Joint estimation of tissue types and linear attenuation coefficients for photon counting CT
Joint estimation of tissue types and linear attenuation coefficients for photon counting CT
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DOI：
10.1118/1.4927261
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发表时间：
2015-09-01
期刊：
影响因子：
3.8
通讯作者：
Amaya, Kenji
中科院分区：
文献类型：
--
作者：
Nakada, Kento;Taguchi, Katsuyuki;Amaya, Kenji
目的：新发展的光谱计算机断层扫描（CT），如光子计数检测器CT，可以通过材料分解技术更准确地识别组织类型。然而，许多迭代重建方法，包括针对频谱CT开发的方法，都采用正则化项，该正则化项的惩罚过渡是利用CT图像本身的像素值设计的。同样，重建后应用组织类型识别方法；因此，不可能考虑由投影似然得到的概率分布。本工作的目的是开发综合的图像重建和组织类型识别算法，以提高重建图像和组织类型映射的质量。方法：作者提出了一个新的框架，通过基于体素的潜在变量对组织类型进行最大后验估计，联合进行光子计数检测器CT的图像重建、材料分解和组织类型识别。潜在变量使用基于体素的耦合马尔可夫随机场来描述人体器官的连续性和非连续性，并使用一组高斯分布来包含组织类型与其衰减特性之间的统计关系。将该方法的性能与滤波后的反投影法和二次惩罚似然法进行了定量比较。结果：结果表明，在图像噪声和分辨率之间的权衡优于现有的重建方法。二次惩罚似然法改进了重建图像的标准差（SD）和偏倚：偏倚，-0.9 vs -0.1 Hounsfield单位（HU）；SD, 46.8 vs 27.4 HU。二次惩罚似然法的组织类型识别准确率为80.1%比86.9%。结论：该方法不仅可以更准确地识别组织类型，而且可以利用组织类型信息重建噪声更低、清晰度更高的CT图像。(C) 2015年美国医学物理学家协会。
Purpose: Newly developed spectral computed tomography (CT) such as photon counting detector CT enables more accurate tissue-type identification through material decomposition technique. Many iterative reconstruction methods, including those developed for spectral CT, however, employ a regularization term whose penalty transition is designed using pixel value of CT image itself. Similarly, the tissue-type identification methods are then applied after reconstruction; thus, it is impossible to take into account probability distribution obtained from projection likelihood. The purpose of this work is to develop comprehensive image reconstruction and tissue-type identification algorithm which improves quality of both reconstructed image and tissue-type map.Methods: The authors propose a new framework to jointly perform image reconstruction, material decomposition, and tissue-type identification for photon counting detector CT by applying maximum a posteriori estimation with voxel-based latent variables for the tissue types. The latent variables are treated using a voxel-based coupled Markov random field to describe the continuity and discontinuity of human organs and a set of Gaussian distributions to incorporate the statistical relation between the tissue types and their attenuation characteristics. The performance of the proposed method is quantitatively compared to that of filtered backprojection and a quadratic penalized likelihood method by 100 noise realization.Results: Results showed a superior trade-off between image noise and resolution to current reconstruction methods. The standard deviation (SD) and bias of reconstructed image were improved from quadratic penalized likelihood method: bias, -0.9 vs -0.1 Hounsfield unit (HU); SD, 46.8 vs 27.4 HU. The accuracy of tissue-type identification was also improved from quadratic penalized likelihood method: 80.1% vs 86.9%.Conclusions: The proposed method makes it possible not only to identify tissue types more accurately but also to reconstruct CT images with decreased noise and enhanced sharpness owing to the information about the tissue types. (C) 2015 American Association of Physicists in Medicine.