Abstract: In medical diagnosis， sparse sampling can reduce radiation damage to patients during CT scanning. However， direct reconstruction of sparse sampling projection data will cause distortion and artifacts in the reconstructed CT images. In order to ensure the quality of reconstructed images at low sampling rate， a dual dictionary adaptive learning algorithm is proposed， referring to the dual dictionary learning framework under the Sparse-Land model. K-SVD algorithm is combined with the dual dictionary learning algorithm framework to obtain patched projection data and FBP (filter back projection) algorithm is used to reconstruct high-quality reconstructed images. Experimental results show that the proposed method is superior to COMP double dictionary learning algorithm and MOD double dictionary learning algorithm in CT reconstruction at low sampling rate， and this method effectively improves the performance of CT image reconstruction at low sampling rate.

Key words: CT image reconstruction; K-SVD algorithm; dual dictionary learning algorithm; adaptive learning algorithm; FBP (filter back projection) algorithm