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.

Dose-efficient ultrahigh-resolution scan mode using a photon counting detector computed tomography system

“…[11][12][13][14][18][19][20][21][22][23] More importantly, the energy discrimination nature of the PCD enables a single-detector, single-source, and single-tubepotential multienergy imaging technique. 18,[24][25][26][27][28][29][30] Since PCDs use a direct conversion technique rather than the indirect conversion technique in EIDs, the requirement of septa between detector pixels can be avoided. Therefore, PCDs do not suffer from a septa or grid-induced reduced fill factor as detector cell size is decreased.…”

Model-based iterative reconstruction for flat-panel cone-beam CT with focal spot blur, detector blur, and correlated noise

“…Nearly all MBIR methods have made the assumption that the measurements are statistically independent. However, a few counterexamples can be found in the literature in Fourier rebinned PET (Alessio et al 2003) and in multi-energy CT reconstruction (Sawatzky et al 2014, Liu and Yu 2015, Brown et al 2015). While the independence assumption may be appropriate for some multi-detector CT systems, FP-CBCT data can exhibit significant spatial noise correlation due to the detection process (e.g., as part of the indirect x-ray detection and light spread in the scintillator).…”

A Spectral CT Method to Directly Estimate Basis Material Maps From Experimental Photon-Counting Data

“…From these basis sinograms, basis material map images can be reconstructed using conventional or iterative CT reconstruction techniques [5], [6], [7], [8]. This ‘two-step’ method is susceptible to noise, due to instability of the decomposition step.…”

“…Because of the complex factors affecting photon-counting detection, experimental studies are required to effectively evaluate reconstruction algorithms. Several iterative spectral CT reconstruction approaches have been recently investigated on experimental data, for example demonstrating the potential to reduce noise [8], [21], perform material decomposition [22], and improve contrast-to-noise ratio [23]. To our knowledge, this paper presents the first experimental implementation of a spectral CT iterative algorithm that both models the nonlinear polyenergetic X-ray transmission while enforcing convex constraints on the basis maps.…”