MRI-Based Attenuation Correction for Whole-Body PET/MRI: Quantitative Evaluation of Segmentation- and Atlas-Based Methods
MRI-Based Attenuation Correction for Whole-Body PET/MRI: Quantitative Evaluation of Segmentation- and Atlas-Based Methods
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DOI：
10.2967/jnumed.110.078949
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发表时间：
2011-09-01
影响因子：
9.3
通讯作者：
Schoelkopf, Bernhard
中科院分区：
文献类型：
--
作者：
Hofmann, Matthias;Bezrukov, Ilja;Schoelkopf, Bernhard
PET/MRI是一种新兴的双模态成像技术，需要新的PET衰减校正（AC）方法。我们评估了2种基于全身MRI的AC（MRAC）算法：一种基本的MR图像分割算法和一种基于图谱配准和模式识别（AT&PR）的方法。方法：11例患者均接受了全身PET/CT研究和单独的多床全身MRI研究。MR图像分割算法使用图像阈值、狄克逊脂肪-水分割和成分分析的组合来检测肺部。MR图像被分割成5个组织类别（不包括骨），每个类别被分配一个默认的线性衰减值。AT&PR算法使用先前对齐的MRI/CT图像体积对的数据库。对于每个患者，这些对被配准到患者MRI体积，并且机器学习技术被用于在连续尺度上预测衰减值。通过使用正常器官和病变中的感兴趣体积对AC PET图像进行定量分析，对MRAC方法进行比较。我们假设基于CT的AC后的PET/CT值为参考标准。结果如下：在正常生理摄取区域，分割和AT&PR方法的平均标准化摄取值的平均误差分别为14.1% +/- 10.2%和7.7% +/- 8.4%。基于病变的错误为7.5% +/- 7.9%的分割方法和5.7% +/- 4.7%的AT&PR方法。结论：使用AT&PR的MRAC方法提供了比基本MR图像分割方法更好的整体PET量化准确性。这种更好的量化是由于关于骨内或骨附近的感兴趣体积的误差量显著减少以及关于肺外区域的误差量略微减少。
PET/MRI is an emerging dual-modality imaging technology that requires new approaches to PET attenuation correction (AC). We assessed 2 algorithms for whole-body MRI-based AC (MRAC): a basic MR image segmentation algorithm and a method based on atlas registration and pattern recognition (AT&PR). Methods: Eleven patients each underwent a whole-body PET/CT study and a separate multibed whole-body MRI study. The MR image segmentation algorithm uses a combination of image thresholds, Dixon fat-water segmentation, and component analysis to detect the lungs. MR images are segmented into 5 tissue classes (not including bone), and each class is assigned a default linear attenuation value. The AT&PR algorithm uses a database of previously aligned pairs of MRI/CT image volumes. For each patient, these pairs are registered to the patient MRI volume, and machine-learning techniques are used to predict attenuation values on a continuous scale. MRAC methods are compared via the quantitative analysis of AC PET images using volumes of interest in normal organs and on lesions. We assume the PET/CT values after CT-based AC to be the reference standard. Results: In regions of normal physiologic uptake, the average error of the mean standardized uptake value was 14.1% +/- 10.2% and 7.7% +/- 8.4% for the segmentation and the AT&PR methods, respectively. Lesion-based errors were 7.5% +/- 7.9% for the segmentation method and 5.7% +/- 4.7% for the AT&PR method. Conclusion: The MRAC method using AT&PR provided better overall PET quantification accuracy than the basic MR image segmentation approach. This better quantification was due to the significantly reduced volume of errors made regarding volumes of interest within or near bones and the slightly reduced volume of errors made regarding areas outside the lungs.