Dipy statistics

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Citations per year

Number of citations per year for the bioinformatics software tool Dipy
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Tool usage distribution map

This map represents all the scientific publications referring to Dipy per scientific context
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Associated diseases

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Popular tool citations

chevron_left 3D image analysis Image segmentation Image registration Image visualization Tractography Image registration Image visualization Morphometric analysis chevron_right
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Protocols

Dipy specifications

Information


Unique identifier OMICS_23382
Name Dipy
Alternative name Diffusion Imaging in Python
Software type Application/Script
Interface Command line interface
Restrictions to use None
Operating system Unix/Linux, Mac OS, Windows
Programming languages Python
Computer skills Advanced
Version 0.13.0
Stability Stable
Maintained Yes

Subtool


  • Quickbundles

Download


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Versioning


No version available

Maintainer


  • person_outline Ian Nimmo-Smith

Additional information


http://nipy.org/dipy/documentation.html

Publications for Diffusion Imaging in Python

Dipy citations

 (21)
call_split

Broca’s Area as a Pre articulatory Phonetic Encoder: Gating the Motor Program

2018
Front Hum Neurosci
PMCID: 5826965
PMID: 29520225
DOI: 10.3389/fnhum.2018.00064
call_split See protocol

[…] eighted sequence and a 3D-FLAIR sequence were also acquired for anatomical characterization.HARDI datasets were corrected for movement and eddy-current distortions using FMRIB Software Library (FSL). Diffusion Imaging in Python (Dipy) software was used to estimate fractional anisotropy (FA) and for q-ball residual-bootstrap fiber tracking of language pathways (, ). Tracking was performed using an […]

library_books

White Matter Tract Segmentation as Multiple Linear Assignment Problems

2018
Front Neurosci
PMCID: 5808221
PMID: 29467600
DOI: 10.3389/fnins.2017.00754

[…] volution algorithm (CSD) (Tournier et al., ) with a single shell (b = 1,000). The tractograms were obtained with the Euler Delta Crossing (EuDX) algorithm, see (Garyfallidis et al., ), implemented in DiPy, with 106 seeds. The number of streamlines of the resulting tractograms is approximately 100–140 thousands. […]

library_books

Bayesian Tractography Using Geometric Shape Priors

2017
Front Neurosci
PMCID: 5594407
PMID: 28936158
DOI: 10.3389/fnins.2017.00483

[…] ut here we have used only 30 subjects for our experiments. The dMRI images in HCP has an isotropic resolution of 1.25 mm. To estimate a diffusion tensor at each voxel, we use the open source software Dipy (Garyfallidis et al., ). Figure shows one slice of the 3 × 3 diffusion tensors estimated from a randomly selected dMRI image in HCP; a zoom-in of a small part of the image is shown on its right. […]

library_books

Mapping White Matter Microstructure in the One Month Human Brain

2017
Sci Rep
PMCID: 5575288
PMID: 28852074
DOI: 10.1038/s41598-017-09915-6

[…] nih.gov/pub/dist/doc/program_help/3dSkullStrip.html) and diffusion tensors were estimated for each voxel using the robust estimation of tensors by outlier rejection (RESTORE) algorithm as part of the diffusion imaging in python (DIPY) open source software package. As the largest b-value of the acquisition consisted of 1500 s/mm2, all three shells were utilized in fitting the diffusion tensors. Eig […]

library_books

Stability metrics for optic radiation tractography: Towards damage prediction after resective surgery

2017
J Neurosci Methods
PMCID: 5538260
PMID: 28648721
DOI: 10.1016/j.jneumeth.2017.05.029

[…] ons. However, unlike DTI, HARDI is not commonly applied within a medical MRI diagnosis. Instead, the DTI data may be improved by applying contextual enhancement (, ), such as the one available in the DIPY framework (http://dipy.org). Additionally, in order to improve the image quality of the diffusion measurements it may be beneficial to apply denoising. This may, for example, be achieved by a rec […]

library_books

Differences in brain morphology and working memory capacity across childhood

2017
PMCID: 5947821
PMID: 28748537
DOI: 10.1111/desc.12579

[…] re converted from the native DICOM to compressed NIfTI‐1 format using the dcm2nii tool (http://www.mccauslandcenter.sc.edu/mricro/mricron/dcm2nii.html). Subsequently, the images were submitted to the DiPy v0.8.0 implementation (Garyfallidis et al., ) of a non‐local means de‐noising algorithm (Coupe et al., ) to boost the signal‐to‐noise ratio. Next, a brain mask of the b0 image was created using t […]


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Dipy institution(s)
Wolfson College, University of Cambridge, Cambridge, UK; Medical Research Council Cognition and Brain Sciences Unit, Cambridge, UK; Henry H. Wheeler Jr. Brain Imaging Center, University of California, Berkeley, CA, USA; Wolfson Brain Imaging Centre, University of Cambridge, Cambridge, UK

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