Abstract
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.
Original language | English (US) |
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Pages (from-to) | N65-N79 |
Journal | Physics in medicine and biology |
Volume | 59 |
Issue number | 10 |
DOIs | |
State | Published - May 21 2014 |
Externally published | Yes |
Keywords
- computed tomography (CT)
- energy-resolved CT
- K-edge imaging
- material-decomposition
- statistical image reconstruction
- total variation regularization
ASJC Scopus subject areas
- Radiological and Ultrasound Technology
- Radiology Nuclear Medicine and imaging