An x-ray CT polymer gel dosimetry prototype: I. Remnant artefact removal

Abstract

In this study a new x-ray CT polymer gel dosimetry (PGD) filtering technique is presented for the removal of (i) remnant ring and streak artefacts, and (ii) ‘structured’ noise in the form of minute, intrinsic gel density fluctuations. It is shown that the noise present within x-ray CT PGD images is not purely stochastic (pixel by pixel) in nature, but rather is ‘structured’, and hence purely stochastic-based noise-removal filters fail in removing this significant, unwanted noise component. The remnant artefact removal (RAR) technique is based on a class of signal stripping (i.e. baseline-estimation) algorithms typically used in the estimation of unwanted non-uniform baselines underlying spectral data. Here the traditional signal removal algorithm is recast, whereby the ‘signal’ that is removed is the structured noise and remnant artefacts, leaving the desired polymer gel dose distribution. The algorithm is extended to 2D and input parameters are optimized for PGD images. RAR filter results are tested on (i) synthetic images with measured gel background images added, in order to accurately represent actual noise present in PGD images, and (ii) PGD images of a three-field gel irradiation. RAR results are compared to a top-performing noise filter (adaptive mean, AM), used in previous x-ray CT PGD studies. It is shown that, in all cases, the RAR filter outperforms the AM filter, particularly in cases where either (i) a low-dose gel image has been acquired or (ii) the signal-to-noise ratio of the PG image is low, as in the case when a low number of image averages are acquired within a given experiment. Guidelines for the implementation of the RAR filter are given.