Department of Engineering Physics, Tsinghua University, Beijing, China.
J Xray Sci Technol. 2013;21(2):161-76. doi: 10.3233/XST-130370.
In recent years, the total variation (TV) minimization method has been widely used for compressed sensing (CS) based CT image reconstruction. In this paper, we propose a few-view reweighted sparsity hunting (FRESH) method for CT image reconstruction, and demonstrate the superior performance of this method. Specifically, the key of the purposed method is that a reweighted total variation (RwTV) measure is used to characterize image sparsity in the cost function, outperforming the conventional TV counterpart. To solve the RwTV minimization problem efficiently, the Split-Bregman method and other state-of-the-art L1 optimization methods are compared. Inspired by the fast iterative shrinkage/thresholding algorithm (FISTA), a predication step is incorporated for fast computation in the Split-Bregman framework. Extensive numerical experiments have shown that our FRESH approach performs significantly better than competing algorithms in terms of image quality and convergence speed for few-view CT. High-quality images were reconstructed by our FRESH method after 250 iterations using only 15 few-view projections of the Forbild head phantom while other competitors needed more than 800 iterations. Remarkable improvements in details in the experimental evaluation using actual sheep thorax data further indicate the potential real-world application of the FRESH method.
近年来，全变差（TV）最小化方法已被广泛应用于基于压缩感知（CS）的 CT 图像重建。本文提出了一种用于 CT 图像重建的少视角重加权稀疏搜索（FRESH）方法，并证明了该方法的优越性能。具体来说，该方法的关键在于在代价函数中使用重加权全变差（RwTV）度量来描述图像稀疏性，优于传统的 TV 方法。为了有效地解决 RwTV 最小化问题，我们比较了 Split-Bregman 方法和其他最先进的 L1 优化方法。受快速迭代收缩/阈值算法（FISTA）的启发，在 Split-Bregman 框架中引入了预测步骤以实现快速计算。大量的数值实验表明，在少视角 CT 情况下，我们的 FRESH 方法在图像质量和收敛速度方面明显优于竞争算法。通过仅使用 15 个 Forbild 头部模型的少视角投影，我们的 FRESH 方法在 250 次迭代后即可重建高质量的图像，而其他竞争算法则需要超过 800 次迭代。使用实际羊胸数据进行的实验评估中的细节方面的显著改进进一步表明了 FRESH 方法在实际应用中的潜力。
