Slice-wise reconstruction for low-dose cone-beam CT using a deep residual convolutional neural network
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Because of the growing concern over the radiation dose delivered to patients, X-ray cone-beam CT (CBCT) imaging of low dose is of great interest. It is difficult for traditional reconstruction methods such as Feldkamp to reduce noise and keep resolution at low doses. A typical method to solve this problem is using optimization-based methods with careful modeling of physics and additional constraints. However, it is computationally expensive and very time-consuming to reach an optimal solution. Recently, some pioneering work applying deep neural networks had some success in characterizing and removing artifacts from a low-dose data set. In this study, we incorporate imaging physics for a cone-beam CT into a residual convolutional neural network and propose a new end-to-end deep learning-based method for slice-wise reconstruction. By transferring 3D projection to a 2D problem with a noise reduction property, we can not only obtain reconstructions of high image quality, but also lower the computational complexity. The proposed network is composed of three serially connected sub-networks: a cone-to-fan transformation sub-network, a 2D analytical inversion sub-network, and an image refinement sub-network. This provides a comprehensive solution for end-to-end reconstruction for CBCT. The advantages of our method are that the network can simplify a 3D reconstruction problem to a 2D slice-wise reconstruction problem and can complete reconstruction in an end-to-end manner with the system matrix integrated into the network design. Furthermore, reconstruction can be less computationally expensive and easily parallelizable compared with iterative reconstruction methods.
KeywordsCone-beam CT Slice-wise Residual U-net Low dose Image denoising
- 1.Y. Xing, L. Zhang, A free-geometry cone beam CT and its FDK-type reconstruction. J. X-ray Sci. Technol. 15(3), 157–167 (2007)Google Scholar
- 4.L.J.M. Kroft, J.J.H. Roelofs, J. Geleijns, Scan time and patient dose for thoracic imaging in neonates and small children using axial volumetric 320-detector row CT compared to helical 64-, 32-, and 16- detector row CT acquisitions. Pediatr. Radiol. 40(3), 294–300 (2010). https://doi.org/10.1007/s00247-009-1436-x CrossRefGoogle Scholar
- 27.J. Hao, L. Zhang, L. Li et al., A practical image reconstruction and processing method for symmetrically off-center detector CBCT system. Nucl. Sci. Technol. 24(4), 17–22 (2013)Google Scholar
- 30.K. Liang, L. Zhang, H. Yang, et al. Optimize interpolation-based MAR for practical dental CT with deep learning, in The 5th International Conference on Image Formation in X-ray Computed Tomography (CT meeting 2018) (2018), pp. 423–425Google Scholar