title: A technique for the deidentification of structural brain MR images.
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. [2002]: Neuron 33:341-355; Fischl et al. [2004]: 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. [1998]: IEEE Trans Med Imaging 17:87-97), defaced, and then skull-stripped using either a hybrid watershed algorithm (Ségonne et al. [2004]: Neuroimage 22:1060-1075, in FreeSurfer) or Brain Surface Extractor (Sandor and Leahy [1997]: IEEE Trans Med Imaging 16:41-54; Shattuck et al. [2001]: 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.
authors.affiliation: Laboratory of Cognitive Imaging, Department of Psychiatry, University of California, San Diego, La Jolla, USA.
authors.forename: Amanda
authors.initials: A
authors.lastname: Bischoff-Grethe
authors.forename: I Burak
authors.initials: IB
authors.lastname: Ozyurt
authors.forename: Evelina
authors.initials: E
authors.lastname: Busa
authors.forename: Brian T
authors.initials: BT
authors.lastname: Quinn
authors.forename: Christine
authors.initials: C
authors.lastname: Fennema-Notestine
authors.forename: Camellia P
authors.initials: CP
authors.lastname: Clark
authors.forename: Shaunna
authors.initials: S
authors.lastname: Morris
authors.forename: Mark W
authors.initials: MW
authors.lastname: Bondi
authors.forename: Terry L
authors.initials: TL
authors.lastname: Jernigan
authors.forename: Anders M
authors.initials: AM
authors.lastname: Dale
authors.forename: Gregory G
authors.initials: GG
authors.lastname: Brown
authors.forename: Bruce
authors.initials: B
authors.lastname: Fischl
mesh_terms: D000328:Adult; D000368:Aged; D000375:Aging / Q000502:physiology; D000465:Algorithms; D000544:Alzheimer Disease / Q000473:pathology; D001921:Brain / Q000033:anatomy & histology*; D003627:Data Interpretation, Statistical; D003863:Depression / Q000473:pathology; D005260:Female; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008297:Male; D008875:Middle Aged; D012886:Skull / Q000033:anatomy & histology