A three-dimensional dual-domain deep network for high-pitch and sparse helical CT reconstruction
This work addresses CT reconstruction challenges for medical imaging applications, representing an incremental improvement by combining existing algorithmic optimizations with deep learning.
The paper tackles the problem of reconstructing high-pitch helical CT images from sparse detector data by proposing a GPU-accelerated Katsevich algorithm and embedding it into a dual-domain deep network, resulting in reduced streak artifacts and preserved details with superior performance in subjective and objective evaluations.
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.