Clinical Assessment of Emission- and Segmentation-Based MR-Guided Attenuation Correction in Whole-Body Time-of-Flight PET/MR Imaging
Clinical Assessment of Emission- and Segmentation-Based MR-Guided Attenuation Correction in Whole-Body Time-of-Flight PET/MR Imaging
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DOI：
10.2967/jnumed.115.154807
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发表时间：
2015-06-01
影响因子：
9.3
通讯作者：
Zaidi, Habib
中科院分区：
文献类型：
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作者：
Mehranian, Abolfazl;Zaidi, Habib
基于发射的衰减校正的活度和衰减联合最大似然重建（MLAA）自飞行时间正电子发射断层成像/磁共振成像（PET/MR）出现以来再次受到关注。最近，我们利用磁共振成像约束的高斯混合模型（GMM）提高了MLAA算法的性能。在本研究中，我们将所提出算法的性能与商业系统上实施的标准4类基于磁共振的衰减校正（MRAC）进行了比较。方法：对5名头颈部接受¹⁸F - 氟脱氧葡萄糖（¹⁸F - FDG）检查的患者分别进行PET/MR成像和PET/CT扫描。将狄克逊脂肪和水的磁共振图像与CT图像配准。通过将磁共振图像分割为4种组织类别并分配预定义的衰减系数来得出MRAC图。对于MLAA - GMM，将磁共振图像分割为已知的组织类别，包括脂肪、软组织、肺、背景空气以及一个包含皮质骨、含气腔和金属伪影的未知磁共振低强度类别。一个配准的骨概率图也包含在未知组织类别中。最后，使用在患者群体上参数化的单峰高斯分布，将GMM先验约束在已知组织类别的衰减图上。结果：结果表明，MLAA - GMM算法通过区分骨骼与气隙，并提供更准确的患者特异性软组织和肺的衰减系数，优于MRAC方法。研究发现，MRAC和MLAA - GMM方法在肺部导致的平均标准摄取值误差分别为 - 5.4%和 - 3.5%，在软组织/病变中分别为 - 7.4%和 - 5.0%，在骨骼中分别为 - 18.4%和 - 10.2%。结论：所提出的MLAA算法有望在飞行时间PET/MR系统上准确得出衰减图。
The joint maximum-likelihood reconstruction of activity and attenuation (MLAA) for emission-based attenuation correction has regained attention since the advent of time-of-flight PET/MR imaging. Recently, we improved the performance of the MLAA algorithm using an MR imaging-constrained gaussian mixture model (GMM). In this study, we compare the performance of our proposed algorithm with standard 4-class MR-based attenuation correction (MRAC) implemented on commercial systems. Methods: Five head and neck 18F-FDG patients were scanned on PET/MR imaging and PET/CT scanners. Dixon fat and water MR images were registered to CT images. MRAC maps were derived by segmenting the MR images into 4 tissue classes and assigning predefined attenuation coefficients. For MLAA-GMM, MR images were segmented into known tissue classes, including fat, soft tissue, lung, background air, and an unknown MR low-intensity class encompassing cortical bones, air cavities, and metal artifacts. A coregistered bone probability map was also included in the unknown tissue class. Finally, the GMM prior was constrained over known tissue classes of attenuation maps using unimodal gaussians parameterized over a patient population. Results: The results showed that the MLAA-GMM algorithm outperformed the MRAC method by differentiating bones from air gaps and providing more accurate patient-specific attenuation coefficients of soft tissue and lungs. It was found that the MRAC and MLAA-GMM methods resulted in average standardized uptake value errors of -5.4% and -3.5% in the lungs, -7.4% and -5.0% in soft tissues/lesions, and -18.4% and -10.2% in bones, respectively. Conclusion: The proposed MLAA algorithm is promising for accurate derivation of attenuation maps on time-of-flight PET/MR systems.