Bai Huiping, Zhang Weikang, Zhao Jun, Wang Yujie, Sun Jianqi
Opt Express. 2018 Oct 1;26(20):26566-26575. doi: 10.1364/OE.26.026566.
Grating-based phase-contrast is a hot topic in recent years owing to its excellent imaging contrast capability on soft tissues. Although it is compatible with conventional X-ray tubes and applicable in many fields, long scanning time, and high radiation dose obstruct its wider use in clinical and medical fields, especially for computed tomography applications. In this study, we solve this challenge by reducing the projection views and compensating the loss of reconstruction quality through dual-dictionary learning algorithm. The algorithm is implemented in two steps. First, estimated high-quality absorption images are obtained from the first dual-quality dictionary learning, which uses the correspondence between high-quality images and low-quality ones reconstructed from highly under-sampled data. Then, the second absorption-phase dual-modality dictionary learning is adopted to yield both estimated phase and absorption images, resulting in complementary information for both modality images. Afterwards the absorption and phase images are gradually improved in iterative reconstructions. By using SSIM RMSE measurements and visual assessment for enlarged regions of interest, our proposed method can improve the resolution of these two modality images and recover smaller structures, as compared to conventional methods.
基于光栅的相衬成像由于其对软组织具有出色的成像对比能力，近年来成为一个热门话题。尽管它与传统X射线管兼容且适用于许多领域，但扫描时间长和辐射剂量高阻碍了它在临床和医学领域的更广泛应用，特别是在计算机断层扫描应用中。在本研究中，我们通过减少投影视图并通过双字典学习算法补偿重建质量的损失来解决这一挑战。该算法分两步实现。首先，从第一次双质量字典学习中获得估计的高质量吸收图像，该学习利用了从高度欠采样数据重建的高质量图像与低质量图像之间的对应关系。然后，采用第二次吸收-相位双模态字典学习来生成估计的相位和吸收图像，从而为两种模态图像提供互补信息。之后，吸收图像和相位图像在迭代重建中逐步得到改善。通过使用结构相似性指数测量（SSIM）、均方根误差（RMSE）测量以及对放大的感兴趣区域进行视觉评估，与传统方法相比，我们提出的方法可以提高这两种模态图像的分辨率并恢复更小的结构。
