Sparse-view tomographic reconstruction using residual U-Net with attention gates

Chang-Chieh Cheng

doi:10.1117/12.2688209

2 April 2024 Sparse-view tomographic reconstruction using residual U-Net with attention gates

Chang-Chieh Cheng

Author Affiliations +

Proceedings Volume 12926, Medical Imaging 2024: Image Processing; 129261E (2024) https://doi.org/10.1117/12.2688209
Event: SPIE Medical Imaging, 2024, San Diego, California, United States

Conference Poster

Abstract

Reconstructing a tomographic image with sparse-view sampling is a major challenge in low-dose computed tomography. Recently, several studies have reported that deep-learning-based methods can reconstruct images of 512 × 512 pixels from 60-view X-ray projections without large artifacts. In this study, a U-Net variant with residual connections and attention gates is proposed for sparse-view computed tomography. A pair of the proposed U-Nets with a loss function based on the structural similarity index measure can be applied to synthesize sparse-view sampling sinograms and denoise reconstructed images. The experimental results indicate the performance of the proposed method is superior to those of other U-Net-based methods for fewer than 60 projection views. Experiments on a public data set of chest tomographic images validated that the proposed method can be used for COVID-19 identification.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Chang-Chieh Cheng "Sparse-view tomographic reconstruction using residual U-Net with attention gates", Proc. SPIE 12926, Medical Imaging 2024: Image Processing, 129261E (2 April 2024); https://doi.org/10.1117/12.2688209

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