Iterative Image Reconstruction Using Non-Local Means With Total Variation From Insufficient Projection Data

Abstract

In this work, algebraic reconstruction technique (ART) is extended by using non-local means (NLM) and total variation (TV) for reduction of artifacts that are due to insufficient projection data. TV and NLM algorithms use different image models and their application in tandem becomes a powerful denoising method that reduces erroneous variations in the image while preserving edges and details. Simulations were performed on a widely used 2D Shepp-Logan phantom to demonstrate performance of the introduced method (ART + TV) NLM and compare it to TV based ART (ART + TV) and ART. The results indicate that (ART + TV) NLM achieves better reconstructions compared to (ART + TV) and ART.