Head Magnetic Resonance Imaging (MRI) is routinely collected and shared for research under strict regulatory frameworks. These frameworks require removing potential identifiers before sharing. But, even after skull stripping, the brain parenchyma contains unique signatures that can match other MRIs from the same participants across databases, posing a privacy risk if additional data features are available. Current regulatory frameworks often mandate evaluating such risks based on the assessment of a certain level of reasonableness. Prior studies have already suggested that a brain MRI could enable participant linkage, but they have relied on training-based or computationally intensive methods. Here, we demonstrate that linking an individual's skull-stripped T1-weighted MRI, which may lead to re-identification if other identifiers are available, is possible using standard preprocessing followed by image similarity computation. Nearly perfect linkage accuracy was achieved in matching data samples across various time intervals, scanner types, spatial resolutions, and acquisition protocols, despite potential cognitive decline, simulating MRI matching across databases. These results aim to contribute meaningfully to the development of thoughtful, forward-looking policies in medical data sharing.

翻译：头部磁共振成像（MRI）在严格监管框架下被常规采集并共享用于研究。这些框架要求在共享前移除潜在标识符。然而，即使进行颅骨剥离后，脑实质仍包含独特的特征，能够跨数据库匹配同一参与者的其他MRI影像，若存在其他数据特征则可能构成隐私风险。现行监管框架通常要求基于合理程度的评估来判定此类风险。先前研究已表明脑MRI可能实现参与者关联，但这些研究依赖于基于训练或计算密集型方法。本文证明，通过标准预处理及随后的图像相似性计算，能够实现个体颅骨剥离T1加权MRI的跨数据库关联（若存在其他标识符则可能导致再识别）。尽管存在潜在的认知衰退，我们在模拟跨数据库MRI匹配时，在不同时间间隔、扫描仪类型、空间分辨率和采集协议下均实现了近乎完美的关联准确率。这些结果旨在为制定审慎且具有前瞻性的医疗数据共享政策提供有意义的参考。