We are very happy to announce another major release of Diffusion Imaging in Python (Dipy).
0.7.1 (Thursday, 16 Jan 2014)
Reconstruction
* Constrained Spherical Deconvolution (CSD).
* Simple Harmonic Oscillator based Reconstruction and Estimation (SHORE).
* Sharpening Deconvolution Transform (SDT).
* Signal-to-noise ratio estimation.
* RESTORE fitting for DTI.
* Westin's Tensor maps.
Tracking
* Enabled automated seeding in masks.
* Streamline filtering through specific ROIs using `target`.
Segmentation
* Brain and foreground extraction using median_otsu.
Visualization
* Streamtube visualization.
* Simultaneous peaks and ODF visualization.
Connectivity
* Connectivity matrices and density maps.
Parallel processing
* Parallel processing is possible for all reconstruction models using `peaks_from_model`.
Data access
* Access to more publicly available datasets directly through Dipy functions.
Installation
* Installing Dipy is now easier and more universal.
* Available with pip, easy_install, neurodebian and other methods.
Overall
* 3x more tutorials than previous release.
* 2x more contributors from the previous release.