Sparse and limited-angle CT image reconstruction using dictionary learning constrained by L1 norm

Abstract

Reconstructed images from computed tomography (CT) using the algebraic reconstruction technique (ART) and simultaneous ART (SART) algorithms often suffer from obvious artefacts when only sparse and limited-angle projection data are available. Using the ability of dictionary learning (DL) in image feature extraction and sparse signal representation, a new iterative reconstruction algorithm, ART-DL-L1, is proposed to overcome the aforementioned limitations. This new algorithm is based on DL and an L1 norm constraint, combined with ART. An alternate iterative solving strategy based on an approach of 'ART first, then adaptive dictionary learning' is suggested and is explicitly described in a flowchart depicting the ART-DL-L1 algorithm. For both a noisy projection of 360° sparse data and limitedangle data of 120°, simulation reconstruction results from the classic Shepp-Logan image obtained using ART-DL-L1 appear to be better than those obtained using SART and total variation (TV) algorithms and also better than the cutting-edge ART-DL-L2 algorithm. Five evaluation metrics corresponding to the root-mean-square error (RMSE), the mean absolute error (MAE), the peak signal-to-noise ratio (PSNR), the residuals and the structural similarity (SSIM) index are adopted to estimate the reconstruction effect. The results suggest that the five metrics obtained using ART-DL-L1 outperform those obtained using the other three algorithms. The impact of using patches of various sizes played by the DL part in ART-DL-L1 is considered in the simulations and the patch size achieving the best reconstructed image quality is identified in this case as 25 (5 × 5). Overall, the proposed ART-DL-L1 algorithm may reduce artefacts and suppress noise from incomplete noisy projection CT imaging to some degree.