Purpose: To demonstrate the dose reduction potential in CT using a novel algorithm (NC‐PICCS) based on a non‐convex l0 homotopic approximation. Methods: We generalized the Prior Image Constrained Compressed Sensing (PICCS) algorithm to solve an l0‐quasinorm by homotopic approximation using continuation on the parameter p (lp‐norm) which starts at p=1 and ends at p ≅ 0. The algorithm was validated using both computer simulations and in‐vivo animal studies. A pig perfusion study dataset was used to show the advantages of this novel method over existing compressed sensing approaches. Results: the NC‐PICCS method allows exact image reconstruction to be achieved from fewer projections than with methods such as standard CS or PICCS, both of which employ a convex l1 norm. For the synthetic data used, we were able to reconstruct the image with as few as 4 projections when a prior image was available in contrast to 12 projections without a prior image. When the method was applied to the in‐vivo pig perfusion data the number of projections required for exact reconstruction was about 20. Conclusions: the NC‐PICCS method provides a framework to highly reduce the dose in CT time series studies. Although there is no theoretical guarantee of finding a global minimum due to the non‐convex nature of NC‐PICCS, substantial empirical evidence suggests that it performs better than previously reported compressed sensing methods for CT reconstruction in practice.
ASJC Scopus subject areas
- Radiology Nuclear Medicine and imaging