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NINS_QC: A Data Quality Checker in Neuroimaging.

Komal Jindal1, Shallu Sharma1, Pravat K Mandal1,2,3

  • 1Neuroimaging and Neurospectroscopy, National Brain Research Centre, Gurugram, Haryana 122051, India.

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|March 19, 2026
PubMed
Summary
This summary is machine-generated.

A new platform, NINS_QC, streamlines quality control for multimodal neuroimaging and neurospectroscopy data. This user-friendly tool enhances data reliability and reproducibility in clinical neuroscience research.

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Area of Science:

  • Neuroscience
  • Medical Imaging
  • Data Science

Background:

  • Data quality evaluation is crucial but labor-intensive in clinical neuroscience.
  • A lack of integrated quality control (QC) platforms for multimodal neuroimaging data hinders brain research.
  • Existing QC pipelines often focus solely on MRI data, neglecting other modalities.

Purpose of the Study:

  • To develop a comprehensive, user-friendly QC platform for multimodal neuroimaging and neurospectroscopy data.
  • To address the limitations of existing QC pipelines by integrating various data types.
  • To facilitate reliable and reproducible clinical neuroscience research through enhanced data quality assessment.

Main Methods:

  • Developed the Neuroimaging and Neurospectroscopy (NINS) QC platform, named NINS_QC.
  • Designed NINS_QC as a transparent and user-friendly framework.
  • Enabled distribution as a standalone or downloadable executable for local data processing.

Main Results:

  • NINS_QC supports multimodal neuroimaging (MRI, fMRI, DTI, MRS) and neuropsychological data.
  • The platform allows for both metrics-based and visual-based data assessment.
  • It is suitable for environments with strict data privacy requirements due to local processing capabilities.

Conclusions:

  • NINS_QC provides a unified solution for quality control of diverse neuroimaging data.
  • The platform enhances the reliability and reproducibility of clinical neuroscience research.
  • NINS_QC offers a flexible and accessible tool for researchers dealing with multimodal datasets.