The LONI Pipeline Processing Environment

• Published in: NeuroImage (Orlando, Fla.) Vol. 19; no. 3; pp. 1033 - 1048
• Main Authors: Rex, David E, Ma, Jeffrey Q, Toga, Arthur W
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 01.07.2003; Elsevier Limited
• Subjects: Algorithms; Application programming interface; Automated analysis; Automation; Brain Mapping - methods; Data Interpretation, Statistical; Data processing; Data processing environment; Dataflow model; Distributed processing; Female; Humans; Image Processing, Computer-Assisted - methods; Linux; Local Area Networks; Male; Medical imaging; Microcomputers; Nonlinear Dynamics; Operating systems; Pipelines; Random access memory; Reproducibility of Results; Software; Studies; Visual programming; Automated analysis; Visual programming; Data processing environment; Dataflow model
• ISSN: 1053-8119; 1095-9572
• DOI: 10.1016/S1053-8119(03)00185-X

The analysis of raw data in neuroimaging has become a computationally entrenched process with many intricate steps run on increasingly larger datasets. Many software packages exist that provide either complete analyses or specific steps in an analysis. These packages often possess diverse input and output requirements, utilize different file formats, run in particular environments, and have limited abilities with certain types of data. The combination of these packages to achieve more sensitive and accurate results has become a common tactic in brain mapping studies but requires much work to ensure valid interoperation between programs. The handling, organization, and storage of intermediate data can prove difficult as well. The LONI Pipeline Processing Environment is a simple, efficient, and distributed computing solution to these problems enabling software inclusion from different laboratories in different environments. It is used here to derive a T1-weighted MRI atlas of the human brain from 452 normal young adult subjects with fully automated processing. The LONI Pipeline Processing Environment’s parallel processing efficiency using an integrated client/server dataflow model was 80.9% when running the atlas generation pipeline from a PC client (Acer TravelMate 340T) on 48 dedicated server processors (Silicon Graphics Inc. Origin 3000). The environment was 97.5% efficient when the same analysis was run on eight dedicated processors.