Probabilistic Air Segmentation and Sparse Regression Estimated Pseudo CT for PET/MR Attenuation Correction.
Probabilistic Air Segmentation and Sparse Regression Estimated Pseudo CT for PET/MR Attenuation Correction.
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DOI：
10.1148/radiol.14140810
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发表时间：
2015-05
期刊：
影响因子：
19.7
通讯作者：
An H
中科院分区：
文献类型：
--
作者：
Chen Y;Juttukonda M;Su Y;Benzinger T;Rubin BG;Lee YZ;Lin W;Shen D;Lalush D;An H
通过从t1加权MR和atlas CT图像中估计伪CT图像，建立脑PET/磁共振（MR）成像正电子发射断层扫描（PET）衰减校正方法。在这项机构审查委员会批准并符合hipaa标准的研究中，在获得书面同意后获取了20名受试者的PET/MR/CT图像。提出了一种用于伪CT估计的概率空气分割稀疏回归（PASSR）方法。在概率空气图的帮助下进行空气分割。对于非空气区域，利用atlas MR patch稀疏回归估计伪CT数。PET图像的平均绝对百分比误差（MAPE）计算为一种方法与参考标准连续CT衰减校正方法之间PET信号强度的归一化平均绝对差。采用Friedman方差分析和Wilcoxon配对对检验对PASSR方法与Dixon分割、CT分割和总体平均CT图谱（平均图谱）方法的MAPE进行统计学比较。全脑、灰质和白质的平均MAPE±标准差分别为2.42%±1.0、3.28%±0.93和2.16%±1.75，显著低于Dixon、CT分割和图谱平均值（P < 0.01）。pasr对全脑容量的误差分别为68.0%±16.5、85.8%±12.9和96.0%±2.5，误差在±2%、±5%和±10%以内，显著高于其他方法（P < 0.01）。通过衰减校正减少PET误差，PASSR优于Dixon、CT分割和平均图谱方法。
To develop a positron emission tomography (PET) attenuation correction method for brain PET/magnetic resonance (MR) imaging by estimating pseudo computed tomographic (CT) images from T1-weighted MR and atlas CT images. In this institutional review board–approved and HIPAA-compliant study, PET/MR/CT images were acquired in 20 subjects after obtaining written consent. A probabilistic air segmentation and sparse regression (PASSR) method was developed for pseudo CT estimation. Air segmentation was performed with assistance from a probabilistic air map. For nonair regions, the pseudo CT numbers were estimated via sparse regression by using atlas MR patches. The mean absolute percentage error (MAPE) on PET images was computed as the normalized mean absolute difference in PET signal intensity between a method and the reference standard continuous CT attenuation correction method. Friedman analysis of variance and Wilcoxon matched-pairs tests were performed for statistical comparison of MAPE between the PASSR method and Dixon segmentation, CT segmentation, and population averaged CT atlas (mean atlas) methods. The PASSR method yielded a mean MAPE ± standard deviation of 2.42% ± 1.0, 3.28% ± 0.93, and 2.16% ± 1.75, respectively, in the whole brain, gray matter, and white matter, which were significantly lower than the Dixon, CT segmentation, and mean atlas values (P < .01). Moreover, 68.0% ± 16.5, 85.8% ± 12.9, and 96.0% ± 2.5 of whole-brain volume had within ±2%, ±5%, and ±10% percentage error by using PASSR, respectively, which was significantly higher than other methods (P < .01). PASSR outperformed the Dixon, CT segmentation, and mean atlas methods by reducing PET error owing to attenuation correction.
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DOI：
10.1007/s00259-012-2113-0
发表时间：
2012-07-01
影响因子：
9.1
作者：
Samarin, Andrei;Burger, Cyrill;Kuhn, Felix P.
通讯作者：
Kuhn, Felix P.
影响因子：
9.3
作者：
Hofmann, Matthias;Steinke, Florian;Pichler, Bernd J.
通讯作者：
Pichler, Bernd J.
影响因子：
9.3
作者：
Martinez-Moeller, Axel;Souvatzoglou, Michael;Nekolla, Stephan G.
通讯作者：
Nekolla, Stephan G.
影响因子：
3.8
作者：
Johansson, Adam;Karlsson, Mikael;Nyholm, Tufve
通讯作者：
Nyholm, Tufve
影响因子：
3.8
作者：
Fei, Baowei;Yang, Xiaofeng;Votaw, John R.
通讯作者：
Votaw, John R.