{ "info": { "author": "Riccardo Metere", "author_email": "rick@metere.it", "bugtrack_url": null, "classifiers": [ "Development Status :: 3 - Alpha", "Environment :: Console", "Environment :: Console :: Curses", "Environment :: Win32 (MS Windows)", "Environment :: X11 Applications", "Intended Audience :: Healthcare Industry", "Intended Audience :: Science/Research", "Intended Audience :: System Administrators", "License :: OSI Approved :: GNU General Public License v3 or later (GPLv3+)", "Natural Language :: English", "Operating System :: OS Independent", "Operating System :: POSIX", "Programming Language :: Python", "Programming Language :: Python :: 2", "Programming Language :: Python :: 3", "Topic :: Scientific/Engineering", "Topic :: Scientific/Engineering :: Medical Science Apps.", "Topic :: System :: Archiving", "Topic :: System :: Monitoring", "Topic :: System :: Systems Administration", "Topic :: Utilities" ], "description": "=====================================\nDCMPI - DICOM Preprocessing Interface\n=====================================\n\n**DCMPI** - DICOM Preprocessing Interface:\nexplore/distill DICOM data.\n\nThis software implements an automated information extraction tool from DICOM\nfiles containing MRI acquisitions. It is specifically targeted at the output\nof the Siemens scanners, although it should be easily adaptable to other\nvendors and eventually even different acquisition modalities.\n\nIt features a Graphical User-Interface to manage the most used export options.\nThis is also mirrored by a CLI of the same software.\n\nAdditionally, several other command-line tools are provided, which offer a\nmuch finer control over the single tasks.\n\nThe available tasks are:\n\n- DICOM copy/moving and sorting according to acquisition\n (this includes a JSON summary of the acquisitions and series)\n- extraction of DICOM metadata and experimental storing as JSON\n- extraction of acquisition protocol information\n- extraction of human-friendly information from the DICOM, stored as JSON\n- extraction of the image data and conversion to the popular NIfTI format\n (this is supported through the software\n `dcm2nii `_,\n with partial support for other programs, e.g.\n `isis `_)\n- creation of HTML reports from the DICOM information\n (PDF conversion is supported via `wkhtmltopdf `_)\n- backup of the DICOM files as compressed archives\n- monitor of a folder to detect appearance of new DICOM files\n\nThe subsequent data analysis is probably best performed using\n`PyMRT `_, which natively understands the\noutput of DCMPI.\n\nNotes\n-----\nThis work is based on reverse engineering of the DICOM files and as such should\nnot be used as a reliable source of information.\nSome of the features are only partially supported.\nPlease get in contact if you would like to suggest additional features.\n\nWARNING\n-------\nThis is a research tool and it is provided 'as is'.\nDO **NOT** BASE ANY DIAGNOSIS ON THE INFORMATION PROCESSED BY THIS SOFTWARE!\n\nInstallation\n------------\nThe recommended way of installing the software is through\n`PyPI `_:\n\n.. code:: shell\n\n $ pip install dcmpi\n\nAlternatively, you can the clone the source repository from\n`Bitbucket `_:\n\n.. code:: shell\n\n $ mkdir dcmpi\n $ cd dcmpi\n $ git clone git@bitbucket.org:norok2/dcmpi.git\n $ python setup.py install\n\nFor more details see also ``INSTALL.rst``.\n\nLicense\n-------\nThis work is licensed through the terms and conditions of the\n`GPLv3+ `_\nThe use of this software for scientific purpose leading to a publication\nshould be acknowledged through citation of the upcoming paper.\n\nAcknowledgements\n----------------\nThis software originated as part of my Ph.D. work at the\n`Max Planck Institute for Human Cognitive and Brain Sciences\n`_ and the `University of Leipzig\n`_.\n\nFor a complete list of authors please see ``AUTHORS.rst``.\n\nPeople who have influenced this work are acknowledged in ``THANKS.rst``.\n\nThis work was partly funded by the European Union\nthrough the Seventh Framework Programme Marie Curie Actions\nvia the \"Ultra-High Field Magnetic Resonance Imaging: HiMR\"\nInitial Training Network (FP7-PEOPLE-2012-ITN-316716).\n\n\n", "description_content_type": "", "docs_url": 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