Paper Info
Title
APPIAN: Automated Pipeline for PET Image Analysis.
Abstract
APPIAN is an automated pipeline for user-friendly and reproducible analysis of positron emission tomography (PET) images with the aim of automating all processing steps up to the statistical analysis of measures derived from the final output images. The three primary processing steps are coregistration of PET images to T1-weighted magnetic resonance (MR) images, partial-volume correction (PVC), and quantification with tracer kinetic modeling. While there are alternate open-source PET pipelines, none offers all of the features necessary for making automated PET analysis as reliably, flexibly and easily extendible as possible. To this end, a novel method for automated quality control (QC) has been designed to facilitate reliable, reproducible research by helping users verify that each processing stage has been performed as expected. Additionally, a web browser-based GUI has been implemented to allow both the 3D visualization of the output images, as well as plots describing the quantitative results of the analyses performed by the pipeline. APPIAN also uses flexible region of interest (ROI) definitionwith both volumetric and, optionally, surface-based ROI-to allow users to analyze data from a wide variety of experimental paradigms, e. g., longitudinal lesion studies, large cross-sectional population studies, multi-factorial experimental designs, etc. Finally, APPIAN is designed to be modular so that users can easily test new algorithms for PVC or quantification or add entirely new analyses to the basic pipeline. We validate the accuracy of APPIAN against the Monte-Carlo simulated SORTEO database and show that, after PVC, APPIAN recovers radiotracer concentrations within 93-100% accuracy.
Year
DOI
Venue
2018
10.3389/fninf.2018.00064
FRONTIERS IN NEUROINFORMATICS
Keywords
Field
DocType
science,automation,pipeline,software,quality control,PET
Data mining,Computer vision,Population,Pipeline transport,Computer science,Visualization,Automation,Artificial intelligence,Region of interest,Modular design,Python (programming language),Design of experiments
Journal
Volume
Citations 
PageRank 
12
0
0.34
References 
Authors
13
5
Name
Order
Citations
PageRank
Thomas Funck120.72
Kevin Larcher2111.91
Paule-Joanne Toussaint3161.70
Alan C. Evans43045574.95
Alexander Thiel5142.41