全身 PET/CT 中的面部匿名化和隐私问题。

IF 9.1 1区 医学 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING
Journal of Nuclear Medicine Pub Date : 2023-08-01 Epub Date: 2023-06-02 DOI:10.2967/jnumed.122.265280
Aaron R Selfridge, Benjamin A Spencer, Yasser G Abdelhafez, Keisuke Nakagawa, John D Tupin, Ramsey D Badawi
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引用次数: 0

摘要

全身正电子发射计算机断层显像/计算机断层扫描(PET/CT)图像经渲染后可生成受试者的面部和身体图像。为了解决共享数据时的隐私和可识别性问题,我们开发并验证了一种在三维容积数据中模糊(玷污)受试者面部的工作流程。方法:为了验证我们的方法,我们测量了30名健康受试者在3个或6个时间点同时接受[18F]FDG PET和CT成像的图像去污前后的面部可识别性。简而言之,我们使用谷歌 FaceNet 计算了面部嵌入,并使用聚类分析估算了可识别性。结果:通过 CT 图像呈现的人脸与其他时间点的 CT 扫描的正确匹配率为 93%,而在污损后,正确匹配率降至 6%。通过 PET 图像呈现的人脸与其他时间点的 PET 图像的正确匹配率最高为 64%,与 CT 图像的正确匹配率最高为 50%,两者在污损后均降至 7%。我们进一步证明,玷污的 CT 图像可用于 PET 重建过程中的衰减校正,在最靠近面部的大脑皮层区域引入的最大偏差为 -3.3%。结论我们相信,所提出的方法为在线或机构间共享图像数据提供了一个匿名和谨慎的基线,并将有助于促进合作和未来的合规性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Facial Anonymization and Privacy Concerns in Total-Body PET/CT.

Total-body PET/CT images can be rendered to produce images of a subject's face and body. In response to privacy and identifiability concerns when sharing data, we have developed and validated a workflow that obscures (defaces) a subject's face in 3-dimensional volumetric data. Methods: To validate our method, we measured facial identifiability before and after defacing images from 30 healthy subjects who were imaged with both [18F]FDG PET and CT at either 3 or 6 time points. Briefly, facial embeddings were calculated using Google's FaceNet, and an analysis of clustering was used to estimate identifiability. Results: Faces rendered from CT images were correctly matched to CT scans at other time points at a rate of 93%, which decreased to 6% after defacing. Faces rendered from PET images were correctly matched to PET images at other time points at a maximum rate of 64% and to CT images at a maximum rate of 50%, both of which decreased to 7% after defacing. We further demonstrated that defaced CT images can be used for attenuation correction during PET reconstruction, introducing a maximum bias of -3.3% in regions of the cerebral cortex nearest the face. Conclusion: We believe that the proposed method provides a baseline of anonymity and discretion when sharing image data online or between institutions and will help to facilitate collaboration and future regulatory compliance.

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来源期刊
Journal of Nuclear Medicine
Journal of Nuclear Medicine 医学-核医学
CiteScore
13.00
自引率
8.60%
发文量
340
审稿时长
1 months
期刊介绍: The Journal of Nuclear Medicine (JNM), self-published by the Society of Nuclear Medicine and Molecular Imaging (SNMMI), provides readers worldwide with clinical and basic science investigations, continuing education articles, reviews, employment opportunities, and updates on practice and research. In the 2022 Journal Citation Reports (released in June 2023), JNM ranked sixth in impact among 203 medical journals worldwide in the radiology, nuclear medicine, and medical imaging category.
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