Department of Radiology, Sengkang General Hospital, Singhealth, 110 Sengkang E Way, Singapore, 544886, Singapore.
Clinical Data Analytics & Radiomics Group, Bioinformatics Institute, Agency for Science, Technology and Research, Singapore, 30 Biopolis St, Matrix, Singapore, 138671, Singapore.
MAGMA. 2024 Dec;37(6):993-1003. doi: 10.1007/s10334-024-01170-x. Epub 2024 Jun 21.
Defacing research MRI brain scans is often a mandatory step. With current defacing software, there are issues with Windows compatibility and researcher doubt regarding the adequacy of preservation of brain voxels in non-T1w scans. To address this, we developed PyFaceWipe, a multiplatform software for multiple MRI contrasts, which was evaluated based on its anonymisation ability and effect on downstream processing.
Multiple MRI brain scan contrasts from the OASIS-3 dataset were defaced with PyFaceWipe and PyDeface and manually assessed for brain voxel preservation, remnant facial features and effect on automated face detection. Original and PyFaceWipe-defaced data from locally acquired T1w structural scans underwent volumetry with FastSurfer and brain atlas generation with ANTS.
214 MRI scans of several contrasts from OASIS-3 were successfully processed with both PyFaceWipe and PyDeface. PyFaceWipe maintained complete brain voxel preservation in all tested contrasts except ASL (45%) and DWI (90%), and PyDeface in all tested contrasts except ASL (95%), BOLD (25%), DWI (40%) and T2* (25%). Manual review of PyFaceWipe showed no failures of facial feature removal. Pinna removal was less successful (6% of T1 scans showed residual complete pinna). PyDeface achieved 5.1% failure rate. Automated detection found no faces in PyFaceWipe-defaced scans, 19 faces in PyDeface scans compared with 78 from the 224 original scans. Brain atlas generation showed no significant difference between atlases created from original and defaced data in both young adulthood and late elderly cohorts. Structural volumetry dice scores were ≥ 0.98 for all structures except for grey matter which had 0.93. PyFaceWipe output was identical across the tested operating systems.
PyFaceWipe is a promising multiplatform defacing tool, demonstrating excellent brain voxel preservation and competitive defacing in multiple MRI contrasts, performing favourably against PyDeface. ASL, BOLD, DWI and T2* scans did not produce recognisable 3D renders and hence should not require defacing. Structural volumetry dice scores (≥ 0.98) were higher than previously published FreeSurfer results, except for grey matter which were comparable. The effect is measurable and care should be exercised during studies. ANTS atlas creation showed no significant effect from PyFaceWipe defacing.
通常，损坏研究性磁共振成像（MRI）脑扫描是一项强制性步骤。使用当前的损坏软件，存在与 Windows 兼容性问题，并且研究人员对非 T1w 扫描中脑体素的保留程度存在疑问。为了解决这个问题，我们开发了 PyFaceWipe，这是一种用于多种 MRI 对比的跨平台软件，我们根据其匿名化能力和对下游处理的影响对其进行了评估。
使用 PyFaceWipe 和 PyDeface 对 OASIS-3 数据集的多个 MRI 脑扫描对比进行了损坏处理，并手动评估了脑体素的保留情况、残留的面部特征以及对自动面部检测的影响。使用 FastSurfer 对来自本地获取的 T1w 结构扫描的原始和 PyFaceWipe 损坏的数据进行了体积测量，并使用 ANTS 生成了脑图谱。
成功处理了 OASIS-3 的多个对比的 214 个 MRI 扫描，使用 PyFaceWipe 和 PyDeface 均可完成。除了动脉自旋标记（ASL）（45%）和弥散加权成像（DWI）（90%）外，PyFaceWipe 在所有测试对比中均能完全保留脑体素，而 PyDeface 在除了 ASL（95%）、血氧水平依赖（BOLD）（25%）、DWI（40%）和 T2*（25%）外的所有测试对比中均能保留。手动检查 PyFaceWipe 显示面部特征去除没有失败。耳郭去除效果不太理想（6%的 T1 扫描显示仍存在完整的耳郭）。PyDeface 的失败率为 5.1%。自动检测未在 PyFaceWipe 损坏的扫描中发现任何面部，而在 PyDeface 扫描中发现了 19 个面部，而在 224 个原始扫描中发现了 78 个。在年轻成年和老年晚期队列中，从原始和损坏数据创建的脑图谱之间没有显著差异。除了灰质的 Dice 评分为 0.93 外，所有结构的结构体积测量 Dice 评分均≥0.98。PyFaceWipe 在测试的操作系统上输出结果一致。
PyFaceWipe 是一种很有前途的跨平台损坏工具，在多种 MRI 对比中表现出出色的脑体素保留和有竞争力的损坏效果，与 PyDeface 相比表现出色。ASL、BOLD、DWI 和 T2* 扫描不会产生可识别的 3D 渲染效果，因此不需要进行损坏处理。结构体积测量 Dice 评分（≥0.98）高于先前发表的 FreeSurfer 结果，除了灰质的评分与之前的结果相当。这种效果是可测量的，在研究过程中应谨慎使用。ANTS 图谱创建显示 PyFaceWipe 损坏没有显著影响。
