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In this paper, we propose a new GPU implementation of the Katsevich algorithmfor helical CT reconstruction. Our implementation divides the sinograms andreconstructs the CT images pitch by pitch. By utilizing the periodic propertiesof the parameters of the Katsevich algorithm, our method only needs tocalculate these parameters once for all the pitches and so has lower GPU-memoryburdens and is very suitable for deep learning. By embedding our implementationinto the network, we propose an end-to-end deep network for the high pitchhelical CT reconstruction with sparse detectors. Since our network utilizes thefeatures extracted from both sinograms and CT images, it can simultaneouslyreduce the streak artifacts caused by the sparsity of sinograms and preservefine details in the CT images. Experiments show that our network outperformsthe related methods both in subjective and objective evaluations.

A three-dimensional dual-domain deep network for high-pitch and sparse helical CT reconstruction