Scholarlink

Purpose <p>To validate five automated structural MRI quality assessment tools against expert visual ratings and assess their reliability, validity, and practical utility for large-scale neuroimaging research.</p> Methods <p>Structural MRI data from 92 participants (ages 5–20 years) in the Healthy Brain Network were analyzed. Five tools—FreeSurfer, FSQC, MRIQC, BrainSuite, and the Computational Anatomy Toolbox (CAT)—were evaluated for computational reproducibility, convergent validity with expert ratings, and discriminative ability between expert-rated “Pass” and “Fail” scans. Expert ratings served as the reference standard.</p> Results <p>All tools demonstrated excellent computational reproducibility. FreeSurfer, FSQC, MRIQC, and CAT correlated strongly with expert ratings and discriminated effectively between “Pass” and “Fail” scans. FreeSurfer, FSQC, and CAT achieved near-perfect classification accuracy, although CAT systematically assigned higher scores even to poor-quality scans, suggesting the need for stricter thresholds. MRIQC aligned less strongly but captured complementary quality dimensions. BrainSuite metrics did not correspond to expert ratings or separate scan quality.</p> Conclusion <p>Automated MRI quality assessment tools provide reliable and scalable alternatives to manual inspection. FreeSurfer, FSQC, and CAT approach expert-level accuracy but require careful calibration, while MRIQC provides complementary insights despite weaker alignment. Adoption of automated approaches, with awareness of tool-specific limitations, can enhance reproducibility, efficiency, and rigor in large-scale neuroimaging studies.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Validation of five automated structural MRI quality assessment tools against expert ratings

  • 期刊论文

  • 发表日期：

    2026年2月20日

查看全文
  • Yi-Sheng Wong

摘要

Purpose

To validate five automated structural MRI quality assessment tools against expert visual ratings and assess their reliability, validity, and practical utility for large-scale neuroimaging research.

Methods

Structural MRI data from 92 participants (ages 5–20 years) in the Healthy Brain Network were analyzed. Five tools—FreeSurfer, FSQC, MRIQC, BrainSuite, and the Computational Anatomy Toolbox (CAT)—were evaluated for computational reproducibility, convergent validity with expert ratings, and discriminative ability between expert-rated “Pass” and “Fail” scans. Expert ratings served as the reference standard.

Results

All tools demonstrated excellent computational reproducibility. FreeSurfer, FSQC, MRIQC, and CAT correlated strongly with expert ratings and discriminated effectively between “Pass” and “Fail” scans. FreeSurfer, FSQC, and CAT achieved near-perfect classification accuracy, although CAT systematically assigned higher scores even to poor-quality scans, suggesting the need for stricter thresholds. MRIQC aligned less strongly but captured complementary quality dimensions. BrainSuite metrics did not correspond to expert ratings or separate scan quality.

Conclusion

Automated MRI quality assessment tools provide reliable and scalable alternatives to manual inspection. FreeSurfer, FSQC, and CAT approach expert-level accuracy but require careful calibration, while MRIQC provides complementary insights despite weaker alignment. Adoption of automated approaches, with awareness of tool-specific limitations, can enhance reproducibility, efficiency, and rigor in large-scale neuroimaging studies.