Super Sparse Projection Reconstruction of Computed Tomography Image Based-on Reweighted Total Variation
Sparse projection is an effective way to reduce the exposure to radiation during X-ray CT imaging. However, reconstruction of images from sparse projection data is challenging. In this paper, a novel method called reweight total variation (WTV) is applied to solve the challenging problem. And based on WTV, an iteration algorithm which allows the image to be reconstructed accurately is also proposed. The experimental results on both simulated and real images have consistently shown that, compared to the popular total variation (TV) method and the classical Algebra Reconstruction Technique (ART), the proposed method achieves better results when the projection is sparse, and performs comparably with TV and ART when the number of projections is relatively high. Therefore, the application of the proposed reconstruction algorithm may permit reduction of the radiation exposure without trade-off in imaging performance.
KeywordsSparse Projection Reconstruction CT Reweight total variation Iterative Reconstruction
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