Accurate computed tomography (CT) reconstruction from incomplete projections is an important research topic. Sparse sampling and limited-angle sampling are two effective ways to reduce the X-ray radiation dose or the scanning time. However, it is technically complicated to realize sparse sampling in medical CT since the tube power or the pre-patient collimator is difficult to be switched frequently. Limited-angle sampling makes it difficult to reconstruct an accurate image. The developed multiple limited-angles (MLA) sampling scheme could well balance the technical implementation complexity and the CT reconstruction difficulty. It does not require frequent switching of the tube power or the pre-patient collimator. The data correlation of the acquired projections is lower than that in limited-angle sampling. Using the projections acquired by MLA sampling, CT images reconstructed by the total variation minimization (TVM) method suffer from shading artifacts. Because the artifacts are distributed in several fixed directions, the artifact-suppression reconstruction model based on multi-direction total variation was designed for MLA CT reconstruction in this work. The multi-direction total variation minimization (MDTVM) was utilized to solve the optimization model. Experiments on digital phantoms and real projections indicated that MDTVM can better suppress the shading artifacts than TVM.

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