Defacing biases manual and automated quality assessments of structural MRI with MRIQC
Year: 2022
Type: preprint
Abstract: Defacing (i.e. removing facial features) from structural imaging has become a necessary step before data sharing to ensure participants’ anonymity (Schwarz et al. 2021; Fig 1A). This process has proven to have some deleterious effects on the downstream research workflow (de Sitter et al. 2020). Here, we present an exploratory analysis prior to testing the hypothesis that both quality ratings by human experts and the image quality metrics (IQMs) that MRIQC (Esteban et al. 2017) extracts are affected by defacing. We found sufficient evidence on a small sample that there might be an effect. Therefore, we will pre-register and carry out a confirmatory analysis on a larger, unseen, sample.
Language: English
Subfield: Radiology, Nuclear Medicine and Imaging
Field: Medicine
Domain: Health Sciences
Open Access status: green
OpenAlex ID: W4206995297