Affiliations: Department of Engineering Physics, Tsinghua
University, Beijing, China | Key Laboratory of Particle {and} Radiation Imaging
(Tsinghua University), Ministry of Education, Beijing, China | VT-WFU School of Biomedical Engineering and Sciences,
Virginia Tech, VA, USA
Note: [] Corresponding author: Zhiqiang Chen, Department of Engineering
Physics, Tsinghua University, Beijing 100084, China. E-mail:
[email protected]
Abstract: In recent years, the total variation (TV) minimization method has
been widely used for compressed sensing (CS) based CT image reconstruction. In
this paper, we propose a few-view reweighted sparsity hunting (FRESH) method
for CT image reconstruction, and demonstrate the superior performance of this
method. Specifically, the key of the purposed method is that a reweighted total
variation (RwTV) measure is used to characterize image sparsity in the cost
function, outperforming the conventional TV counterpart. To solve the RwTV
minimization problem efficiently, the Split-Bregman method and other
state-of-the-art L1 optimization methods are compared. Inspired by the fast
iterative shrinkage/thresholding algorithm (FISTA), a predication step is
incorporated for fast computation in the Split-Bregman framework. Extensive
numerical experiments have shown that our FRESH approach performs significantly
better than competing algorithms in terms of image quality and convergence
speed for few-view CT. High-quality images were reconstructed by our FRESH
method after 250 iterations using only 15 few-view projections of the Forbild
head phantom while other competitors needed more than 800 iterations.
Remarkable improvements in details in the experimental evaluation using actual
sheep thorax data further indicate the potential real-world application of the FRESH method.
