petprep 0.0.1a0

Preprocessing of positron emission tomography (PET) 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. PETPrep is an analysis-agnostic tool that addresses the challenge of robust and reproducible preprocessing for PET data. PETPrep automatically adapts a best-in-breed workflow to the idiosyncrasies of virtually any dataset, ensuring high-quality preprocessing without manual intervention. PETPrep robustly produces high-quality results on diverse PET data. Additionally, PETPrep introduces less uncontrolled spatial smoothness than observed with commonly used preprocessing tools. PETPrep 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 and encompasses a large set of tools from well-known neuroimaging packages, including FSL, ANTs, FreeSurfer, AFNI, and Nilearn. 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.

PETPrep performs basic preprocessing steps (coregistration, normalization, unwarping, noise component extraction, segmentation, skullstripping etc.) providing outputs that can be easily submitted to a variety of group level analyses, including task-based or resting-state PET imaging, graph theory measures, surface or volume-based statistics, etc. PETPrep allows you to easily do the following:

• Take PET data from unprocessed (only reconstructed) to ready for analysis.

• 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.

[Nat Meth doi:10.1038/s41592-018-0235-4] [Documentation petprep.org] [Software doi:10.5281/zenodo.852659] [Support neurostars.org]