16 research outputs found
Direct Iterative Reconstruction of Multiple Basis Material Images in Photon-counting Spectral CT
In this work, we perform direct material reconstruction from spectral CT data
using a model based iterative reconstruction (MBIR) approach. Material
concentrations are measured in volume fractions, whose total is constrained by
a maximum of unity. A phantom containing a combination of 4 basis materials
(water, iodine, gadolinium, calcium) was scanned using a photon-counting
detector. Iodine and gadolinium were chosen because of their common use as
contrast agents in CT imaging. Scan data was binned into 5 energy (keV) levels.
Each energy bin in a calibration scan was reconstructed, allowing the linear
attenuation coefficient of each material for every energy to be estimated by a
least-squares fit to ground truth in the image domain. The resulting matrix, for energies and materials, is incorporated into the forward
model in direct reconstruction of the basis material images with spatial
and/or inter-material regularization. In reconstruction from a subsequent
low-concentration scan, volume fractions within regions of interest (ROIs) are
found to be close to the ground truth. This work is meant to lay the foundation
for further work with phantoms including spatially coincident mixtures of
contrast materials and/or contrast agents in widely varying concentrations,
molecular imaging from animal scans, and eventually clinical applications
A Spectral CT Method to Directly Estimate Basis Material Maps From Experimental Photon-Counting Data
The proposed spectral CT method solves the constrained one-step spectral CT reconstruction (cOSSCIR) optimization problem to estimate basis material maps while modeling the nonlinear X-ray detection process and enforcing convex constraints on the basis map images. In order to apply the optimization-based reconstruction approach to experimental data, the presented method empirically estimates the effective energy-window spectra using a calibration procedure. The amplitudes of the estimated spectra were further optimized as part of the reconstruction process to reduce ring artifacts. A validation approach was developed to select constraint parameters. The proposed spectral CT method was evaluated through simulations and experiments with a photon-counting detector. Basis material map images were successfully reconstructed using the presented empirical spectral modeling and cOSSCIR optimization approach. In simulations, the cOSSCIR approach accurately reconstructed the basis map images
An algorithm for constrained one-step inversion of spectral CT data
We develop a primal-dual algorithm that allows for one-step inversion of
spectral CT transmission photon counts data to a basis map decomposition. The
algorithm allows for image constraints to be enforced on the basis maps during
the inversion. The derivation of the algorithm makes use of a local upper
bounding quadratic approximation to generate descent steps for non-convex
spectral CT data discrepancy terms, combined with a new convex-concave
optimization algorithm. Convergence of the algorithm is demonstrated on
simulated spectral CT data. Simulations with noise and anthropomorphic phantoms
show examples of how to employ the constrained one-step algorithm for spectral
CT data.Comment: Submitted to Physics in Medicine and Biolog