https://github.com/mckib2/phantominator

Generate numerical phantoms.
https://github.com/mckib2/phantominator

image-reconstruction mri phantom shepp-logan

Last synced: 3 months ago
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Generate numerical phantoms.

• Host: GitHub
• URL: https://github.com/mckib2/phantominator
• Owner: mckib2
• License: mit
• Created: 2019-06-21T22:55:34.000Z (over 5 years ago)
• Default Branch: main
• Last Pushed: 2023-04-16T22:21:30.000Z (almost 2 years ago)
• Last Synced: 2024-10-04T23:21:06.146Z (4 months ago)
• Topics: image-reconstruction, mri, phantom, shepp-logan
• Language: Python
• Size: 80.1 KB
• Stars: 34
• Watchers: 2
• Forks: 2
• Open Issues: 0
• Metadata Files:
  • Readme: README.rst
  • License: LICENSE

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README

        
Installation

============

.. code-block:: bash

    python -m pip install phantominator

The goal is to have easy installation and usage for everyone.  If

something doesn't work, please open an issue and/or submit a pull

request.  We'll get it figured out.

`pygrappa` is an optional dependency required to run the

`phantominator.examples.radial_coil_sens` example.

About

=====

Python package for easy generation of numerical phantoms.  I often

need a simple image to try something out on.  In MATLAB, I would use

the `phantom` command to quickly get something to work with.  In

Python, it's not always quite so easy, so I made this package that's quick

to install and use so there's as little friction as possible.  There

are other implementations of Shepp-Logan available from other

projects, but they are usually not as easy to install or include other

things that I don't want for this project.

This package offers both CT and MR versions.

Going forward, this module will support Python >= 3.8.

Usage

=====

Also see the `examples` module and docstrings.  The interface for CT

phantom generation is similar to MATLAB's `phantom` function.

Examples can be run as:

.. code-block:: bash

    # python -m phantominator.examples.[example-name], e.g.:

    python -m phantominator.examples.shepp_logan

Basic usage:

.. code-block:: python

    # CT phantom

    from phantominator import shepp_logan

    ph = shepp_logan(128)

    # MR phantom (returns proton density, T1, and T2 maps)

    M0, T1, T2 = shepp_logan((128, 128, 20), MR=True)

The Shepp-Logan 3D phantom has ellipsoids in [-1, 1] along the z-axis.

The 2D Shepp-Logan exists at z=-0.25, so if we want just a subset

along the z-axis with the first slice being the traditional 2D

phantom, we can use the `zlims` option:

.. code-block:: python

    from phantominator import shepp_logan

    M0, T1, T2 = shepp_logan((64, 64, 5), MR=True, zlims=(-.25, .25))

We can also generate simple oscillating concentric circles:

.. code-block:: python

    # Dynamic (concentric circles), 20 time frames

    from phantominator import dynamic

    ph = dynamic(128, 20)

If we want to modify ellipse/ellipsoid parameters or we just want to

see what they are.  For example, we can get the MR ellipsoid

parameters like this:

.. code-block:: python

    from phantominator import mr_ellipsoid_parameters

    E = mr_ellipsoid_parameters()

See docstrings for `ct_shepp_logan`, and `mr_shepp_logan` for how

the array `E` is structured.  It follows conventions from MATLAB's

phantom function.

Arbitrary k-space sampling is supported for the single coil 2D

Shepp-Logan phantom:

.. code-block:: python

    # Given k-space coordinates (kx, ky), where kx and ky are 1D

    # arrays using the same unit conventions as BART's traj command,

    # we can find the corresponding k-space measurements:

    from phantominator import kspace_shepp_logan

    k = kspace_shepp_logan(kx, ky)