论文标题
用于高点和稀疏螺旋CT重建的三维双域深网
A three-dimensional dual-domain deep network for high-pitch and sparse helical CT reconstruction
论文作者
论文摘要
在本文中，我们提出了用于螺旋CT重建的Katsevich算法的新GPU实施。我们的实现将正式图划分并通过音高重建CT图像。通过利用Katsevich算法参数的定期性能，我们的方法只需要计算一次这些参数，因此所有音调都需要较低的GPU-MEMORY负担，并且非常适合深度学习。通过将我们的实现嵌入到网络中，我们提出了一个用稀疏检测器的高螺距螺旋CT重建的端到端深网。由于我们的网络利用了从辛图和CT图像中提取的特征，因此它可以同时减少由官方图的稀疏性引起的条纹伪像，并在CT图像中保留细节。实验表明，我们的网络在主观和客观评估中都优于相关方法。
In this paper, we propose a new GPU implementation of the Katsevich algorithm for helical CT reconstruction. Our implementation divides the sinograms and reconstructs the CT images pitch by pitch. By utilizing the periodic properties of the parameters of the Katsevich algorithm, our method only needs to calculate these parameters once for all the pitches and so has lower GPU-memory burdens and is very suitable for deep learning. By embedding our implementation into the network, we propose an end-to-end deep network for the high pitch helical CT reconstruction with sparse detectors. Since our network utilizes the features extracted from both sinograms and CT images, it can simultaneously reduce the streak artifacts caused by the sparsity of sinograms and preserve fine details in the CT images. Experiments show that our network outperforms the related methods both in subjective and objective evaluations.
