In this paper, we propose a new deep learning approach based on unfolded neural networks for the reconstruction of X-ray computed tomography images from few views. We start from a model-based approach in a compressed sensing framework, described by the minimization of a least squares function plus an edge-preserving prior on the solution. In particular, the proposed network automatically estimates the internal parameters of a proximal interior point method for the solution of the optimization problem. The numerical tests performed on both a synthetic and a real dataset show the effectiveness of the framework in terms of accuracy and robustness with respect to noise on the input sinogram when compared to other different data-driven approaches.
CTprintNet: An Accurate and Stable Deep Unfolding Approach for Few-View CT Reconstruction / Loli Piccolomini E.; Prato M.; Scipione M.; Sebastiani A.. - In: ALGORITHMS. - ISSN 1999-4893. - ELETTRONICO. - 16:6(2023), pp. 1-18. [10.3390/a16060270]
CTprintNet: An Accurate and Stable Deep Unfolding Approach for Few-View CT Reconstruction
Loli Piccolomini E.Conceptualization
;Sebastiani A.Formal Analysis
2023
Abstract
In this paper, we propose a new deep learning approach based on unfolded neural networks for the reconstruction of X-ray computed tomography images from few views. We start from a model-based approach in a compressed sensing framework, described by the minimization of a least squares function plus an edge-preserving prior on the solution. In particular, the proposed network automatically estimates the internal parameters of a proximal interior point method for the solution of the optimization problem. The numerical tests performed on both a synthetic and a real dataset show the effectiveness of the framework in terms of accuracy and robustness with respect to noise on the input sinogram when compared to other different data-driven approaches.File | Dimensione | Formato | |
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