대한치매학회지 (Dementia and Neurocognitive Disorders)

대한치매학회 (Korean Dementia Association)
권호 표지
DOI QR코드

DOI QR Code

Assessing the Impact of Defacing Algorithms on Brain Volumetry Accuracy in MRI Analyses

  • Dong-Woo Ryu (Department of Neurology, College of Medicine, The Catholic University of Korea) ;
  • ChungHwee Lee (Department of Neurology, College of Medicine, The Catholic University of Korea) ;
  • Hyuk-je Lee (Department of Neurology, College of Medicine, The Catholic University of Korea) ;
  • Yong S Shim (Department of Neurology, College of Medicine, The Catholic University of Korea) ;
  • Yun Jeong Hong (Department of Neurology, College of Medicine, The Catholic University of Korea) ;
  • Jung Hee Cho (Department of Neurology, College of Medicine, The Catholic University of Korea) ;
  • Seonggyu Kim (Department of Electronic Engineering, Hanyang University) ;
  • Jong-Min Lee (Department of Biomedical Engineering, Hanyang University) ;
  • Dong Won Yang (Department of Neurology, College of Medicine, The Catholic University of Korea)
  • 투고 : 2024.04.15
  • 심사 : 2024.04.26
  • 발행 : 2024.07.31
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

Background and Purpose: To ensure data privacy, the development of defacing processes, which anonymize brain images by obscuring facial features, is crucial. However, the impact of these defacing methods on brain imaging analysis poses significant concern. This study aimed to evaluate the reliability of three different defacing methods in automated brain volumetry. Methods: Magnetic resonance imaging with three-dimensional T1 sequences was performed on ten patients diagnosed with subjective cognitive decline. Defacing was executed using mri_deface, BioImage Suite Web-based defacing, and Defacer. Brain volumes were measured employing the QBraVo program and FreeSurfer, assessing intraclass correlation coefficient (ICC) and the mean differences in brain volume measurements between the original and defaced images. Results: The mean age of the patients was 71.10±6.17 years, with 4 (40.0%) being male. The total intracranial volume, total brain volume, and ventricle volume exhibited high ICCs across the three defacing methods and 2 volumetry analyses. All regional brain volumes showed high ICCs with all three defacing methods. Despite variations among some brain regions, no significant mean differences in regional brain volume were observed between the original and defaced images across all regions. Conclusions: The three defacing algorithms evaluated did not significantly affect the results of image analysis for the entire brain or specific cerebral regions. These findings suggest that these algorithms can serve as robust methods for defacing in neuroimaging analysis, thereby supporting data anonymization without compromising the integrity of brain volume measurements.

키워드

과제정보

This study was supported by a grant from the Ministry of Health and Welfare (HI18C0530).

참고문헌

  1. Schwarz CG, Kremers WK, Arani A, Savvides M, Reid RI, Gunter JL, et al. A face-off of MRI research sequences by their need for de-facing. Neuroimage 2023;276:120199.
  2. Gao C, Landman BA, Prince JL, Carass A. Reproducibility evaluation of the effects of MRI defacing on brain segmentation. J Med Imaging (Bellingham) 2023;10:064001.
  3. Rubbert C, Wolf L, Turowski B, Hedderich DM, Gaser C, Dahnke R, et al. Impact of defacing on automated brain atrophy estimation. Insights Imaging 2022;13:54.
  4. Bhalerao GV, Parekh P, Saini J, Venkatasubramanian G, John JP; ADBS Consortium. Systematic evaluation of the impact of defacing on quality and volumetric assessments on T1-weighted MR-images. J Neuroradiol 2022;49:250-257.
  5. Buimer EEL, Schnack HG, Caspi Y, van Haren NEM, Milchenko M, Pas P, et al. De-identification procedures for magnetic resonance images and the impact on structural brain measures at different ages. Hum Brain Mapp 2021;42:3643-3655.
  6. de Sitter A, Visser M, Brouwer I, Cover KS, van Schijndel RA, Eijgelaar RS, et al. Facing privacy in neuroimaging: removing facial features degrades performance of image analysis methods. Eur Radiol 2020;30:1062-1074.
  7. Jessen F, Amariglio RE, van Boxtel M, Breteler M, Ceccaldi M, Chetelat G, et al. A conceptual framework for research on subjective cognitive decline in preclinical Alzheimer's disease. Alzheimers Dement 2014;10:844-852.
  8. Bischoff-Grethe A, Ozyurt IB, Busa E, Quinn BT, Fennema-Notestine C, Clark CP, et al. A technique for the deidentification of structural brain MR images. Hum Brain Mapp 2007;28:892-903.
  9. Jeong YU, Yoo S, Kim YH, Shim WH. De-identification of facial features in magnetic resonance images: software development using deep learning technology. J Med Internet Res 2020;22:e22739.
  10. Ryu DW, Hong YJ, Cho JH, Kwak K, Lee JM, Shim YS, et al. Automated brain volumetric program measuring regional brain atrophy in diagnosis of mild cognitive impairment and Alzheimer's disease dementia. Brain Imaging Behav 2022;16:2086-2096.
  11. Theyers AE, Zamyadi M, O'Reilly M, Bartha R, Symons S, MacQueen GM, et al. Multisite comparison of MRI defacing software across multiple cohorts. Front Psychiatry 2021;12:617997.
  12. Schwarz CG, Kremers WK, Lowe VJ, Savvides M, Gunter JL, Senjem ML, et al. Face recognition from research brain PET: an unexpected PET problem. Neuroimage 2022;258:119357.
  13. Schwarz CG, Kremers WK, Weigand SD, Prakaashana CM, Senjem ML, Przybelski SA, et al. Effects of de-facing software mri_reface on utility of imaging biomarkers used in Alzheimer's disease research. Neuroimage Clin 2023;40:103507.
  14. Bruna R, Vaghari D, Greve A, Cooper E, Mada MO, Henson RN. Modified MRI anonymization (de-facing) for improved MEG coregistration. Bioengineering (Basel) 2022;9:591.
  15. Delbarre DJ, Santos L, Ganjgahi H, Horner N, McCoy A, Westerberg H, et al. Application of a convolutional neural network to the quality control of MRI defacing. Comput Biol Med 2022;151:106211.