Facing privacy in neuroimaging: removing facial features degrades performance of image analysis methods.

BACKGROUND: Recent 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. METHODS: FFR 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. RESULTS: Automated 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). CONCLUSIONS: All 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.

Do mathematical model studies settle the controversy on the origin of cardiac synchronous trans-thoracic electrical impedance variations? A systematic review.

Impedance cardiography (ICG) is a method to evaluate cardiac-stroke volume and cardiac-output by measuring the cardiac-synchronous changes in the dynamic trans-thoracic electrical impedance (ΔZ). Clinical evaluations on the accuracy of ICG showed varying results. Consequently, the classic assumption in ICG-the aorta as a main source of ΔZ-is questioned and subsequently investigated in simulation studies using mathematical models of the electrical resistivity of the human body. The aim is to review the consensus in mathematical modelling studies that investigate the origin of the ΔZ as measured in ICG. In a systematic literature search, studies were identified and surveyed with reference to characteristics, such as included organs and their resistivity and geometries, electrode positions and calculation of ΔZ, to review the consensus between mathematical modelling studies that investigate the origin of the ΔZ as measured in ICG. Thirteen papers showed considerable variation in the model's characteristics with varying or contradicting outcomes for the ΔZ 's origin. For instance, 11 studies excluded perfused muscle tissue, implying implicitly their insignificance, while 3 other studies included muscle tissue and indicated it as the most important origin of ΔZ. In conclusion, the reviewed papers show a lack of consensus with respect to both the modelled characteristics as well as the model outcomes and, as a result, these studies failed to settle the controversy on ΔZ 's origin. Recommendations have been added to improve future mathematical model studies.