Correction of Motion Artifacts in Dark-Field Radiography of the Human Chest

Correction for X-Ray Scatter and Detector Crosstalk in Dark-Field Radiography

Dark-field radiography, a new X-ray imaging method, has recently been applied to human chest imaging for the first time. It employs conventional X-ray devices in combination with a Talbot-Lau interferometer with a large field of view, providing both attenuation and dark-field radiographs. It is well known that sample scatter creates artifacts in both modalities. Here, we demonstrate that also X-ray scatter generated by the interferometer as well as detector crosstalk create artifacts in the dark-field radiographs, in addition to the expected loss of spatial resolution. We propose deconvolution-based correction methods for the induced artifacts. The kernel for detector crosstalk is measured and fitted to a model, while the kernel for scatter from the analyzer grating is calculated by a Monte-Carlo simulation. To correct for scatter from the sample, we adapt an algorithm used for scatter correction in conventional radiography. We validate the obtained corrections with a water phantom. Finally, we show the impact of detector crosstalk, scatter from the analyzer grating and scatter from the sample and their successful correction on dark-field images of a human thorax.

X-ray dark-field chest radiography: a reader study to evaluate the diagnostic quality of attenuation chest X-rays from a dual-contrast scanning prototype

OBJECTIVES

To compare the visibility of anatomical structures and overall quality of the attenuation images obtained with a dark-field X-ray radiography prototype with those from a commercial radiography system.

METHODS

Each of the 65 patients recruited for this study obtained a thorax radiograph at the prototype and a reference radiograph at the commercial system. Five radiologists independently assessed the visibility of anatomical structures, the level of motion artifacts, and the overall image quality of all attenuation images on a five-point scale, with 5 points being the highest rating. The average scores were compared between the two image types. The differences were evaluated using an area under the curve (AUC) based z-test with a significance level of p ≤ 0.05. To assess the variability among the images, the distributions of the average scores per image were compared between the systems.

RESULTS

The overall image quality was rated high for both devices, 4.2 for the prototype and 4.6 for the commercial system. The rating scores varied only slightly between both image types, especially for structures relevant to lung assessment, where the images from the commercial system were graded slightly higher. The differences were statistically significant for all criteria except for the bronchial structures, the cardiophrenic recess, and the carina.

CONCLUSIONS

The attenuation images acquired with the prototype were assigned a high diagnostic quality despite a lower resolution and the presence of motion artifacts. Thus, the attenuation-based radiographs from the prototype can be used for diagnosis, eliminating the need for an additional conventional radiograph.

KEY POINTS

• Despite a low tube voltage (70 kVp) and comparably long acquisition time, the attenuation images from the dark-field chest radiography system achieved diagnostic quality for lung assessment. • Commercial chest radiographs obtained a mean rating score regarding their diagnostic quality of 4.6 out of 5, and the grating-based images had a slightly lower mean rating score of 4.2 out of 5. • The difference in rating scores for anatomical structures relevant to lung assessment is below 5%.

[Dark-field imaging and computed tomography : Novel X-ray-based contrast imaging modality with great promise for pulmonary imaging]

INTRODUCTION

The spatial and contrast resolution of conventional planar or computed tomographic X‑ray techniques is not sufficient to investigate microstructures of tissues. Dark-field imaging with X‑rays is an emerging technology that recently provided the first clinical results and makes diagnostic use of interactions of the beams with tissue due to their wave character.

APPLICATION

Dark-field imaging can provide information about the microscopic structure or porosity of the tissue under investigation that is otherwise inaccessible. This makes it a valuable complement to conventional X‑ray imaging, which can only account for attenuation. Our results demonstrate that X‑ray dark-field imaging provides pictorial information about the underlying microstructure of the lung in humans. Given the close relationship between alveolar structure and the functional state of the lung, this is of great importance for diagnosis and therapy monitoring and may contribute to a better understanding of lung diseases in the future. In the early detection of chronic obstructive pulmonary disease, which is usually associated with structural impairment of the lung, this novel technique could help to facilitate its diagnosis.

PERSPECTIVE

The application of dark-field imaging to computed tomography is still under development because it is technically difficult. Meanwhile, a prototype for experimental application has been developed and is currently being tested on a variety of materials. Use in humans is conceivable especially for tissues whose microstructure favors characteristic interactions due to the wave nature of the X‑rays.

Deep-learning-based denoising of X-ray differential phase and dark-field images

PURPOSE

Statistical photon noise has always been a common problem in X-ray multi-contrast imaging and significantly influenced the quality of retrieved differential phase and dark-field images. We intend to develop a deep learning-based denoising algorithm to reduce the noise of retrieved X-ray differential phase and dark-field images.

METHODS

A novel deep learning based image noise suppression algorithm (named DnCNN-P) is presented. We proposed two different denoising modes: Retrieval-Denoising mode (R-D mode) and Denoising-Retrieval mode (D-R mode). While the R-D mode denoises the retrieved images, the D-R mode denoises the raw phase stepping data. The two denoising modes are evaluated under different photon counts and visibilities.

RESULTS

Experimental results show that with the algorithm DnCNN-P used, the D-R mode always exhibits a better noise reduction under diverse experimental conditions, even in the case of a low photon count and/or a low visibility. With a detected photon count of 1800 and a visibility of 0.3, compared to the differential phase images without denoising, the standard deviation is reduced by 89.1% and 16.4% in the D-R and R-D modes. Compared to the dark-field images without denoising, the standard deviation is reduced by 83.7% and 12.6% in the D-R and R-D modes, respectively.

CONCLUSIONS

The novel supervised DnCNN-P algorithm can significantly reduce the noise in retrieved X-ray differential phase and dark-field images. We believe this novel algorithm can be a promising approach to improve the quality of X-ray differential phase and dark-field images, and therefore dose efficiency in future biomedical applications.

Dark-field chest X-ray imaging for the assessment of COVID-19-pneumonia

BACKGROUND

Currently, alternative medical imaging methods for the assessment of pulmonary involvement in patients infected with COVID-19 are sought that combine a higher sensitivity than conventional (attenuation-based) chest radiography with a lower radiation dose than CT imaging.

METHODS

Sixty patients with COVID-19-associated lung changes in a CT scan and 40 subjects without pathologic lung changes visible in the CT scan were included (in total, 100, 59 male, mean age 58 ± 14 years). All patients gave written informed consent. We employed a clinical setup for grating-based dark-field chest radiography, obtaining both a dark-field and a conventional attenuation image in one image acquisition. Attenuation images alone, dark-field images alone, and both displayed simultaneously were assessed for the presence of COVID-19-associated lung changes on a scale from 1 to 6 (1 = surely not, 6 = surely) by four blinded radiologists. Statistical analysis was performed by evaluation of the area under the receiver-operator-characteristics curves (AUC) using Obuchowski's method with a 0.05 level of significance.

RESULTS

We show that dark-field imaging has a higher sensitivity for COVID-19-pneumonia than attenuation-based imaging and that the combination of both is superior to one imaging modality alone. Furthermore, a quantitative image analysis shows a significant reduction of dark-field signals for COVID-19-patients.

CONCLUSIONS

Dark-field imaging complements and improves conventional radiography for the visualisation and detection of COVID-19-pneumonia.