  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.
