Hendrik Mattern1, Martin Knoll1, Falk Lüsebrink1,2, Oliver Speck1,3,4,5. 1. Biomedical Magnetic Resonance, Otto-von-Guericke-University Magdeburg, Magdeburg, Germany. 2. Medicine and Digitalization, Department of Neurology, Otto-von-Guericke University Magdeburg, Magdeburg, Germany. 3. German Center for Neurodegenerative Disease, Magdeburg, Germany. 4. Center for Behavioral Brain Sciences, Magdeburg, Germany. 5. Leibniz Institute for Neurobiology, Magdeburg, Germany.
Abstract
PURPOSE: Publicly available data provision is an essential part of open science. However, open data can conflict with data privacy and data protection regulations. Head scans are particularly vulnerable because the subject's face can be reconstructed from the acquired images. Although defacing can impede subject identification in reconstructed images, this approach is not applicable to k-space raw data. To address this challenge and allow defacing of raw data for publication, we present chemical shift-based prospective k-space anonymization (CHARISMA). METHODS: In spin-warp imaging, fat shift occurs along the frequency-encoding direction. By placing an oil-filled mask onto the subject's face, the shifted fat signal can overlap with the face to deface k-space during the acquisition. The CHARISMA approach was tested for gradient-echo sequences in a single subject wearing the oil-filled mask at 7 T. Different fat shifts were compared by varying the readout bandwidth. Furthermore, intensity-based segmentation was used to test whether the images could be unmasked retrospectively. RESULTS: To impede subject identification after retrospective unmasking, the signal of face and shifted oil should overlap. In this single-subject study, a shift of 3.3 mm to 4.9 mm resulted in the most efficient masking. Independent of CHARISMA, long TEs induce signal decay and dephasing, which impeded unmasking. CONCLUSION: To our best knowledge, CHARISMA is the first prospective k-space defacing approach. With proper fat-shift direction and amplitude, this easy-to-build, low-cost solution impaired subject identification in gradient-echo data considerably. Further sequences will be tested with CHARISMA in the future.
PURPOSE: Publicly available data provision is an essential part of open science. However, open data can conflict with data privacy and data protection regulations. Head scans are particularly vulnerable because the subject's face can be reconstructed from the acquired images. Although defacing can impede subject identification in reconstructed images, this approach is not applicable to k-space raw data. To address this challenge and allow defacing of raw data for publication, we present chemical shift-based prospective k-space anonymization (CHARISMA). METHODS: In spin-warp imaging, fat shift occurs along the frequency-encoding direction. By placing an oil-filled mask onto the subject's face, the shifted fat signal can overlap with the face to deface k-space during the acquisition. The CHARISMA approach was tested for gradient-echo sequences in a single subject wearing the oil-filled mask at 7 T. Different fat shifts were compared by varying the readout bandwidth. Furthermore, intensity-based segmentation was used to test whether the images could be unmasked retrospectively. RESULTS: To impede subject identification after retrospective unmasking, the signal of face and shifted oil should overlap. In this single-subject study, a shift of 3.3 mm to 4.9 mm resulted in the most efficient masking. Independent of CHARISMA, long TEs induce signal decay and dephasing, which impeded unmasking. CONCLUSION: To our best knowledge, CHARISMA is the first prospective k-space defacing approach. With proper fat-shift direction and amplitude, this easy-to-build, low-cost solution impaired subject identification in gradient-echo data considerably. Further sequences will be tested with CHARISMA in the future.
Authors: Thorsten A Bley; Oliver Wieben; Christopher J François; Jean H Brittain; Scott B Reeder Journal: J Magn Reson Imaging Date: 2010-01 Impact factor: 4.813
Authors: Stuart Berg; Dominik Kutra; Thorben Kroeger; Christoph N Straehle; Bernhard X Kausler; Carsten Haubold; Martin Schiegg; Janez Ales; Thorsten Beier; Markus Rudy; Kemal Eren; Jaime I Cervantes; Buote Xu; Fynn Beuttenmueller; Adrian Wolny; Chong Zhang; Ullrich Koethe; Fred A Hamprecht; Anna Kreshuk Journal: Nat Methods Date: 2019-09-30 Impact factor: 28.547
Authors: Nicholas J Tustison; Brian B Avants; Philip A Cook; Yuanjie Zheng; Alexander Egan; Paul A Yushkevich; James C Gee Journal: IEEE Trans Med Imaging Date: 2010-04-08 Impact factor: 10.048
Authors: Mark E Ladd; Peter Bachert; Martin Meyerspeer; Ewald Moser; Armin M Nagel; David G Norris; Sebastian Schmitter; Oliver Speck; Sina Straub; Moritz Zaiss Journal: Prog Nucl Magn Reson Spectrosc Date: 2018-06-08 Impact factor: 9.795
Authors: David C Van Essen; Stephen M Smith; Deanna M Barch; Timothy E J Behrens; Essa Yacoub; Kamil Ugurbil Journal: Neuroimage Date: 2013-05-16 Impact factor: 6.556