A novel scatter separation method for multi-energy x-ray imaging

Abstract

X-ray imaging coupled with recently emerged energy-resolved photon counting detectors provides the ability to differentiate material components and to estimate their respective thicknesses. However, such techniques require highly accurate images. The presence of scattered radiation leads to a loss of spatial contrast and, more importantly, a bias in radiographic material imaging and artefacts in computed tomography (CT). The aim of the present study was to introduce and evaluate a partial attenuation spectral scatter separation approach (PASSSA) adapted for multi-energy imaging. This evaluation was carried out with the aid of numerical simulations provided by an internal simulation tool, Sindbad-SFFD. A simplified numerical thorax phantom placed in a CT geometry was used. The attenuation images and CT slices obtained from corrected data showed a remarkable increase in local contrast and internal structure detectability when compared to uncorrected images. Scatter induced bias was also substantially decreased. In terms of quantitative performance, the developed approach proved to be quite accurate as well. The average normalized root-mean-square error between the uncorrected projections and the reference primary projections was around 23%. The application of PASSSA reduced this error to around 5%. Finally, in terms of voxel value accuracy, an increase by a factor >10 was observed for most inspected volumes-of-interest, when comparing the corrected and uncorrected total volumes.

Real‐time scatter estimation for medical CT using the deep scatter estimation: Method and robustness analysis with respect to different anatomies, dose levels, tube voltages, and data truncation

“…Scatter estimation approaches, which are the focus of this manuscript, try to derive an estimate of the distribution of scattered x rays that is, subsequently, used to correct the acquired projection data . Here, the scatter distribution can either be estimated using dedicated hardware such as beam blockers or primary modulation grids or using software‐based approaches that rely on physical, empirical, or consistency‐based models to predict x‐ray scattering …”

Deep Scatter Estimation (DSE): Accurate Real-Time Scatter Estimation for X-Ray CT Using a Deep Convolutional Neural Network

“…While scatter suppression approaches try to reduce the amount of scattered X-rays reaching the detector using anti-scatter grids or collimators B Joscha Maier joscha.maier@dkfz.de 1 German Cancer Research Center, Im Neuenheimer Feld 280, 69120 Heidelberg, Germany [26], scatter estimation approaches aim at deriving an estimate of the scatter distribution that is used to correct the acquired projection data [27]. Thereby, the scatter estimate can either be derived using dedicated hardware such as beam blockers or primary modulation grids [3,7,8,20,24,28,38,39] or using software-based approaches that rely on physical or empirical models to predict X-ray scattering [1,2,11,17,18,21,22,30,[32][33][34]36,37]. Among these methods the gold standard is to use a Monte Carlo (MC) photon transport code [27].…”

Energy-sensitive scatter estimation and correction for spectral x-ray imaging with photon-counting detectors

“…However, this approach often requires two projections (with and without the grid) to accurately obtain the spatial scatter distribution from object. In the context of spectral x-ray imaging with PCDs, the use of partially-attenuating grids [35] and primary modulators [36] have been proposed. However, introducing these additional components [37] to the beam path often alters the patient dose and also results in image artifacts.…”