Backprojection regularization with weighted ramp filter for tomographic reconstruction

Zhenglin Wang, Ivan Lee

Abstract: Although filtered backprojection (FBP) is popular, backprojection then filtering (BPF) still receives a few attentions. Usually, BPF is inferior to FBP in terms of reconstruction quality. There are two main causes. First, BPF has to use a 2-dimensional discrete ramp filter formed by sampling the continuous ramp filter, resulting in DC shift and aliasing artefacts. Second, the common ramp filter amplifies high frequency noise much. To address such two issues, a weighted ramp filter is investigated to reduce the amplification of high frequency noise, and then a total-variation based backprojection regularization (BPR) method is developed to mitigate the DC shift and improve the robustness to noise. The experimental results show that BPR outperforms FBP for low-dose CT imaging reconstruction.