title: Proximal ADMM for multi-channel image reconstruction in spectral X-ray CT.
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.
authors.forename: Alex
authors.initials: A
authors.lastname: Sawatzky
authors.forename: Qiaofeng
authors.initials: Q
authors.lastname: Xu
authors.forename: Carsten O
authors.initials: CO
authors.lastname: Schirra
authors.forename: Mark A
authors.initials: MA
authors.lastname: Anastasio
mesh_terms: D000465:Algorithms; D000818:Animals; D003198:Computer Simulation; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008954:Models, Biological; D019047:Phantoms, Imaging; D013902:Radiography, Thoracic; D014057:Tomography, X-Ray Computed / Q000379:methods*