APPIAN: Automated Pipeline for PET Image Analysis

Frontiers in Neuroinformatics

Kevin Larcher¹  Alan C. Evans²  Alexander Thiel²  Thomas Funck³  Paule-Joanne Toussaint⁴ 

[1] Biospective, Inc., Montreal, QC, Canada;Department of Neurology and Neurosurgery, McGill University, Montreal, QC, Canada;Jewish General Hospital and Lady Davis Institute for Medical Research, Montreal, QC, Canada;Montreal Neurological Institute, McGill University, Montreal, QC, Canada;

关键词: open science;    automation;    pipeline;    software;    quality control;    PET;   

DOI  :  10.3389/fninf.2018.00064

来源: DOAJ

【 摘 要 】

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) definition—with 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.

【 授权许可】

Unknown