Metadata-Version: 2.1
Name: brainweb
Version: 1.6.1
Summary: BrainWeb-based multimodal models of 20 normal brains
Home-page: https://github.com/casperdcl/brainweb
Author: Casper da Costa-Luis
Author-email: casper.dcl@physics.org
License: MPLv2.0
Keywords: pet-mr,volume-rendering,neuroimaging,fdg,mri
Platform: any
Classifier: Development Status :: 4 - Beta
Classifier: Framework :: IPython
Classifier: License :: OSI Approved :: Mozilla Public License 2.0 (MPL 2.0)
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 2
Classifier: Programming Language :: Python :: 3
Description-Content-Type: text/x-rst
Requires-Dist: tqdm (>=4.42.0)
Requires-Dist: requests
Requires-Dist: numpy
Requires-Dist: scikit-image
Provides-Extra: plot
Requires-Dist: matplotlib ; extra == 'plot'
Provides-Extra: register
Requires-Dist: dipy ; extra == 'register'
Provides-Extra: test
Requires-Dist: docutils ; extra == 'test'
Requires-Dist: pygments ; extra == 'test'
Requires-Dist: flake8 ; extra == 'test'

The following example may be launched interactively via any of the following:

- |Binder|
- `Local file <README.ipynb>`__
- `GitHub Preview <https://github.com/casperdcl/brainweb/blob/master/README.ipynb>`__

.. |Binder| image:: https://mybinder.org/badge_logo.svg
   :target: https://mybinder.org/v2/gh/casperdcl/brainweb/master?filepath=README.ipynb

BrainWeb-based multimodal models of 20 normal brains
====================================================

This project was initially inspired by "`BrainWeb: 20 Anatomical Models
of 20 Normal
Brains <http://brainweb.bic.mni.mcgill.ca/brainweb/anatomic_normal_20.html>`__."

However there are a number of generally useful tools, image processing &
display functions included in this project. For example, this includes
``volshow()`` for interactive comparison of multiple 3D volumes,
``get_file()`` for caching data URLs, and ``register()`` for image
coregistration.

|PyPI| |CI| |Quality| |DOI| |LICENCE|

**Download and Preprocessing for PET-MR Simulations**

This notebook will not re-download/re-process files if they already
exist.

-  Output data

   -  ``~/.brainweb/subject_*.npz``: dtype(shape):
      ``float32(127, 344, 344)``

-  `Raw data
   source <http://brainweb.bic.mni.mcgill.ca/brainweb/anatomic_normal_20.html>`__

   -  ``~/.brainweb/subject_*.bin.gz``: dtype(shape):
      ``uint16(362, 434, 362)``

-  Install

   -  ``pip install brainweb``

--------------

-  Author: Casper da Costa-Luis <casper.dcl@physics.org>
-  Date: 2017-2020
-  Licence: `MPLv2.0 <https://www.mozilla.org/MPL/2.0>`__

.. |PyPI| image:: https://img.shields.io/pypi/v/brainweb.svg
   :target: https://pypi.org/project/brainweb
.. |CI| image:: https://travis-ci.org/casperdcl/brainweb.svg?branch=master
   :target: https://travis-ci.org/casperdcl/brainweb
.. |Quality| image:: https://api.codacy.com/project/badge/Grade/cdad13693b0141199c31d5b44c7ab185
   :target: https://www.codacy.com/app/casper-dcl/brainweb
.. |DOI| image:: https://zenodo.org/badge/DOI/10.5281/zenodo.3269888.svg
   :target: https://doi.org/10.5281/zenodo.3269888
.. |LICENCE| image:: https://img.shields.io/pypi/l/brainweb.svg?label=licence
   :target: https://www.mozilla.org/MPL/2.0

.. code:: python

    from __future__ import print_function, division
    %matplotlib notebook
    import brainweb
    from brainweb import volshow
    import numpy as np
    from os import path
    from tqdm.auto import tqdm
    import logging
    logging.basicConfig(level=logging.INFO)

Raw Data
--------

.. code:: python

    # download
    files = brainweb.get_files()

    # read last file
    data = brainweb.load_file(files[-1])

    # show last subject
    print(files[-1])
    volshow(data, cmaps=['gist_ncar']);

::

    ~/.brainweb/subject_54.bin.gz

.. image:: https://raw.githubusercontent.com/casperdcl/brainweb/master/raw.png

Transform
---------

Convert raw image data:

-  Siemens Biograph mMR resolution (~2mm) & dimensions (127, 344, 344)
-  PET/T1/T2/uMap intensities

   -  PET defaults to FDG intensity ratios; could use e.g. Amyloid instead

-  randomised structure for PET/T1/T2

   -  t (1 + g [2 G_sigma(r) - 1]), where

      -  r = rand(127, 344, 344) in [0, 1),
      -  Gaussian smoothing sigma = 1,
      -  g = 1 for PET; 0.75 for MR, and
      -  t = the PET or MR piecewise constant phantom

.. code:: python

    # show region probability masks
    PetClass = brainweb.FDG
    label_probs = brainweb.get_label_probabilities(files[-1], labels=PetClass.all_labels)
    volshow(label_probs[brainweb.trim_zeros_ROI(label_probs)], titles=PetClass.all_labels, frameon=False);

.. image:: https://raw.githubusercontent.com/casperdcl/brainweb/master/pmasks.png

.. code:: python

    brainweb.seed(1337)

    for f in tqdm(files, desc="mMR ground truths", unit="subject"):
        vol = brainweb.get_mmr_fromfile(
            f,
            petNoise=1, t1Noise=0.75, t2Noise=0.75,
            petSigma=1, t1Sigma=1, t2Sigma=1,
            PetClass=PetClass)

.. code:: python

    # show last subject
    print(f)
    volshow([vol['PET' ][:, 100:-100, 100:-100],
             vol['uMap'][:, 100:-100, 100:-100],
             vol['T1'  ][:, 100:-100, 100:-100],
             vol['T2'  ][:, 100:-100, 100:-100]],
            cmaps=['hot', 'bone', 'Greys_r', 'Greys_r'],
            titles=["PET", "uMap", "T1", "T2"],
            frameon=False);

::

    ~/.brainweb/subject_54.bin.gz

.. image:: https://raw.githubusercontent.com/casperdcl/brainweb/master/mMR.png

.. code:: python

    # add some lesions
    brainweb.seed(1337)
    im3d = brainweb.add_lesions(vol['PET'])
    volshow(im3d[:, 100:-100, 100:-100], cmaps=['hot']);

.. image:: https://raw.githubusercontent.com/casperdcl/brainweb/master/lesions.png

.. code:: python

    # bonus: use brute-force registration to transform
    #!pip install -U 'brainweb[register]'
    reg = brainweb.register(
        data[:, ::-1], target=vol['PET'],
        src_resolution=brainweb.Res.brainweb,
        target_resolution=brainweb.Res.mMR)

    volshow({
        "PET":    vol['PET'][:, 100:-100, 100:-100],
        "RawReg": reg[       :, 100:-100, 100:-100],
        "T1":     vol['T1' ][:, 100:-100, 100:-100],
    }, cmaps=['hot', 'gist_ncar', 'Greys_r'], ncols=3, tight_layout=5, figsize=(9.5, 3.5), frameon=False);

.. image:: https://raw.githubusercontent.com/casperdcl/brainweb/master/reg.png


