flEXPOSE: a flexible exposure parameter optimisation engine for x-ray projection imaging

OBJECTIVE: To develop and illustrate the potential of a new, flexible, open-source software engine for task-based optimisation of exposure parameter settings in x-ray projection imaging. Approach: The engine was built with several modules scripted in Python, to automate the different processes of optimisation of exposure parameters. Input is taken from a set of pre-calculated data containing image quality and dose metrics, and system parameters defined by the user. Modular code is employed, with classes responsible for image quality and dose calculations. For this study, the image quality (IQ) module incorporated the standard signal-to-noise ratio (SNR) and a version of SNR (SNRw) that is weighted for the influence of the x-ray focus size and finite x-ray pulse width on the task. Optimal x-ray factors for a specified task are established by the Optimizer class that maximizes an FOM defined as SNR2 or SNRw2 divided by dose. A set of six experiments with different degrees of complexity was performed to illustrate the engine and the influence of x-ray factor selection for a cardiac imaging task. Main Results: A full parameter search covering 2400 different combinations of tube potential, additional copper filtration and focal spot size for 11 distinct patient thicknesses took approximately 30 minutes. The six experiments demonstrated that it is essential to consider x-ray tube power limitations and, when applicable, object motion and dose limits when determining the optimal exposure parameters. Significance: The proposed engine automates optimal exposure parameter selection for user-defined image quality metrics and dose estimates. The influence of x-ray system parameters on system performance can be explored systematically. The engine is provided as an open-source resource with a modular structure that can be extended to include different figures of merit, image quality and dose metrics. The repository containing the engine is available at https://gitlab.kuleuven.be/medphysqa/deploy/flexpose/flexpose.