MR-based attenuation correction for PET/MRI neurological studies with continuous-valued attenuation coefficients for bone through a conversion from R2* to CT-Hounsfield units.
MR-based attenuation correction for PET/MRI neurological studies with continuous-valued attenuation coefficients for bone through a conversion from R2* to CT-Hounsfield units.
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DOI：
10.1016/j.neuroimage.2015.03.009
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发表时间：
2015-05-15
期刊：
影响因子：
5.7
通讯作者：
An H
中科院分区：
文献类型：
--
作者：
Juttukonda MR;Mersereau BG;Chen Y;Su Y;Rubin BG;Benzinger TLS;Lalush DS;An H
PET/MRI中基于AimMR的光子衰减校正仍然具有挑战性，特别是对于需要定量数据的神经学应用。现有的方法要么不够准确，要么受到所需计算时间的限制。本研究的目的是开发一种基于MR的衰减校正方法，准确地将骨组织从空气中分离出来，并提供连续值的衰减系数bone.Materials和methodsPET/MRI和CT数据集获得98个主题（平均年龄[± SD]：66岁[± 9.8]，57名女性）使用IRB批准的协议和知情同意。受试者注射352 ± 29 MBq的18F-Florbetapir示踪剂，注射后立即或50分钟开始PET采集。使用PET/CT系统单独采集头部的CT图像。采集双回波超短回波时间（UTE）图像和两点狄克逊图像。通过UTE回波1图像的逐体素乘法逆的阈值分割空气区域。通过从UTE回波1和UTE回波2图像计算的R2* 图像的阈值分割骨区域。使用从狄克逊图像分解的脂肪和水图像分割脂肪和软组织区域。空气、脂肪和软组织的线性衰减系数（拉克）分别为0、0.092和0.1 cm− 1。骨的拉克来自相应R2* 和CT值之间的回归分析。PET图像重建使用金标准CT方法和建议的CAR-RiDR method.ResultsThe RiDR分割方法产生的平均Dice系数± SD跨科目的0.75 ± 0.05骨和0.60 ± 0.08的空气。与使用恒定CT值（46.9% ± 5.8，p < 10− 6）相比，骨拉克的CAR模型大大提高了估计CT值的准确性（28.2% ± 3.0平均误差）。最后，CAR-RiDR方法在受试者的PET重建中提供了较低的全脑平均绝对误差（MAPE ± SD），为2.55% ± 0.86。区域PET误差也很低，范围从0.88%到3.79%，在24个脑ROIS.ConclusionWe提出了一个基于MR的衰减校正方法（CAR-RiDR）定量PET神经成像。所提出的方法采用UTE和狄克逊图像，并由两个新的组件：1）准确分割的空气和骨的逆的UTE 1图像和R2* 图像，分别和2）估计连续LAC值的骨使用R2* 和CT亨氏单位之间的回归。从我们的分析中，我们得出结论，所提出的方法接近（< 3%的误差）的黄金标准CT缩放方法的PET重建精度。
AimMR-based correction for photon attenuation in PET/MRI remains challenging, particularly for neurological applications requiring quantitation of data. Existing methods are either not sufficiently accurate or are limited by the computation time required. The goal of this study was to develop an MR-based attenuation correction method that accurately separates bone tissue from air and provides continuous-valued attenuation coefficients for bone.Materials and methodsPET/MRI and CT datasets were obtained from 98 subjects (mean age [± SD]: 66 yrs [± 9.8], 57 females) using an IRB-approved protocol and with informed consent. Subjects were injected with 352 ± 29 MBq of18F-Florbetapir tracer, and PET acquisitions were begun either immediately or 50 min after injection. CT images of the head were acquired separately using a PET/CT system. Dual echo ultrashort echo-time (UTE) images and two-point Dixon images were acquired. Regions of air were segmented via a threshold of the voxel-wise multiplicative inverse of the UTE echo 1 image. Regions of bone were segmented via a threshold of the R2* image computed from the UTE echo 1 and UTE echo 2 images. Regions of fat and soft tissue were segmented using fat and water images decomposed from the Dixon images. Air, fat, and soft tissue were assigned linear attenuation coefficients (LACs) of 0, 0.092, and 0.1 cm− 1, respectively. LACs for bone were derived from a regression analysis between corresponding R2* and CT values. PET images were reconstructed using the gold standard CT method and the proposed CAR-RiDR method.ResultsThe RiDR segmentation method produces mean Dice coefficient ± SD across subjects of 0.75 ± 0.05 for bone and 0.60 ± 0.08 for air. The CAR model for bone LACs greatly improves accuracy in estimating CT values (28.2% ± 3.0 mean error) compared to the use of a constant CT value (46.9% ± 5.8, p < 10− 6). Finally, the CAR-RiDR method provides a low whole-brain mean absolute percent-error (MAPE ± SD) in PET reconstructions across subjects of 2.55% ± 0.86. Regional PET errors were also low and ranged from 0.88% to 3.79% in 24 brain ROIs.ConclusionWe propose an MR-based attenuation correction method (CAR-RiDR) for quantitative PET neurological imaging. The proposed method employs UTE and Dixon images and consists of two novel components: 1) accurate segmentation of air and bone using the inverse of the UTE1 image and the R2* image, respectively and 2) estimation of continuous LAC values for bone using a regression between R2* and CT-Hounsfield units. From our analysis, we conclude that the proposed method closely approaches (< 3% error) the gold standard CT-scaled method in PET reconstruction accuracy.
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影响因子：
10.6
作者：
Burgos, Ninon;Cardoso, M. Jorge;Ourselin, Sebastien
通讯作者：
Ourselin, Sebastien
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：
9.3
作者：
Hofmann, Matthias;Steinke, Florian;Pichler, Bernd J.
通讯作者：
Pichler, Bernd J.
影响因子：
2.9
作者：
Gottschalk, PG;Dunn, JR
通讯作者：
Dunn, JR
DOI：
10.1007/s00259-011-1842-9
发表时间：
2011-09-01
影响因子：
9.1
作者：
Eiber, Matthias;Martinez-Moeller, Axel;Beer, Ambros J.
通讯作者：
Beer, Ambros J.