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

项目摘要/摘要 共享所有公共资助的研究数据的需求日益增长，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 来自核磁共振的图像，面部识别软件可以将这些重建图像与参与者的照片进行匹配。标准 核磁共振去身份识别会从图像标题中删除参与者的姓名，但不会阻止人脸 承认。确定的单个研究参与者将不可逆转地与所有收集的 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病学 测试。虽然数据使用协议可以合法地保护研究管理员，但这些法律机制不能 直接保护参与者。如果参与者是由粗心大意或恶意的个人公开确认的，这 这一事件将严重和永久性地侵蚀公众对医学研究的信任和参与。许多大型 阿尔茨海默病(AD)和相关痴呆症的成像研究很容易受到这种威胁的影响。 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 普通的、普通的脸部(即数字脸部“移植”)。与现有的移除或模糊面部的方法不同，我们的 一种方法，通过产生去识别的 类似于自然图像的核磁共振成像。这种迫在眉睫的隐私威胁随着蓬勃发展的技术和 增加研究数据的公开共享。我们建议：通过以下方式改进我们的身份识别软件 与人脸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 对Mayo诊所衰老研究进行测试/验证；添加面向其他成像设备的功能；测试和 提高应用于不同人群时的性能；并免费共享该软件以供研究使用。 目标1：改进和验证优化的人脸识别算法：1)进一步改进人脸识别算法 识别性能；1B)进一步减少对脑生物标记物测量的影响；1C)测试和 使用梅奥诊所老龄化和阿尔茨海默病研究中心研究的图像进行验证。 目标2：增加识别其他成像序列和方式的能力：2)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 提出的去身份方法；3B)采用软件，确保算法保护所有参与者 同样如此。 目标4：传播软件和教育材料：4A)免费共享软件以供研究使用；4B) 为保护参与者制定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.