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Quantiphyse is a data viewer and analysis platform for volumetric medical imaging data

Project description

Quantiphyse

Viewer and data processing for 3D/4D medical imaging data

Overview

Quantiphyse provides tools for modelling and analysis of 3D/4D volumetric data, principally MRI data.

Core features:

  • Loading/Saving 3D/4D NIFTI files
  • Analysis tools including single/multiple voxel analysis and data comparison
  • Generic processing including smoothing, resampling, clustering

Features available via plugins

  • Registration, motion correction
  • Modelling tools for DCE, ASL, DSC and CEST MRI
  • Integration of selected FSL tools

See: http://quantiphyse.readthedocs.org/en/latest/ for full documentation.

License

Quantiphyse is available free under an academic (non-commercial) license. See the LICENSE file for full details, and contact OUI if interested in commercial licensing.

Installation

See https://quantiphyse.readthedocs.io/en/latest/basics/install.html for current installation instructions

Running from source code (for developers)

Running from source is recommended only if your are interested in developing the software further.

  1. Install the dependencies:

The list of Python dependencies is in requirements.txt

For example:

pip install -r requirements.txt
  1. Build extensions

python setup.py build_ext --inplace

  1. Run from source directory

python qp.py

Packaging

The scripts packaging/build.py is used to build a frozen distribution package in the form of a compressed archive (tar.gz or .zip) and a platform-dependent package (deb, msi or dpg). It should run autonomously, however you may need to input the sudo password on Linux in order to build a deb package.

The --snapshot option removes the version number from package filenames so you can provided them for download without having to change the link URLs.

The --maxi option builds a package which includes selected plugins, assuming these are downloaded

To Do list

Issue tracker

Current issues can be viewed on the GitHub issue tracker (https://github.com/physimals/quantiphyse/issues)

Roadmap

v0.6 (Released June 2018)

  • ASL tools first version (preprocess, model fit, calibration, multiphase)
  • Improved viewer (full resolution, aligned)

v0.8 (Target Mar 2019)

  • Integration of selected FSL tools (FLIRT, FAST, BET, FSL_ANAT?) [x]
  • Improved registration support (apply transform) [x]
  • Improved ASL tools based on oxasl (inc. ENABLE, VEASL, DEBLUR) [x]
  • Fabber T1 [x]
  • Fabber DCE [x]
  • DSC widget [x]
  • Improvements to ROI builder - working 'paint' tool [x]
  • Motion simulation [x]
  • Add noise [x]

v0.10 (Target 2020)

  • Stable interface for QpWidget, QpData, Process [ ]
  • Python 3 [x]
  • Support PySide and PySide2 - ideally the latter by default [x]
  • Improved manual data alignment tools [ ]
  • Multi-overlay view [x]
  • Perfusion simulator [x]

Migration to PySide2

  • Current version of Quantipihyse is targeted at Pyside2
  • Should still run under Pyside1 but not guaranteed
  • Currently using our own fork of pyqtgraph awaiting official release with Pyside2 support

Vague Plans for Future

  • MoCo/Registration

    • Bartek's MC method
  • 3D view

    • Probably not that useful but fun and may be easy(?) with vispy. Reliant on good refactoring of ImageView
    • Application to surfaces (Tom K?)
    • Use VisPy?
  • Add Jola's texture analysis which sounds cool, whatever it is

  • PK modelling validation

    • QIBA [x]
    • QIN
  • Simplify/rewrite generic Fabber interface

  • Improve memory usage by swapping out data which are not being displayed?

  • All widgets which process within ROI should work with the subimage within the bounding box of the ROI, not the whole image.

    • Supervoxels does this already with great performance improvement.
  • Support other file formats using NIBABEL.

    • DICOM conversion included where DCMSTACK is available
  • Add semiquantitative measures

    • Area under the curve
    • Enhancing fraction

Project details


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quantiphyse-0.9.9.post6.tar.gz (1.3 MB view hashes)

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