Abstract
The development of spectral X-ray computed tomography (CT) using binned photon-counting detectors has received great attention in recent years and has enabled selective imaging of contrast agents loaded with K-edge materials. A practical issue in implementing this technique is the mitigation of the high-noise levels often present in material-decomposed sinogram data. In this work, the spectral X-ray CT reconstruction problem is formulated within a multi-channel (MC) framework in which statistical correlations between the decomposed material sinograms can be exploited to improve image quality. Specifically, a MC penalized weighted least squares (PWLS) estimator is formulated in which the data fidelity term is weighted by the MC covariance matrix and sparsity-promoting penalties are employed. This allows the use of any number of basis materials and is therefore applicable to photon-counting systems and K-edge imaging. To overcome numerical challenges associated with use of the full covariance matrix as a data fidelity weight, a proximal variant of the alternating direction method of multipliers is employed to minimize the MC PWLS objective function. Computer-simulation and experimental phantom studies are conducted to quantitatively evaluate the proposed reconstruction method.
Original language | English (US) |
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Article number | 6808543 |
Pages (from-to) | 1637-1668 |
Number of pages | 32 |
Journal | IEEE Transactions on Medical Imaging |
Volume | 33 |
Issue number | 8 |
DOIs | |
State | Published - Aug 2014 |
Externally published | Yes |
Keywords
- Energy-resolved X-ray computed tomography (CT)
- K-edge imaging
- material-decomposition
- multi-channel image reconstruction
- sparsity-promoting regularization
- statistical image reconstruction
- total variation regularization
ASJC Scopus subject areas
- Software
- Radiological and Ultrasound Technology
- Computer Science Applications
- Electrical and Electronic Engineering