Postreconstruction Nonlocal Means Filtering of Whole-Body PET With an Anatomical Prior
Postreconstruction Nonlocal Means Filtering of Whole-Body PET With an Anatomical Prior
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DOI：
10.1109/tmi.2013.2292881
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发表时间：
2014-03-01
影响因子：
10.6
通讯作者：
Meikle, Steven
中科院分区：
文献类型：
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作者：
Chan, Chung;Fulton, Roger;Meikle, Steven
正电子发射断层扫描（PET）图像通常遭受由于高水平的噪声和低空间分辨率的低信噪比（SNR），这不利地影响其性能的病变检测和量化。来自多模态成像系统的高分辨率解剖图像中存在的互补信息可能用于提高检测和/或量化病变的能力。然而，使用解剖先验的先前方法通常需要匹配的器官/病变边界。在这项研究中，我们研究了使用解剖信息来抑制PET图像中的噪声，同时保留计算机断层扫描（CT）上没有相应边界的突出信号的定量准确性和幅度。该方法通过基于非局部均值（NLM）滤波器的重建后滤波器来实现，该滤波器通过基于图像内体素块之间的相似性度量计算体素的加权平均来降低噪声。从CT获得的解剖学知识被纳入约束相似性测量的体素的子集内。与使用解剖先验的其他方法相比，相邻体素的实际数量和用于平滑的权重是根据子集内PET图像的稳健测量确定的。因此，所提出的方法可以对PET和CT之间的信号失配具有鲁棒性。一个3-D的搜索方案也进行了研究的体积PET/CT数据。建议的解剖学引导的中值非局部均值滤波器（AMNLM）首先使用计算机体模和物理体模进行评估，以模拟现实但具有挑战性的情况下，小病变位于均匀的区域，可以检测到PET，但不能在CT上。提出的方法进行了进一步评估与肺部病变患者的临床研究。所提出的方法的性能进行了比较高斯，边缘保持双边和NLM滤波器，以及中值非局部均值（MNLM）滤波没有解剖前。提出的AMNLM方法产生了改善的病变对比度和SNR相比，其他方法，即使与不完善的解剖知识，如丢失病变边界和不匹配的器官边界。
Positron emission tomography (PET) images usually suffer from poor signal-to-noise ratio (SNR) due to the high level of noise and low spatial resolution, which adversely affect its performance for lesion detection and quantification. The complementary information present in high-resolution anatomical images from multi-modality imaging systems could potentially be used to improve the ability to detect and/or quantify lesions. However, previous methods that use anatomical priors usually require matched organ/lesion boundaries. In this study, we investigated the use of anatomical information to suppress noise in PET images while preserving both quantitative accuracy and the amplitude of prominent signals that do not have corresponding boundaries on computerized tomography (CT). The proposed approach was realized through a postreconstruction filter based on the nonlocal means (NLM) filter, which reduces noise by computing the weighted average of voxels based on the similarity measurement between patches of voxels within the image. Anatomical knowledge obtained from CT was incorporated to constrain the similarity measurement within a subset of voxels. In contrast to other methods that use anatomical priors, the actual number of neighboring voxels and weights used for smoothing were determined from a robust measurement on PET images within the subset. Thus, the proposed approach can be robust to signal mismatches between PET and CT. A 3-D search scheme was also investigated for the volumetric PET/CT data. The proposed anatomically guided median nonlocal means filter (AMNLM) was first evaluated using a computer phantom and a physical phantom to simulate realistic but challenging situations where small lesions are located in homogeneous regions, which can be detected on PET but not on CT. The proposed method was further assessed with a clinical study of a patient with lung lesions. The performance of the proposed method was compared to Gaussian, edge-preserving bilateral and NLM filters, as well as median nonlocal means (MNLM) filtering without an anatomical prior. The proposed AMNLM method yielded improved lesion contrast and SNR compared with other methods even with imperfect anatomical knowledge, such as missing lesion boundaries and mismatched organ boundaries.