and numpy (1.10).
- New cleaner visualization API, including compatibility with VTK 6, and functions to create your own interactive visualizations.
- Diffusion Kurtosis Imaging(DKI): Google Summer of Code work by Rafael Henriques.
- Mean Apparent Propagator (MAP) MRI for tissue microstructure estimation.
- Anisotropic Power Maps from spherical harmonic coefficients.
- New framework for affine registration of images.
- 0.9.2 (Wednesday, 18 March 2015)
- Anatomically Constrained Tissue Classifiers for Tracking
- Massive speedup of Constrained Spherical Deconvolution (CSD)
- Recursive calibration of response function for CSD
- New experimental framework for clustering
- Improvements and 10X speedup for Quickbundles
- Improvements in Linear Fascicle Evaluation (LiFE)
- New implementation of Geodesic Anisotropy
- New efficient transformation functions for registration
- Sparse Fascicle Model supports acquisitions with multiple b-values
- 0.8.0 (Tuesday, 6 Jan 2015)
- Nonlinear Image-based Registration (SyN)
- Streamline-based Linear Registration (SLR)
- Linear Fascicle Evaluation (LiFE)
- Cross-validation for reconstruction models
- Sparse Fascicle Model (SFM)
- Non-local means denoising (NLMEANS)
- New modular tracking machinery
- Closed 388 issues and merged 155 pull requests
- A variety of bug-fixes and speed improvements
- 0.7.1 (Thursday, 16 Jan 2014)
- Made installing Dipy easier and more universal
- Fixed automated seeding problems for tracking
- Removed default parameter for odf_vertices in EuDX
- 0.7.0 (Monday, 23 Dec 2013)
- Constrained Spherical Deconvolution (CSD)
- Simple Harmonic Oscillator based Reconstruction and Estimation (SHORE)
- Sharpening Deconvolution Transform (SDT)
- Signal-to-noise ratio estimation
- Parallel processing enabled for all reconstruction models using peaks_from_model
- Simultaneous peak and ODF visualization
- Streamtube visualization
- Electrostatic repulsion for sphere generation
- Connectivity matrices and density maps
- Streamline filtering through specific ROIs using target
- Brain extraction and foreground extraction using median_otsu
- RESTORE fitting for DTI
- Westin's Tensor maps
- Access to more publicly available datasets directly through Dipy functions.
- 3x more tutorials than previous release
- 0.6.0 (Sunday, 24 Mar 2013)
- Cython 0.17+ enforced
- New reconstruction models API
- Diffusion Spectrum Imaging (DSI)
- DSI with deconvolution
- Generalized Q-sampling Imaging 2 (GQI2)
- Modular fiber tracking * deterministic * probabilistic
- Fast volume indexing (a faster ndindex)
- Spherical Harmonic Models * Opdt (Tristan-Vega et. al) * CSA odf (Aganj et. al) * Analytical Q-ball (Descoteaux et. al) * Tuch's Q-ball (Tuch et. al)
- Visualization of spherical functions
- Peak finding in odfs
- Non-linear peak finding
- Sphere Object
- Gradients Object
- 2D sphere plotting
- MultiTensor and Ball & Sticks voxel simulations
- Fetch/Download data for examples
- Software phantom generation
- Angular similarity for comparisons between multiple peaks
- SingleVoxelModel to MultiVoxelModel decorator
- Mrtrix and fibernavigator SH bases
- More Benchmarks
- More Tests
- Color FA and other Tensor metrics added
- Scripts for the ISBI 2013 competition
- Fit_tensor script added
- Radial basis function interpolation on the sphere
- New examples/tutorials
- 0.5.0 (Friday, 11 Feb 2011)
- Initial release.
- Reconstruction algorithms e.g. GQI, DTI
- Tractography generation algorithms e.g. EuDX
- Intelligent downsampling of tracks
- Ultra fast tractography clustering
- Resampling datasets with anisotropic voxels to isotropic
- Visualizing multiple brains simultaneously
- Finding track correspondence between different brains
- Reading many different file formats e.g. Trackvis or Nifti
- Dealing with huge tractographies without memory restrictions
- Playing with datasets interactively without storing
- And much more and even more to come in next releases
- Initial release.