Sequentially optimized projections in X-ray imaging

TL;DR

This paper presents a sequential Bayesian optimization method for selecting projection geometries in X-ray tomography, improving imaging efficiency by adaptively choosing angles and positions based on prior measurements.

Contribution

It introduces a greedy, sequential optimization algorithm that adapts projection parameters in real-time using Bayesian principles, considering both A- and D-optimality criteria.

Findings

Effective in 2D numerical experiments

Allows dynamic redefinition of the region of interest

Provides efficient evaluation of optimality criteria

Abstract

This work applies Bayesian experimental design to selecting optimal projection geometries in (discretized) parallel beam X-ray tomography assuming the prior and the additive noise are Gaussian. The introduced greedy exhaustive optimization algorithm proceeds sequentially, with the posterior distribution corresponding to the previous projections serving as the prior for determining the design parameters, i.e. the imaging angle and the lateral position of the source-receiver pair, for the next one. The algorithm allows redefining the region of interest after each projection as well as adapting parameters in the (original) prior to the measured data. Both A and D-optimality are considered, with emphasis on efficient evaluation of the corresponding objective functions. Two-dimensional numerical experiments demonstrate the functionality of the approach.