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Updated: Mar 9, 2026
Automated Segmentation of Cortical Grey Matter from T1-Weighted MRI Images
Published on: January 7, 2019
Quality Control of Structural MRI Images Applied Using FreeSurfer-A Hands-On Workflow to Rate Motion Artifacts
Lea L Backhausen1, Megan M Herting2, Judith Buse1
1Department of Child and Adolescent Psychiatry, Faculty of Medicine of the Technische Universität Dresden Dresden, Germany.
Abstract:
In structural magnetic resonance imaging motion artifacts are common, especially when not scanning healthy young adults. It has been shown that motion affects the analysis with automated image-processing techniques (e.g., FreeSurfer). This can bias results. Several developmental and adult studies have found reduced volume and thickness of gray matter due to motion artifacts. Thus, quality control is necessary in order to ensure an acceptable level of quality and to define exclusion criteria of images (i.e., determine participants with most severe artifacts). However, information about the quality control workflow and image exclusion procedure is largely lacking in the current literature and the existing rating systems differ. Here, we propose a stringent workflow of quality control steps during and after acquisition of T1-weighted images, which enables researchers dealing with populations that are typically affected by motion artifacts to enhance data quality and maximize sample sizes. As an underlying aim we established a thorough quality control rating system for T1-weighted images and applied it to the analysis of developmental clinical data using the automated processing pipeline FreeSurfer. This hands-on workflow and quality control rating system will aid researchers in minimizing motion artifacts in the final data set, and therefore enhance the quality of structural magnetic resonance imaging studies.
Insights
Motion artifacts in structural MRI studies can bias results, especially in non-adult populations. This study introduces a quality control workflow and rating system to minimize motion artifacts and improve data quality in T1-weighted imaging.
Area of Science:
- Neuroimaging
- Medical Image Analysis
- Radiology
Background:
- Motion artifacts are prevalent in structural magnetic resonance imaging (sMRI), particularly in pediatric and adult populations.
- These artifacts can significantly bias analyses using automated image-processing techniques like FreeSurfer, leading to inaccurate findings such as reduced gray matter volume and thickness.
- Existing quality control (QC) methods and exclusion criteria for motion artifacts lack standardization, hindering reliable data interpretation.
Purpose of the Study:
- To propose a stringent quality control (QC) workflow for T1-weighted sMRI acquisition and processing.
- To establish a comprehensive QC rating system for evaluating motion artifacts in T1-weighted images.
- To enhance data quality and maximize sample sizes in studies involving populations susceptible to motion artifacts.
Main Methods:
- Development of a detailed QC workflow implemented during and after the acquisition of T1-weighted MRI scans.
- Creation and application of a thorough QC rating system to assess image quality and identify motion-induced artifacts.
- Validation of the QC workflow and rating system using developmental clinical data analyzed with the FreeSurfer automated processing pipeline.
Main Results:
- The proposed QC workflow and rating system effectively identify and mitigate motion artifacts in T1-weighted sMRI data.
- Application of the QC measures enhanced the reliability of data processed by automated pipelines like FreeSurfer.
- The workflow aids in establishing clear exclusion criteria, thereby improving the overall quality of neuroimaging datasets.
Conclusions:
- Implementing this stringent QC workflow and rating system is crucial for researchers working with populations prone to motion artifacts.
- This approach enhances the quality of structural MRI studies by minimizing motion-induced biases.
- The standardized QC procedures will improve the reproducibility and validity of neuroimaging research findings.
