Regularized image reconstruction with an anatomically adaptive prior for positron emission tomography
Regularized image reconstruction with an anatomically adaptive prior for positron emission tomography
复制标题
DOI：
10.1088/0031-9155/54/24/009
复制
发表时间：
2009-12-21
影响因子：
3.5
通讯作者：
Meikle, Steven
中科院分区：
文献类型：
--
作者：
Chan, Chung;Fulton, Roger;Meikle, Steven
在许多先前的研究中已经建议将准确对齐的解剖信息作为正电子发射断层扫描（PET）中的引导重建和噪声正则化之前的合并。然而，只有当精确的病变轮廓也可用时，才能实现这种方法的优点。在实践中，解剖成像模态可能无法区分正常组织和病理组织，因此在解剖图像中看到的病变边缘可能不对应于发射图像中的功能边界。在这项研究中，我们探索了一种替代方法，将解剖之前到PET图像重建。特别令人感兴趣的是实际情况，即病变在发射图像中很明显，但在相应的解剖图像中却不明显。在所提出的方法中，从解剖先验获得的区域信息被用来估计解剖自适应各向异性中值扩散滤波（AAMDF）先验。该平滑先验被确定并自适应地应用于发射图像上的每个解剖区域，然后被组装以形成用于重建过程中的下一次迭代的先验图像。我们制定了一个两步联合估计重建方案，迭代更新估计图像和先验图像。建议AAMDF先验进行了评估，并与最大后验概率（MAP）重建方法进行了比较，有和没有解剖侧信息。在使用合成和物理体模数据的实验中，AAMDF先验产生了总体上更高的病变与背景对比度，并且在病变估计中的误差小于对于可比水平的背景噪声的其他算法。我们得出结论，病变对比度和量化可以提高使用解剖学推导的平滑之前，而不需要病变边界的知识。这在临床PET/CT中可能具有重要意义，其中病变边界通常无法从CT图像获得。
The incorporation of accurately aligned anatomical information as a prior to guide reconstruction and noise regularization in positron emission tomography (PET) has been suggested in many previous studies. However, the advantages of this approach can only be realized if the exact lesion outline is also available. In practice, the anatomical imaging modality may be unable to differentiate between normal and pathological tissues, and thus the edges of lesions seen in the anatomical image may not correspond to functional boundaries in the emission image. In this study, we explored an alternative approach to incorporating an anatomical prior into PET image reconstruction. Of particular interest was the realistic situation where lesions are apparent in the emission images but not in the corresponding anatomical images. In the proposed method, regional information obtained from the anatomical prior was used to estimate an anatomically adaptive anisotropic median-diffusion filtering (AAMDF) prior. This smoothing prior was determined and applied adaptively to each anatomical region on the emission image and then assembled to form a prior image for the next iteration in the reconstruction process. We formulated a two-step joint estimation reconstruction scheme to update the estimated image and prior image iteratively. The proposed AAMDF prior was evaluated and compared with maximum a posteriori (MAP) reconstruction methods with and without anatomical side information. In experiments using synthetic and physical phantom data, the AAMDF prior yielded overall higher lesion-to-background contrast and less error in lesion estimation than other algorithms for a comparable level of background noise. We conclude that lesion contrast and quantification can be improved using an anatomically derived smoothing prior without requiring knowledge of the lesion boundary. This may have important implications in clinical PET/CT, where lesion boundaries are often not obtainable from CT images.