Total variation-stokes strategy for sparse-view X-ray CT image reconstruction.
Total variation-stokes strategy for sparse-view X-ray CT image reconstruction.
复制标题
稀疏视图 X 射线 CT 图像重建的全变分斯托克斯策略
DOI：
10.1109/tmi.2013.2295738
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发表时间：
2014-03
影响因子：
10.6
通讯作者：
Moore W
中科院分区：
文献类型：
--
作者：
Liu Y;Liang Z;Ma J;Lu H;Wang K;Zhang H;Moore W
先前的研究已经表明，通过最小化具有某些数据和/或其它约束的待估计图像的总变差（TV），可以从稀疏视图投影数据重建分段平滑的X射线计算机断层摄影图像。然而，由于TV模型的分段常数假设，重建图像经常被报告遭受块状或片状伪影。为了克服这一缺点，本文提出了一种全变分-斯托克斯-凸集投影（TVS-POCS）重建方法。TVS模型是通过引入等照度线方向来恢复稀疏视图数据情况下可能丢失的信息。因此，在所得到的图像中保留了沿法线和切线方向的期望的沿着方向。与以前的基于TV的图像重建算法相比，通过TVS-POCS方法保留的图像质量在消除斑片伪影和保留细微结构方面有望产生显著的增益。为了评价所提出的TVS-POCS方法，使用数字体模、物理体模和临床数据实验进行定性和定量研究。结果表明，所提出的方法可以产生图像与几个显着的增益，衡量的通用质量指标和半高全宽的优点，相比其相应的基于电视的算法。此外，研究结果还表明，TVS-POCS方法在全视图数据情况下可以达到滤波反投影重建的金标准结果，而大多数迭代方法在全视图情况下可能会失败，因为它们的结果中存在人工纹理。
Previous studies have shown that by minimizing the total variation (TV) of the to-be-estimated image with some data and/or other constraints, a piecewise-smooth X-ray computed tomography image can be reconstructed from sparse-view projection data. However, due to the piecewise constant assumption for the TV model, the reconstructed images are frequently reported to suffer from the blocky or patchy artifacts. To eliminate this drawback, we present a total variation-stokes-projection onto convex sets (TVS-POCS) reconstruction method in this paper. The TVS model is derived by introducing isophote directions for the purpose of recovering possible missing information in the sparse-view data situation. Thus the desired consistencies along both the normal and the tangent directions are preserved in the resulting images. Compared to the previous TV-based image reconstruction algorithms, the preserved consistencies by the TVS-POCS method are expected to generate noticeable gains in terms of eliminating the patchy artifacts and preserving subtle structures. To evaluate the presented TVS-POCS method, both qualitative and quantitative studies were performed using digital phantom, physical phantom and clinical data experiments. The results reveal that the presented method can yield images with several noticeable gains, measured by the universal quality index and the full-width-at-half-maximum merit, as compared to its corresponding TV-based algorithms. In addition, the results further indicate that the TVS-POCS method approaches to the gold standard result of the filtered back-projection reconstruction in the full-view data case as theoretically expected, while most previous iterative methods may fail in the full-view case because of their artificial textures in the results.