A comparison between Filtered Backprojection, Post-Smoothed Weighted Least Squares, and Penalized Weighted Least Squares for CT reconstruction

We examined the impact of several CT reconstruction approaches on image noise and spatial resolution. Reconstructions were performed using filtered backprojection (FBP) with ramp and low pass filters, weighted least squares (WLS) iterative reconstruction (IR) with ordered subsets and post-smoothing, and penalized weighted least squares (PWLS) IR with ordered subsets using a generalized Geman prior, varying the exponent and the strength of the prior. The results show that WLS could have a significant noise-resolution benefit relative to FBP, depending on the object and the corresponding statistics of the measurements. The use of priors (PWLS) can further reduce the image noise compared to non-penalized WLS. The spatial resolution is very well preserved for larger contrasts/edges and for larger values of n, but the performance is more comparable to FBP for smaller contrasts/edges and for smaller values of n. Penalized IR has the potential of improving spatial resolution and reducing image noise when compared to post-smoothed non-penalized IR and FBP.