Sparsity-regularized image reconstruction of decomposed K-edge data in spectral CT.
The development of spectral computed tomography (CT) using binned photon-counting detectors has garnered great interest in recent years and has enabled selective imaging of K-edge materials. A practical challenge in CT image reconstruction of K-edge materials is the mitigation of image artifacts that arise from reduced-view and/or noisy decomposed sinogram data. In this note, we describe and investigate sparsity-regularized penalized weighted least squares-based image reconstruction algorithms for reconstructing K-edge images from few-view decomposed K-edge sinogram data. To exploit the inherent sparseness of typical K-edge images, we investigate use of a total variation (TV) penalty and a weighted sum of a TV penalty and an ℓ1-norm with a wavelet sparsifying transform. Computer-simulation and experimental phantom studies are conducted to quantitatively demonstrate the effectiveness of the proposed reconstruction algorithms.
Department of Biomedical Engineering, Washington University in St Louis, St Louis, MO, USA.
Qiaofeng
Q
Xu
Alex
A
Sawatzky
Mark A
MA
Anastasio
Carsten O
CO
Schirra
D003198:Computer Simulation; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D053768:Metal Nanoparticles; D019047:Phantoms, Imaging; D014057:Tomography, X-Ray Computed / Q000379:methods*; D015018:Ytterbium
D015018:Ytterbium