Sparsity-regularized image reconstruction of decomposed K-edge data in spectral CT

作者：Xu Qiaofeng^*; Sawatzky Alex; Anastasio Mark A; Schirra Carsten O

来源：Physics in Medicine and Biology, 2014, 59(10): N65-N79.

DOI：10.1088/0031-9155/59/10/N65

• computed tomography (CT)
• energy-resolved CT
• K-edge imaging
• material-decomposition
• statistical image reconstruction
• total variation regularization

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 l(1)-norm with a wavelet sparsifying transform. Computer-simulation and experimental phantom studies are conducted to quantitatively demonstrate the effectiveness of the proposed reconstruction algorithms.

• 出版日期2014-5-21