Impact of defacing on automated brain atrophy estimation.
Impact of defacing on automated brain atrophy estimation.
复制标题
DOI：
10.1186/s13244-022-01195-7
复制
发表时间：
2022-03-26
影响因子：
4.7
通讯作者：
Alzheimer’s Disease Neuroimaging Initiative
中科院分区：
文献类型：
--
作者：
Rubbert C;Wolf L;Turowski B;Hedderich DM;Gaser C;Dahnke R;Caspers J;Alzheimer’s Disease Neuroimaging Initiative
毁损已成为大脑MRI扫描匿名化的强制性要求;然而，对数据完整性的担忧也被提出。因此，我们系统地评估了不同毁损程序对自动脑萎缩估计的影响。ADNI共纳入268例阿尔茨海默病患者，其中包括非加速（n = 154），会话内非加速重复（n = 67）和加速3D T1成像（n = 114）。使用开源软件veganbagel计算每个原始的、未修改的扫描和使用afni_refacer、fsl_deface、mri_deface、mri_reface、PyDeface或spm_deface污损后的萎缩图，并计算z评分之间的均方根误差（RMSE）。来自未加速和未加速重复成像的RMSE值作为基准。离群值定义为RMSE >第75百分位数，并使用Grubbs检验。基准RMSE为0.28 ± 0.1（范围0.12-0.58，第75百分位数0.33）。使用第75百分位数临界值发现未加速和加速T1成像的离群值：afni_refacer（未加速：18，加速：16）、fsl_deface（未加速：4，加速：18）、mri_deface（未加速：0，加速：15）、mri_reface（未加速：0，加速：2）和spm_deface（未加速：0，加速：7）。PyDeface性能最佳，无离群值（未加速平均RMSE 0.08 ± 0.05，加速平均RMSE 0.07 ± 0.05）。根据Grubbs检验发现了以下离群值：afni_refacer（未加速：16，加速：13）、fsl_deface（未加速：10，加速：21）、mri_deface（未加速：7，加速：20）、mri_reface（未加速：7，加速：6）、PyDeface（未加速：5，加速：8）和spm_deface（未加速：10，加速：12）。大多数污损方法都会对萎缩估计产生影响，尤其是在加速3D T1成像中。仅PyDeface显示了良好的结果，对萎缩估计的影响可忽略不计。
Defacing has become mandatory for anonymization of brain MRI scans; however, concerns regarding data integrity were raised. Thus, we systematically evaluated the effect of different defacing procedures on automated brain atrophy estimation. In total, 268 Alzheimer’s disease patients were included from ADNI, which included unaccelerated (n = 154), within-session unaccelerated repeat (n = 67) and accelerated 3D T1 imaging (n = 114). Atrophy maps were computed using the open-source software veganbagel for every original, unmodified scan and after defacing using afni_refacer, fsl_deface, mri_deface, mri_reface, PyDeface or spm_deface, and the root-mean-square error (RMSE) between z-scores was calculated. RMSE values derived from unaccelerated and unaccelerated repeat imaging served as a benchmark. Outliers were defined as RMSE > 75th percentile and by using Grubbs’s test. Benchmark RMSE was 0.28 ± 0.1 (range 0.12–0.58, 75th percentile 0.33). Outliers were found for unaccelerated and accelerated T1 imaging using the 75th percentile cutoff: afni_refacer (unaccelerated: 18, accelerated: 16), fsl_deface (unaccelerated: 4, accelerated: 18), mri_deface (unaccelerated: 0, accelerated: 15), mri_reface (unaccelerated: 0, accelerated: 2) and spm_deface (unaccelerated: 0, accelerated: 7). PyDeface performed best with no outliers (unaccelerated mean RMSE 0.08 ± 0.05, accelerated mean RMSE 0.07 ± 0.05). The following outliers were found according to Grubbs’s test: afni_refacer (unaccelerated: 16, accelerated: 13), fsl_deface (unaccelerated: 10, accelerated: 21), mri_deface (unaccelerated: 7, accelerated: 20), mri_reface (unaccelerated: 7, accelerated: 6), PyDeface (unaccelerated: 5, accelerated: 8) and spm_deface (unaccelerated: 10, accelerated: 12). Most defacing approaches have an impact on atrophy estimation, especially in accelerated 3D T1 imaging. Only PyDeface showed good results with negligible impact on atrophy estimation.
登录
查看更多内容
DOI：
10.1016/j.nicl.2018.09.013
发表时间：
2018
期刊：
NeuroImage. Clinical
影响因子：
--
作者：
Klöppel S;Yang S;Kellner E;Reisert M;Heimbach B;Urbach H;Linn J;Weidauer S;Andres T;Bröse M;Lahr J;Lützen N;Meyer PT;Peter J;Abdulkadir A;Hellwig S;Egger K;Alzheimer's Disease Neuroimaging Initiative
通讯作者：
Alzheimer's Disease Neuroimaging Initiative
影响因子：
3.3
作者：
Griswold, MA;Jakob, PM;Haase, A
通讯作者：
Haase, A
影响因子：
5.7
作者：
Leung, Kelvin K.;Malone, Ian M.;Fox, Nick C.
通讯作者：
Fox, Nick C.
影响因子：
9
作者：
Fumagalli, Giorgio G.;Basilico, Paola;Galimberti, Daniela
通讯作者：
Galimberti, Daniela
影响因子：
5.9
作者：
Hedderich, Dennis M.;Dieckmeyer, Michael;Grimmer, Timo
通讯作者：
Grimmer, Timo