NINS_QC: A Data Quality Checker in Neuroimaging

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.