The principles and effectiveness of X-ray scatter correction software for diagnostic X-ray imaging: A scoping review

Scatter estimation and correction using time-of-flight and deconvolution in x-ray medical imaging

Objective.Time-of-flight (TOF) scatter rejection requires a total timing jitter, including the detector timing jitter and the x-ray source's pulses width, of 50 ps or less to mitigate most of the effects of scattered photons in radiography and CT imaging. However, since the total contribution of the source and detector to the timing jitter can be retrieved during an acquisition with nothing between the source and detector, it can be demonstrated that this contribution may be partially removed to improve the image quality.Approach.A scatter correction method using iterative deconvolution of the measured time point-spread function estimates the number of scattered photons detected in each pixel. To evaluate the quality of the estimation, GATE was used to simulate the radiography of a water cylinder with bone inserts, and a head and torso in a system with total timing jitters from 100 ps up to 500 ps full-width-at-half-maximum (FWHM).Main results.With a total timing jitter of 200 ps FWHM, 89% of the contrast degradation caused by scattered photons was recovered in a head and torso radiography, compared to 28% with a simple time threshold method. Corrected images using the estimation have a percent root-mean square error between 2% and 14% in both phantoms with timing jitters from 100 to 500 ps FWHM which is lower than the error achieved with scatter rejection alone at 100 ps FWHM.Significance.TOF x-ray imaging has the potential to mitigate the effects of the scattering contribution and offers an alternative to anti-scatter grids that avoids loss of primary photons. Compare to simple TOF scatter rejection using only a threshold, the deconvolution estimation approach has lower requirements on both the source and detector. These requirements are now within reach of state-of-the-art systems.

Evaluation of Virtual Grid processed clinical pelvic radiographs

BACKGROUND

A physical scatter grid is not often used in pelvic bedside examinations. However, multiple studies regarding scatter correction software (SC SW) are available for mobile chest radiography but the results are unclear for pelvic radiography.

PURPOSE

We evaluated SC SW of Fujifilm (Virtual Grid) on gridless pelvic radiographs obtained from a human Thiel-embalmed body to investigate the potential of Virtual Grid in pelvic bedside examinations.

METHODS

Gridless, Virtual Grid, and physical grid pelvic radiographs of a female Thiel-embalmed body were collected with a broad range of tube loads. Different software (SW) grid ratios-6:1, 10:1, 13:1, 17:1, and 20:1-were applied on the gridless radiographs to investigate the image quality (IQ) improvement of 13 IQ criteria in a visual grading analysis (VGA) setup.

RESULTS

Gridless radiograph scores are significantly lower (p < 0.001) than Virtual Grid and physical grid scores obtained with the same tube load. Virtual Grid radiographs score better than gridless radiographs obtained with a higher tube load which makes a dose reduction possible. The averaged ratings of the IQ criteria processed with different SW ratios increase with increasing SW grid ratios. However, no statistically significant differences were found between the SW grid ratios. The scores of the physical grid radiographs are higher than those of the Virtual Grid radiographs when they are obtained with the same tube load.

CONCLUSION

We conclude that Virtual Grid with an SW ratio of 6:1 improves the IQ of gridless pelvic radiographs in such a manner that a dose reduction is possible. However, physical grid radiograph ratings are higher compared to those of Virtual Grid radiographs.

Time-of-flight scatter rejection in x-ray radiography

Objective.Time-of-flight (TOF) scatter rejection allows for identifying and discarding scattered photons without the use of an anti-scatter grid (ASG). Although TOF scatter rejection was initially presented for cone-beam computed tomography, we propose, herein, to extend this approach to x-ray radiography. This work aims to evaluate with simulations if TOF scatter rejection can outperform ASGs for radiography.Approach.GATE was used to simulate the radiography of a head and a torso and a water cylinder with bone inserts in a system with total timing jitters from 0 ps up to 500 ps full-width-at-half-maximum. The transmission factor of TOF scatter rejection for primary and scattered photons was evaluated as if it were a virtual ASG.Main results.With a total timing jitter of 50 ps, TOF scatter rejection can reach a selectivity of 4.93 with a primary photons transmission of 99%. Reducing the timing jitter close to 0 ps increases the selectivity up to 15.85 for a head and torso radiography, outperforming typical ASGs which usually have a selectivity from 2.5 to 10 with a primary photons transmission from 50% to 70%.Significance.This suggests that TOF scatter rejection may be suitable to replace ASGs in applications requiring lower radiation exposure if sufficiently low timing jitter is achieved.

Improving input contrast estimation to an x-ray imaging system

Objective.An appropriate parameter to study the performance of an x-ray imaging system is contrast transfer, or the system's ability to capture contrast in the radiation beam and bring it to the image. However, determining the input contrast to the system is difficult, as it is heavily affected by secondary radiation, which in turn depends on a multitude of factors. This work presents a method to improve the calculation of input contrast to the imaging system when PMMA phantoms are used.Approach.An expression to obtain input contrast from primary radiation attenuation and scatter-to-primary ratio is shown, and the approximation upon which it is based is discussed. Primary and secondary radiation emerging from the phantoms are calculated for monoenergetic pencil beams impinging on planar PMMA phantoms of different thicknesses. Monte Carlo simulations of two types of anti-scatter grids are also incorporated into the calculations.Main results.The primary and secondary components of radiant energy and grid transmission factors are presented for monoenergetic beams with energies from 10 to 150 keV. These results are then used to calculate input contrast for polyenergetic beams when using a commercial image quality phantom combined with different thicknesses of PMMA and anti-scatter grids.Significance.The information of the object contrast carried by the beam constitutes the input to the imaging system. An accurate determination of this input contrast can be carried out in a wide variety of situations from the study of a reduced number of cases, as those presented in this work for monoenergetic beams, PMMA phantoms of different thicknesses and anti-scatter grids. The relationship between the input contrast and the contrast due to primary radiation used in this work provides a good approximation for the different combinations of inserts, phantoms, grids, and energy spectra analyzed here.

Noise Reduction for a Virtual Grid Using a Generative Adversarial Network in Breast X-ray Images

In this study, we aimed to address the issue of noise amplification after scatter correction when using a virtual grid in breast X-ray images. To achieve this, we suggested an algorithm for estimating noise level and developed a noise reduction algorithm based on generative adversarial networks (GANs). Synthetic scatter in breast X-ray images were collected using Sizgraphy equipment and scatter correction was performed using dedicated software. After scatter correction, we determined the level of noise using noise-level function plots and trained a GAN using 42 noise combinations. Subsequently, we obtained the resulting images and quantitatively evaluated their quality by measuring the contrast-to-noise ratio (CNR), coefficient of variance (COV), and normalized noise-power spectrum (NNPS). The evaluation revealed an improvement in the CNR by approximately 2.80%, an enhancement in the COV by 12.50%, and an overall improvement in the NNPS across all frequency ranges. In conclusion, the application of our GAN-based noise reduction algorithm effectively reduced noise and demonstrated the acquisition of improved-quality breast X-ray images.

Deep Learning Neural Network Performance on NDT Digital X-ray Radiography Images: Analyzing the Impact of Image Quality Parameters-An Experimental Study

In response to the growing inspection demand exerted by process automation in component manufacturing, non-destructive testing (NDT) continues to explore automated approaches that utilize deep-learning algorithms for defect identification, including within digital X-ray radiography images. This necessitates a thorough understanding of the implication of image quality parameters on the performance of these deep-learning models. This study investigated the influence of two image-quality parameters, namely signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR), on the performance of a U-net deep-learning semantic segmentation model. Input images were acquired with varying combinations of exposure factors, such as kilovoltage, milli-ampere, and exposure time, which altered the resultant radiographic image quality. The data were sorted into five different datasets according to their measured SNR and CNR values. The deep-learning model was trained five distinct times, utilizing a unique dataset for each training session. Training the model with high CNR values yielded an intersection-over-union (IoU) metric of 0.9594 on test data of the same category but dropped to 0.5875 when tested on lower CNR test data. The result of this study emphasizes the importance of achieving a balance in training dataset according to the investigated quality parameters in order to enhance the performance of deep-learning segmentation models for NDT digital X-ray radiography applications.