Metadata-Version: 2.1
Name: aslprep
Version: 0.2.7
Summary: ASLPREP is a robust and easy-to-use pipeline for preprocessing of diverse ASL data.
Home-page: https://github.com/pennlinc/aslprep
Author: Azeez Adebimpe
Author-email: azeez.adebimpe@outlook.com
License: 3-clause BSD
Project-URL: Documentation, https://aslprep.readthedocs.io/
Project-URL: Docker Images, https://hub.docker.com/r/pennlinc/aslprep/tags/
Description: Preprocessing of arterial spin labeling (ASL)  involves numerous steps to clean and standardize
        the data before statistical analysis.
        Generally, researchers create ad hoc preprocessing workflows for each dataset,
        building upon a large inventory of available tools.
        The complexity of these workflows has snowballed with rapid advances in
        acquisition and processing.
        ASLPrep is an analysis-agnostic tool that addresses the challenge of robust and
        reproducible preprocessing for task-based and resting ASL data.
        ASLPrep automatically adapts a best-in-breed workflow to the idiosyncrasies of
        virtually any dataset, ensuring high-quality preprocessing without manual intervention.
        ASLPrep robustly produces high-quality results on diverse ASL data.
        Additionally, ASLPrep introduces less uncontrolled spatial smoothness than observed
        with commonly used preprocxessing tools.
        ASLPrep equips neuroscientists with an easy-to-use and transparent preprocessing
        workflow, which can help ensure the validity of inference and the interpretability
        of results.
        
        The workflow is based on `Nipype <https://nipype.readthedocs.io>`_ and encompases a large
        set of tools from well-known neuroimaging packages, including
        `FSL <https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/>`_,
        `ANTs <https://stnava.github.io/ANTs/>`_,
        `FreeSurfer <https://surfer.nmr.mgh.harvard.edu/>`_,
        `AFNI <https://afni.nimh.nih.gov/>`_,
        and `Nilearn <https://nilearn.github.io/>`_.
        This pipeline was designed to provide the best software implementation for each state of
        preprocessing, and will be updated as newer and better neuroimaging software becomes
        available.
        
        ASLPrep performs basic preprocessing steps (coregistration, normalization, unwarping,segmentation, 
        skullstripping  and computation of  cerebral blood flow (CBF)) providing outputs that can be
        easily submitted to a variety of group level analyses, including task-based or resting-state
        CBF, graph theory measures, surface or volume-based statistics, etc.
        ASLPrep allows you to easily do the following:
        
          * Take ASL data from *unprocessed* (only reconstructed) to ready for analysis.
          * Compute Cerebral Blood Flow(CBF), denoising and partial volume correction
          * Implement tools from different software packages.
          * Achieve optimal data processing quality by using the best tools available.
          * Generate preprocessing-assessment reports, with which the user can easily identify problems.
          * Receive verbose output concerning the stage of preprocessing for each subject, including
            meaningful errors.
          * Automate and parallelize processing steps, which provides a significant speed-up from
            typical linear, manual processing.
        
        [Documentation  `aslprep.org <https://aslprep.readthedocs.io>`_ ]
        
        
        
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Science/Research
Classifier: Topic :: Scientific/Engineering :: Image Recognition
Classifier: License :: OSI Approved :: BSD License
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Requires-Python: >=3.7
Description-Content-Type: text/x-rst; charset=UTF-8
Provides-Extra: datalad
Provides-Extra: doc
Provides-Extra: docs
Provides-Extra: duecredit
Provides-Extra: resmon
Provides-Extra: sentry
Provides-Extra: tests
Provides-Extra: all
