Multi-stage Deep Learning Artifact Reduction for Computed Tomography

• URL: http://arxiv.org/abs/2309.00494v1
• Date: Fri, 1 Sep 2023 14:40:25 GMT
• Title: Multi-stage Deep Learning Artifact Reduction for Computed Tomography
• Authors: Jiayang Shi, Daniel M. Pelt, K. Joost Batenburg
• Abstract summary: We propose a multi-stage deep learning method for artifact removal, in which neural networks are applied to several domains. We show that the neural networks can be effectively trained in succession, resulting in easy-to-use and computationally efficient training.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In Computed Tomography (CT), an image of the interior structure of an object is computed from a set of acquired projection images. The quality of these reconstructed images is essential for accurate analysis, but this quality can be degraded by a variety of imaging artifacts. To improve reconstruction quality, the acquired projection images are often processed by a pipeline consisting of multiple artifact-removal steps applied in various image domains (e.g., outlier removal on projection images and denoising of reconstruction images). These artifact-removal methods exploit the fact that certain artifacts are easier to remove in a certain domain compared with other domains. Recently, deep learning methods have shown promising results for artifact removal for CT images. However, most existing deep learning methods for CT are applied as a post-processing method after reconstruction. Therefore, artifacts that are relatively difficult to remove in the reconstruction domain may not be effectively removed by these methods. As an alternative, we propose a multi-stage deep learning method for artifact removal, in which neural networks are applied to several domains, similar to a classical CT processing pipeline. We show that the neural networks can be effectively trained in succession, resulting in easy-to-use and computationally efficient training. Experiments on both simulated and real-world experimental datasets show that our method is effective in reducing artifacts and superior to deep learning-based post-processing.

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