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. Author manuscript; available in PMC: 2022 Sep 1.
Published in final edited form as: Neuroimage. 2022 Jun 3;258:119357. doi: 10.1016/j.neuroimage.2022.119357

Table 1.

Rates of automatically matching 5 photos of each participant to their correct corresponding imaging-based face reconstruction, using the Microsoft Azure Face API, before and after each de-facing technique.

Standard Face Reconstruction (using the input image only, with minimal preprocessing) Advanced Face Reconstruction (missing nose and mouth automatically replaced with those from an average template) After Re-facing with mri_reface
FLAIR MRI 178/182 (98%) N/A 15/182 (8%)
T1-w MRI 176/182 (97%) N/A 14/182 (8%)
Older FDG PET 44/129 (34%) 54/129 (42%) 0/129 (0%)
Older PiB PET 41/167 (25%) 54/167 (32%) 6/167 (4%)
Older Tau PET 48/167 (29%) 59/167 (35%) 3/167 (2%)
Newer FDG PET 14/14 (100%*) N/A 3/14 (21%)
Newer PiB PET 17/20 (85%*) N/A 3/20 (15%)
Newer Tau PET 18/19 (95%*) N/A 4/19 (21%)
CT (from older PET/CT) 131/167 (78%) N/A 8/167 (5%)

A * marks percentages with very low sample sizes that are likely overestimated and should not be directly compared with other rows.