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- Title
A technique for the deidentification of structural brain MR images.
- Authors
Bischoff-Grethe, Amanda; Ozyurt, I. Burak; Busa, Evelina; Quinn, Brian T.; Fennema-Notestine, Christine; Clark, Camellia P.; Morris, Shaunna; Bondi, Mark W.; Jernigan, Terry L.; Dale, Anders M.; Brown, Gregory G.; Fischl, Bruce
- Abstract
Due to the increasing need for subject privacy, the ability to deidentify structural MR images so that they do not provide full facial detail is desirable. A program was developed that uses models of nonbrain structures for removing potentially identifying facial features. When a novel image is presented, the optimal linear transform is computed for the input volume (Fischl et al. []: Neuron 33:341-355; Fischl et al. []: Neuroimage 23 (Suppl 1):S69-S84). A brain mask is constructed by forming the union of all voxels with nonzero probability of being brain and then morphologically dilated. All voxels outside the mask with a nonzero probability of being a facial feature are set to 0. The algorithm was applied to 342 datasets that included two different T1-weighted pulse sequences and four different diagnoses (depressed, Alzheimer's, and elderly and young control groups). Visual inspection showed none had brain tissue removed. In a detailed analysis of the impact of defacing on skull-stripping, 16 datasets were bias corrected with N3 (Sled et al. []: IEEE Trans Med Imaging 17:87-97), defaced, and then skull-stripped using either a hybrid watershed algorithm (Ségonne et al. []: Neuroimage 22:1060-1075, in FreeSurfer) or Brain Surface Extractor (Sandor and Leahy []: IEEE Trans Med Imaging 16:41-54; Shattuck et al. []: Neuroimage 13:856-876); defacing did not appreciably influence the outcome of skull-stripping. Results suggested that the automatic defacing algorithm is robust, efficiently removes nonbrain tissue, and does not unduly influence the outcome of the processing methods utilized; in some cases, skull-stripping was improved. Analyses support this algorithm as a viable method to allow data sharing with minimal data alteration within large-scale multisite projects. Hum Brain Mapp 2007. © 2007 Wiley-Liss, Inc.
- Publication
Human Brain Mapping, 2007, Vol 28, Issue 9, p892
- ISSN
1065-9471
- Publication type
Article
- DOI
10.1002/hbm.20312