MRI scans provide valuable medical information, however they also contain sensitive and personally identifiable information (PII) that needs to be protected. Whereas MRI metadata is easily sanitized, MRI image data is a privacy risk because it contains information to render highly-realistic 3D visualizations of a patient's head, enabling malicious actors to possibly identify the subject by cross-referencing a database. Data anonymization and de-identification is concerned with ensuring the privacy and confidentiality of individuals' personal information. Traditional MRI de-identification methods remove privacy-sensitive parts (e.g. eyes, nose etc.) from a given scan. This comes at the expense of introducing a domain shift that can throw off downstream analyses. Recently, a GAN-based approach was proposed to de-identify a patient's scan by remodeling it (\eg changing the face) rather than by removing parts. In this work, we propose CP-MAE, a model that de-identifies the face using masked autoencoders and that outperforms all previous approaches in terms of downstream task performance as well as de-identification. With our method we are able to synthesize scans of resolution up to $256^3$ (previously $128^3$) which constitutes an eight-fold increase in the number of voxels. Using our construction we were able to design a system that exhibits a highly robust training stage, making it easy to fit the network on novel data.

翻译：MRI扫描能提供宝贵的医疗信息，但也包含需要保护的个人身份信息（PII）。尽管MRI元数据易于清洗，但MRI图像数据存在隐私风险，因为其中包含可重建患者头部高逼真度3D可视化的信息，恶意行为者可能通过交叉比对数据库来识别受试者。数据匿名化和去标识化旨在确保个人信息的隐私和保密性。传统MRI去标识化方法会从扫描中移除隐私敏感部分（如眼睛、鼻子等），但会引入领域偏移，影响下游分析。近期有研究提出基于生成对抗网络的方法，通过重塑（例如改变面部）而非移除部分来实现患者扫描的去标识化。本研究提出CP-MAE模型，利用掩码自编码器对面部进行去标识化，在下游任务性能和去标识化效果上均超越此前所有方法。利用该方法可合成分辨率高达$256^3$（此前为$128^3$）的扫描图像，相当于体素数量增加八倍。通过该架构，我们设计的系统具有高度鲁棒的训练阶段，便于在新数据上拟合网络。