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

通过替换 MRI 中的面部图像来保护阿尔茨海默病和相关痴呆症研究参与者的机密

• 批准号: 10294027
• 负责人: Christopher George Schwarz
• 金额: $ 79.41万
• 依托单位: MAYO CLINIC ROCHESTER
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-01 至 2025-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10294027
• 关键词: Address; Administrator; Adoption; Age; Aging; Agreement; Algorithms; Alzheimer' s Disease; Alzheimer' s disease related dementia; Amyloid; Bioethics; Biological Markers; Brain; Brain imaging; Clinic; Collaborations; Computer software; Consent; Data; Data Set; Diagnosis; Diffusion; Education; Educational Materials; Ensure; Event; Face; Funding; Head; Health; High Resolution Computed Tomography; Image; Imagery; Individual; Inferior; Legal; Link; Magnetic Resonance Imaging; Measurement; Measures; Medical Research; Metabolism; Methods; Modality; Modernization; Modification; Names; Noise; Participant; Perception; Performance; Perfusion; Policies; Population; Population Heterogeneity; Positron-Emission Tomography; Predisposition; Privacy; Public Participation; Publishing; Race; Recommendation; Research; Resolution; Risk; Sampling; Scanning; Subgroup; Techniques; Technology; Testing; Tracer; Validation; Work; X-Ray Computed Tomography; age effect; base; data sharing; demographics; digital; facial transplantation; genetic risk factor; imaging biomarker; imaging modality; imaging study; improved; neuroimaging; neuropsychiatry; next generation; novel; prevent; programs; public trust; reconstruction; research study; sex; software development; study population; tau Proteins

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.

项目概要/摘要 人们越来越需要共享所有公共资助的研究数据，包括磁共振 同时，已经表明可以生成高分辨率的面部重建。 来自 MRI 的图像，面部识别软件可以将这些重建结果与参与者的标准照片进行匹配。 MRI 去识别化会从图像标题中删除参与者姓名，但不会阻止面部识别 识别出的个人研究参与者将与所有收集到的信息不可逆转地联系在一起。 受保护的健康信息，例如诊断、生物标志物结果、遗传风险因素和神经精神信息 尽管数据使用协议可以合法地保护研究管理员，但这些法律机制并不能。 如果参与者被粗心或恶意的个人公开识别，这将直接保护参与者。 事件将永久削弱公众对许多大型医学研究的信任和参与。 阿尔茨海默病 (AD) 和相关痴呆症的影像学研究很容易受到这种威胁。 为了解决这一威胁，我们提出了一种新技术，通过用 与删除或模糊面部的现有方法不同，我们的方法是通用的、普通的面部（即数字面部“移植”）。 方法通过产生去识别的信息，最大限度地减少成像生物标志物测量中增加的偏差和噪声 类似于自然图像的 MRI 技术，随着技术的发展和技术的不断发展，这种迫在眉睫的隐私威胁也在不断增长。 我们建议：通过以下方式改进我们的去识别化软件。 与人脸识别领域的顶级专家合作；进一步减少对大规模大脑测量的影响； 对 Mayo Clinic 老化研究进行测试/验证；添加去污附加成像模式的功能； 提高应用于不同人群时的性能；并免费共享软件以供研究使用。 目标 1：细化并验证优化的人脸去识别算法：1A) 进一步改进去识别 识别性能；1B) 进一步减少对大脑生物标志物测量的影响；1C) 测试和 使用梅奥诊所衰老研究和阿尔茨海默病研究中心的图像进行验证。 目标 2：添加去识别附加成像序列和模式的功能：2A) 支持 附加 MRI 序列；2B) 支持 PET 图像；2C) 支持 CT 图像。 目标 3：调查年龄、种族和性别的影响：3A) 评估年龄、种族和性别对 提出的去识别化方法；3B）调整软件以确保算法保护所有参与者 平等地。 目标 4：传播软件和教育材料：4A) 免费共享软件以供研究使用；4B) 开发和传播研究材料和建议以保护参与者 隐私。