Two-shot X-ray dark-field imaging

Iterative signal retrieval for X-ray grating interferometry with dual-shot

BACKGROUND

X-ray grating interferometry normally requires multiple steps and exposures, causing a prolonged imaging time. There is motivation to use fewer steps to reduce scanning time and complexity, while keeping fidelity of the retrieved signals.

OBJECTIVE

We propose an iterative signal retrieval method, extracting attenuation, dark field contrast (DFC), and differential phase contrast (DPC) signals from two X-ray exposures.

METHODS

Two shots were captured at G2 grating positions with difference of 1/4 grating period. The algorithm consists of two stages. At the first stage, amplitude of sample phase stepping curve retrieved by virtual phase stepping (VPS) method, visibility and local phase of background phase stepping curve are used to limit the results to the proximity of the ground truth. After the second stage, three high-quality parameters, amplitude, visibility, and local phase, are retrieved through finetuning, and three signals are calculated. Simulated and real-sample experiments were conducted to validate this method.

RESULTS

We used standard phase stepping result as benchmark and calculated structural similarity (SSIM) and peak signal-to-noise ratio (PSNR) between benchmark and parameters retrieved by our dual-shot method and virtual phase stepping (VPS) method. For both simulated and real-sample experiments, the SSIM and PSNR value of dual-shot method are higher than those of VPS method. For real-sample method, we also conducted a three-step PS, and the SSIM and PSNR value of dual-shot method are slightly lower than those of three-step PS.

CONCLUSION

Using our dual-shot method demonstrates higher performance than other single-shot method in retrieving high-quality signals, and it also reduces radiation dose and time.

Generalized reverse projection method for grating-based phase tomography

The reverse projection protocol results in fast phase-contrast imaging thanks to its compatibility with conventional computed-tomography scanning. Many researchers have proposed variants. However, all these reverse projection methods in grating-based phase-contrast imaging are built on the hypothesis of the synchronous phase of reference shifting curves in the whole field of view. The hypothesis imposes uniformity and alignment requirements on the gratings, thus the field of view is generally limited. In this paper, a generalized reverse projection method is presented analytically for the case of non-uniform reference in grating-based phase tomography. The method is demonstrated by theoretical derivation, numerical simulations and synchrotron radiation experiments. The influence of imaging position to sensitivity, and the phase-wrapping phenomenon are also discussed. The proposed method combines the advantages of the high efficiency of the reverse projection method and the universal applicability of the phase-stepping method. The authors believe that the method would be used widely in fast and dose-constrained imaging.

Enhancing the X-Ray Differential Phase Contrast Image Quality With Deep Learning Technique

OBJECTIVE

The purpose of this work is to investigate the feasibility of using deep convolutional neural network (CNN) to improve the image quality of a grating-based X-ray differential phase contrast imaging (XPCI) system.

METHODS

In this work, a novel deep CNN based phase signal extraction and image noise suppression algorithm (named as XP-NET) is developed. The numerical phase phantom, the ex vivo biological specimen and the ACR breast phantom are evaluated via the numerical simulations and experimental studies, separately. Moreover, images are also evaluated under different low radiation levels to verify its dose reduction capability.

RESULTS

Compared with the conventional analytical method, the novel XP-NET algorithm is able to reduce the bias of large DPC signals and hence increasing the DPC signal accuracy by more than 15%. Additionally, the XP-NET is able to reduce DPC image noise by about 50% for low dose DPC imaging tasks.

CONCLUSION

This proposed novel end-to-end supervised XP-NET has a great potential to improve the DPC signal accuracy, reduce image noise, and preserve object details.

SIGNIFICANCE

We demonstrate that the deep CNN technique provides a promising approach to improve the grating-based XPCI performance and its dose efficiency in future biomedical applications.

Transmission, refraction and dark-field retrieval in hard X-ray grating interferometry

A three-image algorithm is proposed to retrieve the sample's transmission, refraction and dark-field information in hard X-ray grating interferometry. Analytical formulae of the three-image algorithm are theoretically derived and presented, and evaluated by proof-of-principle synchrotron radiation experiments. The results confirm the feasibility of the proposed algorithm. The novelty of the proposed algorithm is that it allows versatile and tunable multimodal X-ray imaging by substantially relaxing the existing limitations on the lateral grating position. Furthermore, this algorithm can also be adapted for samples with negligible refraction, reducing the number of required sample measurements to two. Furthermore, the noise properties of the retrieved images are investigated in terms of the standard deviations. Theoretical models are presented and verified by synchrotron radiation measurements. It is shown that the noise standard deviations exhibit strong dependence on the lateral grating position, especially in the case of refraction and dark-field images. Further noise reduction and dose reduction can thus be possible by optimizing the lateral grating position for a selected region of interest. Those results can serve as general guidelines to optimize the data acquisition scheme for specific applications and problems.

Automatic image-domain Moiré artifact reduction method in grating-based x-ray interferometry imaging

In this study, we propose to remove Moiré image artifact induced by system instabilities in grating-based x-ray interferometry imaging using convolutional neural network (CNN) technique. This method reduces Moiré image artifact in image-domain via a learned image post-processing procedure, rather than developing signal retrieval optimization algorithms to minimize the inconsistencies between acquired phase stepping data and assumed signal model. To achieve this aim, we suggested to train the CNN network using dataset synthesized from both natural images and experimentally acquired Moiré artifact-only images. In particular, a novel approach is developed to generate a large number of various high quality Moiré artifact-only images from finite groups of experimental phase stepping data. Both numerical and experimental results demonstrate that the developed CNN method is able to effectively remove the undesired Moiré image artifact. As a result, the image quality of a practical grating-based x-ray interferometry system can be greatly improved.

Improved reconstruction of phase-stepping data for Talbot-Lau x-ray imaging

Grating-based Talbot-Lau x-ray interferometry is a popular method for measuring absorption, phase shift, and small-angle scattering. The standard acquisition method for this modality is phase stepping, where the Talbot pattern is reconstructed from multiple images acquired at different grating positions. We review the implicit assumptions in phase-stepping reconstruction, and find that the assumptions of perfectly known grating positions and homoscedastic noise variance are violated in some scenarios. Additionally, we investigate a recently reported estimation bias in the visibility and dark-field signal. To adapt the phase-stepping reconstruction to these findings, we propose three improvements to the reconstruction. These improvements are (a) to use prior knowledge to compute more accurate grating positions to reduce moiré artifacts, (b) to utilize noise variance information to reduce dark-field and phase noise in high-visibility acquisitions, and (c) to perform correction of an estimation bias in the interferometer visibility, leading to more quantitative dark-field imaging in acquisitions with a low signal-to-noise ratio. We demonstrate the benefit of our methods on simulated data, as well as on images acquired with a Talbot-Lau interferometer.