Obscuring surface anatomy in volumetric imaging data

Abstract

The identifying or sensitive anatomical features in MR and CT images used in research raise patient privacy concerns when such data are shared. In order to protect human subject privacy, we developed a method of anatomical surface modification and investigated the effects of such modification on image statistics and common neuroimaging processing tools. Common approaches to obscuring facial features typically remove large portions of the voxels. The approach described here focuses on blurring the anatomical surface instead, to avoid impinging on areas of interest and hard edges that can confuse processing tools. The algorithm proceeds by extracting a thin boundary layer containing surface anatomy from a region of interest. This layer is then "stretched" and "flattened" to fit into a thin "box" volume. After smoothing along a plane roughly parallel to anatomy surface, this volume is transformed back onto the boundary layer of the original data. The above method, named normalized anterior filtering, was coded in MATLAB and applied on a number of high resolution MR and CT scans. To test its effect on automated tools, we compared the output of selected common skull stripping and MR gain field correction methods used on unmodified and obscured data. With this paper, we hope to improve the understanding of the effect of surface deformation approaches on the quality of de-identified data and to provide a useful de-identification tool for MR and CT acquisitions.

Fig. 1

Triangulation of the anatomical surface over rectangular grid. Base plane with rectangular coordinates in relation to a head volume (left), rectangular grid on this plane (middle) and face surface triangulation mesh (right), with arrow showing coordinate mapping

Fig. 2

2D face ROI of an MR image with computed surface boundary for a “external” surface, b anatomical surface and c “deep” surface

Fig. 3

a boundary layer partition L, b rectangular box partition L̂, c, d quasi-block Ω and corresponding regular block Ω̂, e, f tetrahedral partitions of Ω and Ω̂

Fig. 4

Effect of various masking methods on an MR slice. a unmodified, b fill coating, c localized blur, d normalized filtering

Fig. 5

Surface renderings of a unmodified MR head acquisition, b modified by fill coating, c localized blur, d normalized filtering

Fig. 6

Histograms of a head T1 acquisition: unmodified original (solid line) and masked (interrupted lines) with fill coating, localized blur, and normalized filtering

Fig. 7

Arrows show the lines along which voxel values are averaged in case of a original, b “flattened” anatomical surface

Fig. 8

Intermediate steps of normalized filtering applied to a T1 head acquisition (face ROI is shown). a volume of interest, b boundary layer (sagittal), c normalized boundary layer (coronal), d filtered boundary layer (coronal), e inverse transform of the filtered layer to the original volume (sagittal). Upper row: 3D surface rendering, lower row: a middle 2D slice from the volume

Fig. 9

CT head acquisition modified by normalized filtering. a 2D axial slice with marked face ROI, b 2D slice from the face ROI, c surface rendering of the volume masked with normalized filtering, d filtered 2D slice from (b)

Fig. 10

Triangulation S of the anatomical surface S⁰

Fig. 11

The average normal in vertex S_i,j (left) and construction of upper triangulation Θ_t (right)

Fig. 12

Quasi-block partition element Ω_{i, j} of the anterior layer L