Abstract
BackgroundRecent studies have created awareness that facial features can be reconstructed from high-resolution MRI. Therefore, data sharing in neuroimaging requires special attention to protect participants’ privacy. Facial features removal (FFR) could alleviate these concerns. We assessed the impact of three FFR methods on subsequent automated image analysis to obtain clinically relevant outcome measurements in three clinical groups.MethodsFFR was performed using QuickShear, FaceMasking, and Defacing. In 110 subjects of Alzheimer’s Disease Neuroimaging Initiative, normalized brain volumes (NBV) were measured by SIENAX. In 70 multiple sclerosis patients of the MAGNIMS Study Group, lesion volumes (WMLV) were measured by lesion prediction algorithm in lesion segmentation toolbox. In 84 glioblastoma patients of the PICTURE Study Group, tumor volumes (GBV) were measured by BraTumIA. Failed analyses on FFR-processed images were recorded. Only cases in which all image analyses completed successfully were analyzed. Differences between outcomes obtained from FFR-processed and full images were assessed, by quantifying the intra-class correlation coefficient (ICC) for absolute agreement and by testing for systematic differences using paired t tests.ResultsAutomated analysis methods failed in 0–19% of cases in FFR-processed images versus 0–2% of cases in full images. ICC for absolute agreement ranged from 0.312 (GBV after FaceMasking) to 0.998 (WMLV after Defacing). FaceMasking yielded higher NBV (p = 0.003) and WMLV (p ≤ 0.001). GBV was lower after QuickShear and Defacing (both p < 0.001).ConclusionsAll three outcome measures were affected differently by FFR, including failure of analysis methods and both “random” variation and systematic differences. Further study is warranted to ensure high-quality neuroimaging research while protecting participants’ privacy.Key Points• Protecting participants’ privacy when sharing MRI data is important.• Impact of three facial features removal methods on subsequent analysis was assessed in three clinical groups.• Removing facial features degrades performance of image analysis methods.
Highlights
Recent studies have created awareness that facial features can be reconstructed from high-resolution magnetic resonance imaging (MRI)
Image files should not contain identifying information such as name, date of birth, or any national or hospital-based registration numbers. Such data are often saved in metadata or even filenames of magnetic resonance (MR) images and should be removed before sharing. This is not Subjects in this study were obtained from three different dataset: for Alzheimer’s disease (AD), a dataset from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) study [15]; for multiple sclerosis (MS), a multicenter dataset from the MAGNIMS Study Group [16]; and for treatment-naïve glioblastoma patients, a clinical dataset from the PICTURE project collected in the Amsterdam UMC, location VUmc, in Amsterdam, the Netherlands
We excluded a subject from further analyses if at least one Facial features removal (FFR) method failed on this subject
Summary
Recent studies have created awareness that facial features can be reconstructed from high-resolution MRI. We assessed the impact of three FFR methods on subsequent automated image analysis to obtain clinically relevant outcome measurements in three clinical groups. In 110 subjects of Alzheimer’s Disease Neuroimaging Initiative, normalized brain volumes (NBV) were measured by SIENAX. In 70 multiple sclerosis patients of the MAGNIMS Study Group, lesion volumes (WMLV) were measured by lesion prediction algorithm in lesion segmentation toolbox. In 84 glioblastoma patients of the PICTURE Study Group, tumor volumes (GBV) were measured by BraTumIA. Failed analyses on FFR-processed images were recorded. Differences between outcomes obtained from FFR-processed and full images were assessed, by quantifying the intra-class correlation coefficient (ICC) for absolute agreement and by testing for systematic differences using paired t tests
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