PMID- 30337866
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20200929
IS  - 1662-5196 (Print)
IS  - 1662-5196 (Electronic)
IS  - 1662-5196 (Linking)
VI  - 12
DP  - 2018
TI  - APPIAN: Automated Pipeline for PET Image Analysis.
PG  - 64
LID - 10.3389/fninf.2018.00064 [doi]
LID - 64
AB  - 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.
FAU - Funck, Thomas
AU  - Funck T
AD  - Montreal Neurological Institute, McGill University, Montreal, QC, Canada.
AD  - Jewish General Hospital and Lady Davis Institute for Medical Research, Montreal, 
      QC, Canada.
FAU - Larcher, Kevin
AU  - Larcher K
AD  - Biospective, Inc., Montreal, QC, Canada.
FAU - Toussaint, Paule-Joanne
AU  - Toussaint PJ
AD  - Montreal Neurological Institute, McGill University, Montreal, QC, Canada.
FAU - Evans, Alan C
AU  - Evans AC
AD  - Montreal Neurological Institute, McGill University, Montreal, QC, Canada.
AD  - Biospective, Inc., Montreal, QC, Canada.
AD  - Department of Neurology and Neurosurgery, McGill University, Montreal, QC, 
      Canada.
FAU - Thiel, Alexander
AU  - Thiel A
AD  - Jewish General Hospital and Lady Davis Institute for Medical Research, Montreal, 
      QC, Canada.
AD  - Department of Neurology and Neurosurgery, McGill University, Montreal, QC, 
      Canada.
LA  - eng
PT  - Journal Article
DEP - 20180926
PL  - Switzerland
TA  - Front Neuroinform
JT  - Frontiers in neuroinformatics
JID - 101477957
PMC - PMC6178989
OTO - NOTNLM
OT  - PET
OT  - automation
OT  - open science
OT  - pipeline
OT  - quality control
OT  - software
EDAT- 2018/10/20 06:00
MHDA- 2018/10/20 06:01
PMCR- 2018/01/01
CRDT- 2018/10/20 06:00
PHST- 2018/06/18 00:00 [received]
PHST- 2018/09/06 00:00 [accepted]
PHST- 2018/10/20 06:00 [entrez]
PHST- 2018/10/20 06:00 [pubmed]
PHST- 2018/10/20 06:01 [medline]
PHST- 2018/01/01 00:00 [pmc-release]
AID - 10.3389/fninf.2018.00064 [doi]
PST - epublish
SO  - Front Neuroinform. 2018 Sep 26;12:64. doi: 10.3389/fninf.2018.00064. eCollection 
      2018.