Protecting the Confidentiality of Participants in Studies of Alzheimer's Disease and Related Dementias by Replacing Face Imagery in MRI

项目总结/摘要 共享所有公共资助的研究数据的需求不断增长，包括磁共振 图像（MRI）。同时，已经表明可以生成高分辨率的面部重建 面部识别软件可以将这些重建与参与者的照片进行匹配。标准 MRI去识别从图像标题中删除参与者姓名，但不会阻止面部识别 识别.确定的个人研究参与者将与所有收集的信息不可逆转地联系起来。 受保护的健康信息，如诊断、生物标志物结果、遗传风险因素和神经精神疾病 试验.尽管数据使用协议可以在法律上保护研究管理员，但这些法律的机制不能 直接保护参与者。如果参与者被粗心或恶意的个人公开识别， 事件将严重和永久地削弱公众对医学研究的信任和参与。许多大型 阿尔茨海默病（AD）和相关痴呆的成像研究易受这种威胁。 为了解决这一威胁，我们提出了一种新的技术，通过将面部图像替换为 一般的、平均的脸（即，数字面部“移植”）。与现有的去除或模糊面部的方法不同，我们的 该方法通过产生去识别的生物标记来最小化成像生物标记测量中的附加偏差和噪声 MRI类似于自然图像。这种迫在眉睫的隐私威胁随着新兴技术的发展和 增加研究数据的公共共享。我们建议： 与面部识别领域的顶级专家合作;进一步减少对大脑测量的影响;大规模 在马约诊所老化研究中进行测试/确认;增加对其他成像模式进行破坏的能力;测试和 在应用于不同人群时提高性能;并免费共享软件以供研究使用。 目的1：改进和验证一种优化的人脸去识别算法：1A）进一步改进去识别算法， 识别性能; 1B）进一步减少对脑生物标志物测量的影响; 1C）测试和 使用马约诊所衰老研究和阿尔茨海默病研究中心的研究图像进行验证。 目标2：增加去识别其他成像序列和模态的功能：2A）支持 附加MRI序列; 2B）支持PET图像; 2C）支持CT图像。 目的3：调查年龄、种族和性别的影响：3A）评估年龄、种族和性别对 3B）调整软件以确保算法保护所有参与者 平等地 目标4：传播软件和教材：4A）免费分享软件用于研究; 4 B） 为保护参与者编写和传播研究材料和建议 隐私.

PROJECT SUMMARY / ABSTRACT There exists a growing demand to share all publicly-funded research data, including magnetic resonance images (MRI). Concurrently, it has been shown that high-resolution facial reconstructions can be generated from MRI, and face recognition software can match these reconstructions with participant photos. Standard MRI de-identification removes participant names from the image header, but does nothing to prevent face recognition. Identified individual research participants would be irreversibly linked with all the collected protected health information, such as diagnoses, biomarker results, genetic risk factors, and neuropsychiatric testing. Although data use agreements can legally protect study administrators, these legal mechanisms do not directly protect participants. If participants were publicly identified by a careless or malicious individual, this event would significantly and permanently erode public trust and participation in medical research. Many large imaging studies of Alzheimer's Disease (AD) and related dementias are vulnerable to this threat. To address this threat, we propose a novel technique that de-identifies MRI by replacing facial imagery with a generic, average face (i.e., a digital face “transplant”). Unlike existing methods that remove or blur faces, our approach minimizes added bias and noise in imaging biomarker measurements by producing a de-identified MRI that resembles a natural image. This imminent privacy threat grows with burgeoning technology and with the increased public sharing of research data. We propose to: improve our de-identification software by collaborating with a top expert in face recognition; further reduce effects on brain measurements; large-scale test/validate on Mayo Clinic aging studies; add capability for de-facing additional imaging modalities; test and improve performance when applied to diverse populations; and share the software freely for research use. Aim 1: Refine and validate an optimized face de-identification algorithm: 1A) Further improve de- identification performance; 1B) Further reduce impacts on brain biomarker measurements; 1C) Test and validate using images from the Mayo Clinic Study of Aging and Alzheimer's Disease Research Center studies. Aim 2: Add capability for de-identifying additional imaging sequences and modalities: 2A) Support additional MRI sequences; 2B) Support PET images; 2C) Support CT images. Aim 3: Investigate effects of age, race, and sex: 3A) Evaluate the effects of age, race, and sex on the proposed de-identification method; 3B) Adapt software to ensure that the algorithm protects all participants equally. Aim 4: Disseminate software and educational materials: 4A) Share the software freely for research use; 4B) Develop and disseminate materials and recommendations for research studies for protection of participant privacy.