Generative Modeling in Sinogram Domain for Sparse-view CT Reconstruction
- URL: http://arxiv.org/abs/2211.13926v1
- Date: Fri, 25 Nov 2022 06:49:18 GMT
- Title: Generative Modeling in Sinogram Domain for Sparse-view CT Reconstruction
- Authors: Bing Guan, Cailian Yang, Liu Zhang, Shanzhou Niu, Minghui Zhang, Yuhao
Wang, Weiwen Wu, Qiegen Liu
- Abstract summary: radiation dose in computed tomography (CT) examinations can be significantly reduced by intuitively decreasing the number of projection views.
Previous deep learning techniques with sparse-view data require sparse-view/full-view CT image pairs to train the network with supervised manners.
We present a fully unsupervised score-based generative model in sinogram domain for sparse-view CT reconstruction.
- Score: 12.932897771104825
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: The radiation dose in computed tomography (CT) examinations is harmful for
patients but can be significantly reduced by intuitively decreasing the number
of projection views. Reducing projection views usually leads to severe aliasing
artifacts in reconstructed images. Previous deep learning (DL) techniques with
sparse-view data require sparse-view/full-view CT image pairs to train the
network with supervised manners. When the number of projection view changes,
the DL network should be retrained with updated sparse-view/full-view CT image
pairs. To relieve this limitation, we present a fully unsupervised score-based
generative model in sinogram domain for sparse-view CT reconstruction.
Specifically, we first train a score-based generative model on full-view
sinogram data and use multi-channel strategy to form highdimensional tensor as
the network input to capture their prior distribution. Then, at the inference
stage, the stochastic differential equation (SDE) solver and data-consistency
step were performed iteratively to achieve fullview projection. Filtered
back-projection (FBP) algorithm was used to achieve the final image
reconstruction. Qualitative and quantitative studies were implemented to
evaluate the presented method with several CT data. Experimental results
demonstrated that our method achieved comparable or better performance than the
supervised learning counterparts.
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