Sparse Tomographic Image Reconstruction Method Using Total Variation and Non-Local Means

Abstract

Patient radiation dose is a major issue in computerized tomography (CT) imaging. Therefore, many improvements to the classical reconstruction algorithms are suggested to achieve reasonable image quality with less patient dose. The aim of this work is to improve the well-known algebraic reconstruction algorithm (ART) in order to obtain good image quality with less or limited projection angles. We achieve this purpose by sequential application of ART update, total variation minimization (TV), and non-local means (NLM). Both TV and NLM are widely used in imaging algorithms with high performance. To show the improvement in ART by TV and NLM we used a Shepp-Logan phantom simulation and real data from digital tomosynthesis imaging system. Our results indicate that the proposed method provided superior results over two widely used methods, ART and ART+TV, in many senses including Structure SIMilarity (SSIM), signal to noise ratio (SNR) and root mean squared error (RMSE).