Estimating the spectrum in computed tomography via Kullback-Leibler divergence constrained optimization

TL;DR

This paper introduces a convex optimization method using Kullback-Leibler divergence constraints for accurate and robust x-ray spectrum estimation in computed tomography, demonstrating effectiveness on simulated and experimental data.

Contribution

It proposes a novel convex optimization framework with KL divergence constraints for spectrum estimation, improving stability and incorporating prior knowledge.

Findings

The method accurately reconstructs ground truth spectra in simulations.

Experimental results show good agreement with measured transmission curves.

The approach performs comparably to the expectation-maximization method.

Abstract

We study the problem of spectrum estimation from transmission data of a known phantom. The goal is to reconstruct an x-ray spectrum that can accurately model the x-ray transmission curves and reflects a realistic shape of the typical energy spectra of the CT system. To this end, spectrum estimation is posed as an optimization problem with x-ray spectrum as unknown variables, and a Kullback-Leibler (KL) divergence constraint is employed to incorporate prior knowledge of the spectrum and enhance numerical stability of the estimation process. The formulated constrained optimization problem is convex and can be solved efficiently by use of the exponentiated-gradient (EG) algorithm. We demonstrate the effectiveness of the proposed approach on the simulated and experimental data. The comparison to the expectation-maximization (EM) method is also discussed. In simulations, the proposed algorithm is seen to yield x-ray spectra that closely match the ground truth and represent the attenuation process of x-ray photons in materials, both included and not included in the estimation process. In experiments, the calculated transmission curve is in good agreement with the measured transmission curve, and the estimated spectra exhibits physically realistic looking shapes. The results further show the comparable performance between the proposed optimization-based approach and EM. In conclusion, our formulation of a constrained optimization provides an interpretable and flexible framework for spectrum estimation. Moreover, a KL-divergence constraint can include a prior spectrum and appears to capture important features of x-ray spectrum, allowing accurate and robust estimation of x-ray spectrum in CT imaging.