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3 April 2023 Supervised transform learning for limited angle tomographic reconstruction
Zhipeng Li, Gaoyu Chen, Hao Gao, Yong Long
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Proceedings Volume 12464, Medical Imaging 2023: Image Processing; 1246419 (2023) https://doi.org/10.1117/12.2651336
Event: SPIE Medical Imaging, 2023, San Diego, California, United States
Abstract
Computed tomography (CT) reconstruction from X-ray projections acquired within a limited angle range is challenging. Both analytical and conventional iterative methods suffer from severe artifacts due to the incompleteness of sinogram. To obtain high-quality reconstructions from limited angle CT, it is crucial to integrate model-based methods with better learned priors from existing big databases of CT images. Transform learning is an unsupervised data-driven model that has recently shown promise in several medical imaging applications. However, its performance is limited due to the use of hand-crafted penalty terms on the learned transform and sparse coefficients. Inspired by the great success of convolutional neural network, we propose a supervised transform learning method for limited angle CT image reconstruction, where we redesign the conventional unsupervised iterative transform learning algorithm and learn the priors for both sparse coefficients and transform in a supervised manner. Clinical patient data results show that the proposed method significantly improves image quality of reconstructions, compared to a denoising deep convolutional neural network method, FBPConvNet, and a representative iterative neural network method, LEARN.
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Zhipeng Li, Gaoyu Chen, Hao Gao, and Yong Long "Supervised transform learning for limited angle tomographic reconstruction", Proc. SPIE 12464, Medical Imaging 2023: Image Processing, 1246419 (3 April 2023); https://doi.org/10.1117/12.2651336
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KEYWORDS
CT reconstruction
Reconstruction algorithms
Machine learning
X-ray computed tomography
Deep convolutional neural networks
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