Anomaly Detection and Artifact Recovery in PET Attenuation-Correction Images Using the Likelihood Function.
Anomaly Detection and Artifact Recovery in PET Attenuation-Correction Images Using the Likelihood Function.
复制标题
使用似然函数进行 PET 衰减校正图像中的异常检测和伪影恢复。
DOI：
10.1109/jstsp.2012.2237380
复制
发表时间：
2013
影响因子：
7.5
通讯作者：
Bowsher,JamesE
中科院分区：
文献类型：
--
作者：
Laymon,CharlesM;Bowsher,JamesE
在双模态PET/CT中，CT数据用于生成在PET发射图像的重建中应用的衰减校正。这需要将CT图像转换为511 keV衰减图。进行这种转换的算法需要对患者体内物质的构成进行假设。为了增强CT扫描而给予的诸如造影剂的异常材料会混淆转换算法，并且已经观察到会导致不准确性，即与PET发射扫描时存在的真实511-keV衰减不一致。这些衰减伪影一直延续到最终的衰减校正PET发射图像，并且可能类似于患病组织。我们提出了一种方法来纠正这个问题，采用PET发射数据所携带的衰减信息。提出并测试了一种基于似然性的对比度伪影识别和校正算法。该算法利用了这样一个事实，即对比伪影表现为在其他高质量衰减图像中过高的衰减值。在一项单独的研究中，对数似然作为一个目标函数的组成部分，独立于任何特定的算法，映射出几个成像场景作为统计噪声的函数的性能。完整算法和对数似然在模拟数据的研究中表现良好。需要额外的研究，包括那些与患者数据，以充分了解他们的能力。
In dual modality PET/CT, CT data are used to generate the attenuation correction applied in the reconstruction of the PET emission image. This requires converting the CT image into a 511-keV attenuation map. Algorithms for making this transformation require assumptions about the makeup of material within the patient. Anomalous material such as contrast agent administered to enhance the CT scan confounds conversion algorithms and has been observed to result in inaccuracies, i.e. inconsistencies with the true 511-keV attenuation present at the time of the PET emission scan. These attenuation artifacts carry through to the final attenuation-corrected PET emission image and can resemble diseased tissue. We propose an approach to correcting this problem that employs the attenuation information carried by the PET emission data. A likelihood-based algorithm for identifying and correcting contrast artifacts is presented and tested. The algorithm exploits the fact that contrast artifacts manifest as too-high attenuation values in an otherwise high quality attenuation image. In a separate study, the performance of the loglikelihood as an objective-function component, independent of any particular algorithm, is mapped out for several imaging scenarios as a function of statistical noise. Both the full algorithm and the loglikelihood performed well in studies with simulated data. Additional studies including those with patient data are required to fully understand their capabilities .