Convolutional Sparse Coding for Compressed Sensing CT Reconstruction
Convolutional Sparse Coding for Compressed Sensing CT Reconstruction
复制标题
用于压缩感知 CT 重建的卷积稀疏编码
DOI：
10.1109/tmi.2019.2906853
复制
发表时间：
2019-11-01
影响因子：
10.6
通讯作者：
Zhang, Yi
中科院分区：
文献类型：
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作者：
Bao, Peng;Xia, Wenjun;Zhang, Yi
在过去的几年中，基于字典学习（DL）的方法已成功地用于各种图像重建问题。然而，传统的基于DL的CT重建方法是基于块的，忽略了重叠块中像素的一致性。此外，通过这些方法学习的特征总是包含相同特征的移位版本。近年来，卷积稀疏编码（CSC）已被开发来解决这些问题。在本文中，受CSC在信号处理领域的几个成功应用的启发，我们探讨了CSC在稀疏视图CT重建中的潜力。该方法直接对整幅图像进行处理，无需将图像分割为多个重叠的图像块，从而保持了更多的细节，避免了图像块聚集所带来的伪影。通过预先确定的滤波器，开发了一种交替方案来优化目标函数。仿真和真实的CT数据的实验验证了所提出方法的有效性。定性和定量的结果表明，所提出的方法实现了更好的性能比现有的几个国家的最先进的方法。
Over the past few years, dictionary learning (DL)-based methods have been successfully used in various image reconstruction problems. However, the traditional DL-based computed tomography (CT) reconstruction methods are patch-based and ignore the consistency of pixels in overlapped patches. In addition, the features learned by these methods always contain shifted versions of the same features. In recent years, convolutional sparse coding (CSC) has been developed to address these problems. In this paper, inspired by several successful applications of CSC in the field of signal processing, we explore the potential of CSC in sparse-view CT reconstruction. By directly working on the whole image, without the necessity of dividing the image into overlapped patches in DL-based methods, the proposed methods can maintain more details and avoid artifacts caused by patch aggregation. With predetermined filters, an alternating scheme is developed to optimize the objective function. Extensive experiments with simulated and real CT data were performed to validate the effectiveness of the proposed methods. The qualitative and quantitative results demonstrate that the proposed methods achieve better performance than the several existing state-of-the-art methods.