Low-Dose X-ray CT Reconstruction via Dictionary Learning
TL;DR: The results show that the proposed approach might produce better images with lower noise and more detailed structural features in the authors' selected cases, however, there is no proof that this is true for all kinds of structures.
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Abstract: Although diagnostic medical imaging provides enormous benefits in the early detection and accuracy diagnosis of various diseases, there are growing concerns on the potential side effect of radiation induced genetic, cancerous and other diseases. How to reduce radiation dose while maintaining the diagnostic performance is a major challenge in the computed tomography (CT) field. Inspired by the compressive sensing theory, the sparse constraint in terms of total variation (TV) minimization has already led to promising results for low-dose CT reconstruction. Compared to the discrete gradient transform used in the TV method, dictionary learning is proven to be an effective way for sparse representation. On the other hand, it is important to consider the statistical property of projection data in the low-dose CT case. Recently, we have developed a dictionary learning based approach for low-dose X-ray CT. In this paper, we present this method in detail and evaluate it in experiments. In our method, the sparse constraint in terms of a redundant dictionary is incorporated into an objective function in a statistical iterative reconstruction framework. The dictionary can be either predetermined before an image reconstruction task or adaptively defined during the reconstruction process. An alternating minimization scheme is developed to minimize the objective function. Our approach is evaluated with low-dose X-ray projections collected in animal and human CT studies, and the improvement associated with dictionary learning is quantified relative to filtered backprojection and TV-based reconstructions. The results show that the proposed approach might produce better images with lower noise and more detailed structural features in our selected cases. However, there is no proof that this is true for all kinds of structures.
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Citations
A Lightweight Structure Aimed to Utilize Spatial Correlation for Sparse-View CT Reconstruction
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TL;DR: This paper proposes a lightweight deep learning-based method, LS-AAE, for sparse-view CT reconstruction, leveraging spatial correlation and achieving state-of-the-art performance with high model mobility, outperforming current methods even at one-fourth sampling rate.
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TL;DR: A sinogram inpainting method based on recently rising sparse representation technology is proposed to overcome the problem of reconstruction from undersampling projection data, and visual and numerical results validate the clinical potential of the proposed method.
A high-resolution photon-counting breast CT system with tensor-framelet based iterative image reconstruction for radiation dose reduction
TL;DR: This study demonstrates that the TFIR can reduce the required radiation dose by a factor of two-thirds for a CT image reconstruction compared to the FBP technique, which achieves much better CNR and spatial resolution for high contrast target in addition to retaining similar spatialresolution for low contrast target.
A Dictionary Learning Approach with Overlap for the Low Dose Computed Tomography Reconstruction and Its Vectorial Application to Differential Phase Tomography
TL;DR: The resulting algorithm has proven to be efficient and well-adapted to strongly reduce the required dose and the number of projections in medical tomography.
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