Metadata-Version: 2.1
Name: pyqmri
Version: 1.0.0
Summary: Model-based parameter quantification using OpenCL and Python
Home-page: https://github.com/IMTtugraz/PyQMRI
Author: Oliver Maier
Author-email: oliver.maier@tugraz.at
License: Apache-2.0
Download-URL: https://github.com/IMTtugraz/PyQMRI/archive/1.0.0.tar.gz
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        PyQMRI - Model-Based Parameter Quantification
        =============================================
        PyQMRI is a Python based toolbox for quantitative Magnetic Resonance Imaging (MRI). Utilizing _PyOpenCL and a double-buffering scheme, 
        it enables the accelerated reconsruction and fitting of arbitrary large datasets on memory limited GPUs.
        Currently, PyQMRI supports the processing of 3D Cartesian and non-Cartesian (stack-of-) data.
        
        Examples include T1 quantification from variable flip angle or 
        inversion-recovery Look-Locker data, T2 quantification using a 
        mono-exponential fit, or Diffusion Tensor quantification. 
        
        In addition, a Genereal Model exists that can be invoced 
        using a text file containing the analytical signal equation.
        
        For a real world usage example have a look at the `Quickstart Guide`_.
        The example can also be run interactively using |Colab|.
        
        Installation and usage guides, as well as API documentaiton, can be found in the Documentation_
        
        
        Sample Data
        -----------
        In-vivo datasets used in the original publication (doi: `[10.1002/mrm.27502]`_) can be found at zenodo_. If you use the sample data with the recent release of PyQMRI please delete the "Coils"
        entry in the .h5 to force a recomputation of the receive coil sensitivities as the orientation does not match the data.
        
        
        Contributing
        ------------
        Development and code contributions should be done at our GitLab_ site to facilitate the CI integration and GPU availability there.
        If you want to contribute please make sure that all tests pass and adhere to our `Code of Conduct`_. 
        Prior to running the tests it is necessary to start an ipcluster. 
        An exemplary workflow would be:
        :bash:`ipcluster start &`
        followed by typing
        :bash:`pytest test`
        in the PyQMRI root folder. It is advised to run unit and integration tests after each other as OUT_OF_MEMORY exceptions can occur if both are in one session, e.g.:
        :bash:`pytest test/unittests`
        :bash:`pytest test/integrationtests`
        
        For more detailed instructions on how to contribute have a look at contributing_.
        
        
        Limitations and known Issues:
        ------------------------------
        Currently runs only on GPUs due to having only basic CPU support for the clfft_.
        
        Citation:
        ----------
        Please cite "Oliver Maier, Matthias Schloegl, Kristian Bredies, and Rudolf Stollberger; 3D Model-Based Parameter Quantification on Resource Constrained Hardware using Double-Buffering. Proceedings of the 27th meeting of the ISMRM, 2019, Montreal, Canada" if using the software or parts of it, specifically the PyOpenCL based NUFFT, in your work.
        
        Older Releases:
        ----------------
        You can find the code for 
        
        | Maier O, Schoormans J,Schloegl M, Strijkers GJ, Lesch A, Benkert T, Block T, Coolen BF, Bredies K, Stollberger R 
        | **Rapid T1 quantification from high resolution 3D data with model‐based reconstruction.**
        | *Magn Reson Med.*, 2018; 00:1–16 doi: `[10.1002/mrm.27502]`_
        
        at `[v0.1.0] <https://github.com/IMTtugraz/PyQMRI/tree/v.0.1.0>`_
        
        .. _OpenCL: https://www.khronos.org/opencl/
        .. _clfft: https://github.com/clMathLibraries/clFFT
        .. _gpyfft: https://github.com/geggo/gpyfft
        .. _clinfo: https://github.com/Oblomov/clinfo
        .. _`[10.1002/mrm.27502]`: http://onlinelibrary.wiley.com/doi/10.1002/mrm.27502/full
        .. _zenodo: https://doi.org/10.5281/zenodo.1410918
        .. _NLINV: https://doi.org/10.1002/mrm.21691
        .. _PyOpenCL: https://github.com/inducer/pyopencl
        .. _GoogleColab: https://colab.research.google.com/drive/19BfSJmDPinZDY0m1sMAhETutIiJG3b33?usp=sharing
        .. _contributing: CONTRIBUTING.rst
        .. _`Quickstart Guide` : https://pyqmri.readthedocs.io/en/latest/quickstart.html
        .. _Documentation : https://pyqmri.readthedocs.io/en/latest/?badge=latest
        .. _`Code of Conduct` : CODE_OF_CONDUCT.rst
        .. _GitLab : https://gitlab.tugraz.at/F23B736137140D66/PyQMRI
        
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Operating System :: POSIX :: Linux
Requires-Python: >=3.6
Description-Content-Type: text/x-rst
