Cone Beam X-ray Luminescence Computed Tomography Based on Bayesian Method.
Cone Beam X-ray Luminescence Computed Tomography Based on Bayesian Method.
复制标题
基于贝叶斯方法的锥束X射线发光计算机断层扫描。
DOI：
10.1109/tmi.2016.2603843
复制
发表时间：
2017-01
影响因子：
10.6
通讯作者：
Xing L
中科院分区：
文献类型：
--
作者：
Zhang G;Liu F;Liu J;Luo J;Xie Y;Bai J;Xing L
X射线发光计算机层析成像(XLCT)是近年来提出的一种新的成像方式，其目的是通过X射线实现分子和功能成像。XLCT结合了基于发光的探针的X射线激发和光学信号检测的原理，自然地融合了功能和解剖图像，并为生物医学研究的广泛应用提供了补充信息。为了提高以前研制的窄束XLCT的数据采集效率，本文采用了锥束XLCT(CB-XLCT)模式，充分利用了锥束激励的几何特性。实际上，将锥束X射线用于XLCT的一个主要障碍是，这里的逆问题是严重的病态，阻碍了我们获得良好的图像质量。在本文中，我们提出了一种新的贝叶斯方法来解决CB-XLCT重建的瓶颈。该方法利用基于高斯马尔可夫随机场的局部正则化策略来缓解CB-XLCT的病态。然后使用交替优化方案自动计算所有未知的超参数，同时采用迭代坐标下降算法重建基于体素的闭合解的图像。数值模拟和小鼠实验结果表明，与传统方法相比，自适应贝叶斯方法显著改善了CB-XLCT的图像质量。
X-ray luminescence computed tomography (XLCT), which aims to achieve molecular and functional imaging by X-rays, has recently been proposed as a new imaging modality. Combining the principles of X-ray excitation of luminescence-based probes and optical signal detection, XLCT naturally fuses functional and anatomical images and provides complementary information for a wide range of applications in biomedical research. In order to improve the data acquisition efficiency of previously developed narrow-beam XLCT, a cone beam XLCT (CB-XLCT) mode is adopted here to take advantage of the useful geometric features of cone beam excitation. Practically, a major hurdle in using cone beam X-ray for XLCT is that the inverse problem here is seriously ill-conditioned, hindering us to achieve good image quality. In this paper, we propose a novel Bayesian method to tackle the bottleneck in CB-XLCT reconstruction. The method utilizes a local regularization strategy based on Gaussian Markov random field to mitigate the ill-conditioness of CB-XLCT. An alternating optimization scheme is then used to automatically calculate all the unknown hyperparameters while an iterative coordinate descent algorithm is adopted to reconstruct the image with a voxel-based closed-form solution. Results of numerical simulations and mouse experiments show that the self-adaptive Bayesian method significantly improves the CB-XLCT image quality as compared with conventional methods.