Search

Purpose: To implement a framework generating synthetic spectral panoramic images from single energy CT volumes. Using the framework output to compare the synthetic images against experimental spectral panoramic images for cross-verification. Methods: A simulation framework for generating synthetic spectral panoramic images from CT volumes is described. A cone beam CT scan of an anthropomorphic head phantom is used as input. An experimental spectral panoramic image of the same phantom is acquired. Results: The output of the framework of an anthropomorphic head phantom is compared against an experimental spectral panoramic image of the same phantom. The synthetic and experimental spectral panoramic images resemble each other considerably, especially the bone features. In the soft tissue images, there are some deviations, which are a result of the differences between the experimental and synthetic processing pipelines. Conclusions: It is demonstrated that generating synthetic spectral panoramic images from single energy CT volumes is possible. The synthetic images have many similarities with the experimental results, increasing the confidence in the correctness of the information contained within experimental spectral panoramic images and indicating that the synthetic images could be useful in further research.

Purpose: We aimed to estimate the total lung volume (TLV) from real and synthetic frontal X-ray radiographs on a pixel level using lung thickness maps generated by a U-Net. Methods: 5,959 thorax X-ray computed tomography (CT) scans were retrieved from two publicly available datasets of the lung nodule analysis 2016 (n=656) and the RSNA pulmonary embolism detection challenge 2020 (n=5,303). Additionally, thorax CT scans from 72 subjects (33 healthy: 20 men, mean age [range] = 62.4 [34, 80]; 39 suffering from chronic obstructive pulmonary disease: 25 men, mean age [range] = 69.0 [47, 91]) were retrospectively selected (10.2018-12.2019) from our in-house dataset such that for each subject, a frontal chest X-ray radiograph no older than seven days was available. All CT scans and their corresponding lung segmentation were forward projected using a simulated X-ray spectrum to generate synthetic radiographs and lung thickness maps, respectively. A U-Net model was trained and tested on synthetic radiographs from the public datasets to predict lung thickness maps and consequently estimate TLV. Model performance was further assessed by evaluating the TLV estimations for the in-house synthetic and real radiograph pairs using Pearson correlation coefficient (r) and significance testing. Results: Strong correlations were measured between the predicted and CT-derived ground truth TLV values for test data from synthetic ($n_{Public}$=1,191, r=0.987, P < 0.001; $n_{In-house}$=72, r=0.973, P < 0.001) and real radiographs (n=72, r=0.908, P < 0.001). Conclusion: TLV from U-Net-generated pixel-level lung thickness maps were successfully estimated for synthetic and real radiographs.

Dual-energy imaging is a clinically well-established technique that offers several advantages over conventional X-ray imaging. By performing measurements with two distinct X-ray spectra, differences in energy-dependent attenuation are exploited to obtain material-specific information. This information is used in various imaging applications to improve clinical diagnosis. In recent years, grating-based X-ray dark-field imaging has received increasing attention in the imaging community. The X-ray dark-field signal originates from ultra small-angle scattering within an object and thus provides information about the microstructure far below the spatial resolution of the imaging system. This property has led to a number of promising future imaging applications that are currently being investigated. However, different microstructures can hardly be distinguished with current X-ray dark-field imaging techniques, since the detected dark-field signal only represents the total amount of ultra small-angle scattering. To overcome these limitations, we present a novel concept called dual-energy X-ray dark-field material decomposition, which transfers the basic material decomposition approach from attenuation-based dual-energy imaging to the dark-field imaging modality. We develop a physical model and algorithms for dual-energy dark-field material decomposition and evaluate the proposed concept in experimental measurements. Our results suggest that by sampling the energy-dependent dark-field signal with two different X-ray spectra, a decomposition into two different microstructured materials is possible. Similar to dual-energy imaging, the additional microstructure-specific information could be useful for clinical diagnosis.

Spectral and grating-based differential phase-contrast X-ray imaging are two emerging technologies that offer additional information compared with conventional attenuation-based X-ray imaging. In the case of spectral imaging, energy-resolved measurements allow the generation of material-specific images by exploiting differences in the energy-dependent attenuation. Differential phase-contrast imaging uses the phase shift that an X-ray wave exhibits when traversing an object as contrast generation mechanism. Recently, we have investigated the combination of these two imaging techniques (spectral differential phase-contrast imaging) and demonstrated potential advantages compared with spectral imaging. In this work, we present a noise analysis framework that allows the prediction of (co-) variances and noise power spectra for all three imaging methods. Moreover, the optimum acquisition parameters for a particular imaging task can be determined. We use this framework for a performance comparison of all three imaging methods. The comparison is focused on (projected) electron density images since they can be calculated with all three imaging methods. Our study shows that spectral differential phase-contrast imaging enables the calculation of electron density images with strongly reduced noise levels compared with the other two imaging methods for a large range of clinically relevant pixel sizes. In contrast to conventional differential phase-contrast imaging, there are no long-range noise correlations for spectral differential phase-contrast imaging. This means that excessive low frequency noise can be avoided. We confirm the analytical predictions by numerical simulations.