Simulation of dose reduction in tomosynthesis

Abstract

Methods for simulating dose reduction are valuable tools in the work of optimizing radiographic examinations. Using such methods, clinical images can be simulated to have been collected at other, lower, dose levels without the need of additional patient exposure. A recent technology introduced to healthcare that needs optimization is tomosynthesis, where a number of low-dose projection images collected at different angles is used to reconstruct section images of an imaged object. The aim of the present work was to develop a method of simulating dose reduction for digital radiographic systems, suitable for tomosynthesis. The developed method uses information about the noise power spectrum (NPS) at the original dose level and the simulated dose level to create a noise image that is added to the original image to produce an image that has the same noise properties as an image actually collected at the simulated dose level. As the detective quantum efficiency (DQE) of digital detectors operating at the low dose levels used for tomosynthesis may show a strong dependency on the dose level, it is important that a method for simulating dose reduction for tomosynthesis takes this dependency into account. By applying an experimentally determined relationship between pixel mean and pixel variance, variations in both dose and DQE in relevant dose ranges are taken into account. The developed method was tested on a chest tomosynthesis system and was shown to produce NPS of simulated dose-reduced projection images that agreed well with the NPS of images actually collected at the simulated dose level. The simulated dose reduction method was also applied to tomosynthesis examinations of an anthropomorphic chest phantom, and the obtained noise in the reconstructed section images was very similar to that of an examination actually performed at the simulated dose level. In conclusion, the present article describes a method for simulating dose reduction suitable for tomosynthesis. However, the method applies equally well to any digital radiographic system, although the benefits of correcting for DQE variations may be smaller.