A robust deep learning model for direct CT image reconstruction
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Từ khóa:
Direct image reconstruction
Computed tomography (CT)
Generative adversarial network (GAN)
Wasserstein generative adversarial network (WGAN)
Maximum Likelihood Expectation Maximization (MLEM)
Tóm tắt
Computed Tomography (CT) image reconstruction is an important research direction in the medical field. In addition to traditional reconstruction methods using iterative algorithms such as Maximum Likelihood Expectation Maximization (MLEM) or back-projection and forward-projection methods to reconstruct images from sinograms, deep learning models have emerged as a promising solution. Deep learning architectures, such as convolutional neural network (CNN), U-Net, and generative adversarial network (GAN), allow to significantly improve reconstruction accuracy from sinogram data directly. In this study, we focuses on improving deep learning models to directly reconstruct CT images from sinogram data while also applying traditional reconstruction methods to enhance the quality of the reconstructed images.
Tài liệu tham khảo
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Oct 31, 2025
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Thang, P. C., H. D. A. Bao, D. M. Toan, N. T. Than, và L. M. Tri. “A Robust Deep Learning Model for Direct CT Image Reconstruction”. Tạp Chí Khoa học Và Công nghệ - Đại học Đà Nẵng, vol 23, số p.h 10B, Tháng Mười 2025, tr 7-12, doi:10.31130/ud-jst.2025.23(10B).628E.
