ArticleFrontiers in neuroinformatics2018
APPIAN: Automated Pipeline for PET Image Analysis.
Article in Frontiers in neuroinformatics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
11 citing papers in PubMed.
- Automated long axial field of view PET image processing and kinetic modelling with the TurBO toolbox.European journal of nuclear medicine and molecular imaging · 2026Article
- Critical review of partial volume correction methods in PET and SPECT imaging: benefits, pitfalls, challenges, and future outlook.European journal of nuclear medicine and molecular imaging · 2026Review
- A Methodology to Measure Glucose Metabolism by Quantitative Analysis of PET Images.Journal of healthcare informatics research · 2024Article
- A New Tool for Extracting Static and Dynamic Parameters from [Journal of clinical medicine · 2024Article
- Deep learning model for automatic image quality assessment in PET.BMC medical imaging · 2023Article
- NiftyPAD - Novel Python Package for Quantitative Analysis of Dynamic PET Data.Neuroinformatics · 2023Article
- The impact of subthreshold levels of amyloid deposition on conversion to dementia in patients with amyloid-negative amnestic mild cognitive impairment.Alzheimer's research & therapy · 2022Article
- Automated Data Quality Control in FDOPA brain PET Imaging using Deep Learning.Computer methods and programs in biomedicine · 2021Article
- Kinfitr - an open-source tool for reproducible PET modelling: validation and evaluation of test-retest reliability.EJNMMI research · 2020Article
- MP3: Medical Software for Processing Multi-Parametric Images Pipelines.Frontiers in neuroinformatics · 2020Article
- Magia: Robust Automated Image Processing and Kinetic Modeling Toolbox for PET Neuroinformatics.Frontiers in neuroinformatics · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.