Abstract
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.
Abstract (translated)
头部磁共振成像（MRI）通常在严格的监管框架下收集和共享用于研究。这些框架要求在分享数据前移除潜在的身份标识符。然而，即使去除了颅骨信息后，脑实质中仍然包含独特的特征，这些特征可以在不同数据库中的同一参与者的其他MRI图像之间进行匹配，从而构成隐私风险，特别是当有额外的数据特性可用时。现有的监管框架通常规定需要基于某一合理水平的评估来评定此类风险。 先前的研究已经表明，通过脑部MRI可以实现参与者之间的关联，但它们依赖于训练基或计算密集型方法。在这里，我们展示了使用标准预处理后进行图像相似性计算的方法，可以将去除了颅骨信息后的T1加权MRI与个人匹配起来，即使在有其他身份标识符可用的情况下也可能导致重新识别的问题。 我们在跨不同时间间隔、扫描类型、空间分辨率和采集协议的数据样本中实现了近乎完美的链接准确性，即便考虑到潜在的认知衰退。这些结果模拟了跨数据库进行MRI匹配的情况，并且旨在为医疗数据共享的发展提供有意义的贡献，制定更加周到前瞻性的政策。
URL
https://arxiv.org/abs/2602.10043
