Details

Autor(en) / Beteiligte

• Schwarz, Christopher G.
• Kremers, Walter K.
• Arani, Arvin
• Savvides, Marios
• Reid, Robert I.
• Gunter, Jeffrey L.
• Senjem, Matthew L.
• Cogswell, Petrice M.
• Vemuri, Prashanthi
• Kantarci, Kejal
• Knopman, David S.
• Petersen, Ronald C.
• Jack, Clifford R.

Titel

A face-off of MRI research sequences by their need for de-facing

Ist Teil von

• NeuroImage (Orlando, Fla.), 2023-08, Vol.276, p.120199-120199, Article 120199

Ort / Verlag

United States: Elsevier Inc

Erscheinungsjahr

2023

Link zum Volltext

Quelle

MEDLINE

Beschreibungen/Notizen

• •3D T1-w, 3D T2-w, and 3D T2-FLAIR were all highly re-identifiable (96–98%).•2D T2-FLAIR and 3D multi-echo GRE were each moderately re-identifiable (44–45%).•Diffusion, functional and ASL were each minimally re-identifiable (0–8%).•mri_reface reduced all re-identification rates to ≤8%.•mri_reface effects on brain measurements were all below or similar to scan-rescan. It is now widely known that research brain MRI, CT, and PET images may potentially be re-identified using face recognition, and this potential can be reduced by applying face-deidentification (“de-facing”) software. However, for research MRI sequences beyond T1-weighted (T1-w) and T2-FLAIR structural images, the potential for re-identification and quantitative effects of de-facing are both unknown, and the effects of de-facing T2-FLAIR are also unknown. In this work we examine these questions (where applicable) for T1-w, T2-w, T2*-w, T2-FLAIR, diffusion MRI (dMRI), functional MRI (fMRI), and arterial spin labelling (ASL) sequences. Among current-generation, vendor-product research-grade sequences, we found that 3D T1-w, T2-w, and T2-FLAIR were highly re-identifiable (96–98%). 2D T2-FLAIR and 3D multi-echo GRE (ME-GRE) were also moderately re-identifiable (44–45%), and our derived T2* from ME-GRE (comparable to a typical 2D T2*) matched at only 10%. Finally, diffusion, functional and ASL images were each minimally re-identifiable (0–8%). Applying de-facing with mri_reface version 0.3 reduced successful re-identification to ≤8%, while differential effects on popular quantitative pipelines for cortical volumes and thickness, white matter hyperintensities (WMH), and quantitative susceptibility mapping (QSM) measurements were all either comparable with or smaller than scan-rescan estimates. Consequently, high-quality de-facing software can greatly reduce the risk of re-identification for identifiable MRI sequences with only negligible effects on automated intracranial measurements. The current-generation echo-planar and spiral sequences (dMRI, fMRI, and ASL) each had minimal match rates, suggesting that they have a low risk of re-identification and can be shared without de-facing, but this conclusion should be re-evaluated if they are acquired without fat suppression, with a full-face scan coverage, or if newer developments reduce the current levels of artifacts and distortion around the face.