Adaptive L0 norm constrained reconstructions for sparse-view scan in cone-beam CT

Patient's radiation exposure has been one of the major concerns in modern tomography technology. Performing sparse-view scan can effectively reduce the total radiation dose received by patients, however, the reconstruction becomes challenging. Although iterative algorithms generate much better results than conventional analytical methods such as FDK or BPF in tomography reconstructions, yet they fail to provide satisfactory reconstructions under sparse-view scan due to the high ill-posedness of the problem. Compressed sensing theory provides us possibility to recover 3D image from very limited number of measurements. We in this paper introduce an iterative algorithm based on adaptive l
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norm constrained. Simulated results have shows that the proposed method is capable of providing good reconstructions under sparse-view scans.