Compressed sensing algorithms for fan-beam CT image reconstruction

Compressed sensing can recover a signal that is sparse in some way from a small number of samples. For CT imaging, this has the potential to obtain good reconstruction from a smaller number of projections or views, thereby reducing the amount of patient radiation. In this work, we applied compressed sensing to fan beam CT image reconstruction , which is a special case of an important 3D CT problem (cone beam CT). We compared the performance of two compressed sensing algorithms, denoted as the LP and the QP, in simulation. Our results indicate that the LP generally provides smaller reconstruction error and converges faster, hence is more preferable.

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