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)
Raw data
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"description": "Installation\n============\n\n.. code-block:: bash\n\n python -m pip install phantominator\n\nThe goal is to have easy installation and usage for everyone. If\nsomething doesn't work, please open an issue and/or submit a pull\nrequest. We'll get it figured out.\n\n`pygrappa` is an optional dependency required to run the\n`phantominator.examples.radial_coil_sens` example.\n\nAbout\n=====\n\nPython package for easy generation of numerical phantoms. I often\nneed a simple image to try something out on. In MATLAB, I would use\nthe `phantom` command to quickly get something to work with. In\nPython, it's not always quite so easy, so I made this package that's quick\nto install and use so there's as little friction as possible. There\nare other implementations of Shepp-Logan available from other\nprojects, but they are usually not as easy to install or include other\nthings that I don't want for this project.\n\nThis package offers both CT and MR versions.\n\nGoing forward, this module will support Python >= 3.8.\n\nUsage\n=====\n\nAlso see the `examples` module and docstrings. The interface for CT\nphantom generation is similar to MATLAB's `phantom` function.\n\nExamples can be run as:\n\n.. code-block:: bash\n\n # python -m phantominator.examples.[example-name], e.g.:\n python -m phantominator.examples.shepp_logan\n\nBasic usage:\n\n.. code-block:: python\n\n # CT phantom\n from phantominator import shepp_logan\n ph = shepp_logan(128)\n\n # MR phantom (returns proton density, T1, and T2 maps)\n M0, T1, T2 = shepp_logan((128, 128, 20), MR=True)\n\nThe Shepp-Logan 3D phantom has ellipsoids in [-1, 1] along the z-axis.\nThe 2D Shepp-Logan exists at z=-0.25, so if we want just a subset\nalong the z-axis with the first slice being the traditional 2D\nphantom, we can use the `zlims` option:\n\n.. code-block:: python\n\n from phantominator import shepp_logan\n M0, T1, T2 = shepp_logan((64, 64, 5), MR=True, zlims=(-.25, .25))\n\nWe can also generate simple oscillating concentric circles:\n\n.. code-block:: python\n\n # Dynamic (concentric circles), 20 time frames\n from phantominator import dynamic\n ph = dynamic(128, 20)\n\nIf we want to modify ellipse/ellipsoid parameters or we just want to\nsee what they are. For example, we can get the MR ellipsoid\nparameters like this:\n\n.. code-block:: python\n\n from phantominator import mr_ellipsoid_parameters\n E = mr_ellipsoid_parameters()\n\nSee docstrings for `ct_shepp_logan`, and `mr_shepp_logan` for how\nthe array `E` is structured. It follows conventions from MATLAB's\nphantom function.\n\nArbitrary k-space sampling is supported for the single coil 2D\nShepp-Logan phantom:\n\n.. code-block:: python\n\n # Given k-space coordinates (kx, ky), where kx and ky are 1D\n # arrays using the same unit conventions as BART's traj command,\n # we can find the corresponding k-space measurements:\n from phantominator import kspace_shepp_logan\n k = kspace_shepp_logan(kx, ky)\n",
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