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

ABSTRACT:

Purpose

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.

Methods

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 dat

SUBMITTER: Ha W 

PROVIDER: S-EPMC6461446 | biostudies-literature | 2019 Jan

REPOSITORIES: biostudies-literature