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Demonstrating quality control procedures for fMRI in DPABI.

Bin Lu1,2, Chao-Gan Yan1,2,3,4

  • 1CAS Key Laboratory of Behavioral Science, Institute of Psychology, Beijing, China.

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|March 10, 2023
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This summary is machine-generated.

Quality control for functional magnetic resonance imaging (fMRI) is crucial. This study demonstrates fMRI QC using DPABI pipelines, highlighting the need for automated tools in big data research.

Keywords:
DPABIfMRIneuroimagingpipelinequality control

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Area of Science:

  • Neuroimaging
  • Data Science
  • Medical Imaging Analysis

Background:

  • Quality control (QC) is essential for functional magnetic resonance imaging (fMRI) studies.
  • Current fMRI QC methods differ across preprocessing pipelines, increasing workload with larger datasets and multi-site studies.
  • Standardized QC procedures are needed, especially with growing fMRI data volumes.

Purpose of the Study:

  • To illustrate a quality control (QC) procedure for functional magnetic resonance imaging (fMRI) data using the DPABI pipeline.
  • To demonstrate the application of DPABI-derived reports in identifying and excluding low-quality fMRI data.
  • To provide a practical example of QC within the context of large-scale neuroimaging research.

Main Methods:

  • A well-organized, open-access dataset was preprocessed using the DPABI software pipeline.
  • Six categories of quality control reports generated by DPABI were utilized.
  • Image quality assessment was performed to exclude inadequate datasets.

Main Results:

  • The quality control procedure identified 12 participants (8.6%) with excluded data.
  • An additional 8 participants (5.8%) were categorized as having uncertain data quality.
  • The application of DPABI reports facilitated the QC process.

Conclusions:

  • The DPABI pipeline offers a structured approach to fMRI data quality control.
  • Automated QC tools are increasingly necessary for handling big data in neuroimaging.
  • Visual inspection remains a vital component of rigorous fMRI QC, even with automated methods.