Sparse-view computed tomography image reconstruction via a combination of L1 and SL0 regularization

Low-dose computed tomography reconstruction is an important issue in the medical imaging domain. Sparse-view has been widely studied as a potential strategy. Compressed sensing (CS) method has shown great potential to reconstruct high-quality CT images from sparse-view projection data. Nonetheless, low-contrast structures tend to be blurred by the total variation (TV, L-1-norm of the gradient image) regularization. Moreover, TV will produce blocky effects on smooth and edge regions. To overcome this limitation, this study has proposed an iterative image reconstruction algorithm by combining L-1 regularization and smoothed L-0 (SL0) regularization. SL0 is a smooth approximation of L-0 norm and can solve the problem of L-0 norm being sensitive to noise. To evaluate the proposed method, both qualitative and quantitative studies were conducted on a digital Shepp-Logan phantom and a real head phantom. Experimental comparative results have indicated that the proposed L-1/SL0-POCS algorithm can effectively suppress noise and artifacts, as well as preserve more structural information compared to other existing methods.