Low-Dose Sinogram Restoration in SPECT Imaging Based on Conditional Generative Adversarial Network and LSTM.
Authors: 
Limei Peng, Si LiAuthors Info & Claims
Limei Peng, Si LiAuthors Info & ClaimsPages 328 - 334
Abstract
To address the problem that low-dose SPECT imaging will lead to poor-quality projection images, we propose a network structure based on the conditional generative adversarial network and add a convolutional LSTM module to combine the sequence features of sinograms for low-dose SPECT sinogram restoration, called LCGAN, the spatial and angular information of the sinogram can be better utilized. Projection data from SIMIND software simulations are used to train the proposed model. The recovered sinograms were reconstructed using the model-based iterative reconstruction (MBIR) method. To evaluate the effectiveness of the LCGAN model, the reconstructed images were evaluated using the global metrics PSNR and NMSE and the local metrics COV, and the results showed that the proposed method significantly improved the reconstruction quality of the low-dose sinograms.
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Published In
February 2023
619 pages
ISBN:9781450398411
DOI:10.1145/3587716
Copyright © 2023 ACM.
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Publication History
Published: 07 September 2023
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- Research-article
- Research
- Refereed limited
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- Natural Science Foundation of Guangdong Province
- Science and Technology Program of Guangzhou
- Natural Science Foundation of China
- Opening Project of Guangdong Province Key Laboratory of Computational Science at Sun Yat-sen University
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ICMLC 2023
ICMLC 2023: 2023 15th International Conference on Machine Learning and Computing
February 17 - 20, 2023
Zhuhai, China
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