Ms. Yuyan Song Profile

Yuyan Song

at Southern Medical Univ

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Proceedings Article | 7 April 2023 Poster + Paper

VVBP-tensor-based deep neural network for metal artifact reduction in computed tomography

Manman Zhu, Gaofeng Chen, Qisen Zhu, Yuyan Song, Yongbo Wang, Jianhua Ma

Proceedings Volume 12463, 124634Z (2023) https://doi.org/10.1117/12.2654201

KEYWORDS: Metals, Computed tomography, X-ray computed tomography, Image restoration, Computer simulations, Tunable filters, Neural networks, Image segmentation, Interpolation, Image quality

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The presence of metal often heavily degrades the computed tomography (CT) image quality and inevitably affects the subsequent clinical diagnosis and therapy. With the rapid development of deep learning (DL), a lot of DL-based methods have been proposed for metal artifact reduction (MAR) task in CT imaging, including image domain, projection domain and dual-domain based MAR methods. Recently, view-by-view backprojection tensor (VVBP-Tensor) domain is developed as the intermediary domain between image domain and projection domain, while VVBP-Tensor also has many good mathematical properties, such as low-rank property and structural self-similarity. Therefore, we present a VVBP-Tensor based deep neural network (DNN) framework for better MAR performance in CT imaging. Specifically, the original projection is separately pre-processed by the linear interpolation completion algorithm and the clipping algorithm, to quickly remove most metal artifacts and preserve structural information. Then, the clipped projection is restored by one sinogram recovery network to smooth the projection values in and out of the metal trajectory. In addition, two pre-processed projections are separately transferred to two tensors by filtering, backprojecting and sorting, and two sorted tensors are simultaneously rolled into the MAR reconstruction network for further improving reconstructed CT image quality. The proposed method has a good interpretability since the MAR reconstruction network can be considered as a weighted CT image reconstruction process with learnable adaptive weights along the direction of scan views. The superior MAR performance of the presented method is demonstrated on the simulated dataset in terms of qualitative and quantitative measurements.

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