APPIAN: Automated Pipeline for PET Image Analysis.

Thomas Funck,Alan C Evans,Alexander Thiel,Paule-Joanne Toussaint,Kevin Larcher

doi:10.3389/fninf.2018.00064

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) 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.

Highlights

The increasing availability of large brain imaging data sets makes automated analysis essential
We present APPIAN (Automated Pipeline for positron emission tomography (PET) Image Analysis) a new open-source pipeline based on NiPype (Gorgolewski et al, 2011) for performing automated PET data analysis
APPIAN is a novel PET processing pipeline that seeks to automate the processing of reconstructed PET images for a wide variety of experimental designs

Summary

Introduction

The increasing availability of large brain imaging data sets makes automated analysis essential. Is automated analysis important for saving time, but it increases the reproducibility of research. No existing post-reconstruction positron emission tomography (PET) software package satisfies all the needs of researchers, code that is free, open-source, language agnostic, extendible, deployable on web platforms as well as locally, and including all necessary processing steps prior to statistical analysis. We present APPIAN (Automated Pipeline for PET Image Analysis) a new open-source pipeline based on NiPype (Gorgolewski et al, 2011) for performing automated PET data analysis. The starting point for APPIAN are reconstructed PET images on which all necessary processing steps are performed to obtain quantitative measures from the original PET images (Figure 1).

Methods

Results

Discussion

Conclusion