Evaluation of Spectral X-Ray Imaging for Panoramic Dental Images Based on a Simulation Framework.

Modern photon counting detectors allow the calculation of virtual monoenergetic or material decomposed X-ray images but are not yet used for dental panoramic radiography systems. To assess the diagnostic potential and image quality of photon counting detectors in dental panoramic radiography, ethics approval from the local ethics committee was obtained for this retrospective study. Conventional CT scans of the head and neck region were segmented into bone and soft tissue. The resulting datasets were used to calculate panoramic equivalent thickness bone and soft tissue images by forward projection, using a geometry like that of conventional panoramic radiographic systems. The panoramic equivalent thickness images were utilized to generate synthetic conventional panoramic radiographs and panoramic virtual monoenergetic radiographs at various energies. The conventional, two virtual monoenergetic images at 40 keV and 60 keV, and material-separated bone and soft tissue panoramic equivalent thickness X-ray images simulated from 17 head CTs were evaluated in a reader study involving three experienced radiologists regarding their diagnostic value and image quality. Compared to conventional panoramic radiographs, the material-separated bone panoramic equivalent thickness image exhibits a higher image quality and diagnostic value in assessing the bone structure p < . 001 and details such as teeth or root canals p < . 001 . Panoramic virtual monoenergetic radiographs do not show a significant advantage over conventional panoramic radiographs. The conducted reader study shows the potential of spectral X-ray imaging for dental panoramic imaging to improve the diagnostic value and image quality.

Spectral X-ray dark-field signal characterization from dual-energy projection phase-stepping data with a Talbot-Lau interferometer.

Material-selective analysis of spectral X-ray imaging data requires prior knowledge of the energy dependence of the observed signal. Contrary to conventional X-ray imaging, where the material-specific attenuation coefficient is usually precisely known, the linear diffusion coefficient of the X-ray dark-field contrast does not only depend on the material and its microstructure, but also on the setup geometry and is difficult to access. Here, we present an optimization approach to retrieve the energy dependence of the X-ray dark-field signal quantitatively on the example of closed-cell foams from projection data without the need for additional hardware to a standard grating-based X-ray dark-field imaging setup. A model for the visibility is used to determine the linear diffusion coefficient with a least-squares optimization. The comparison of the results to spectrometer measurements of the linear diffusion coefficient suggests the proposed method to provide a good estimate for the energydependent dark-field signal.

Direct Differentiation of Pathological Changes in the Human Lung Parenchyma With Grating-Based Spectral X-ray Dark-Field Radiography.

Diagnostic lung imaging is often associated with high radiation dose and lacks sensitivity, especially for diagnosing early stages of structural lung diseases. Therefore, diagnostic imaging methods are required which provide sound diagnosis of lung diseases with a high sensitivity as well as low patient dose. In small animal experiments, the sensitivity of grating-based X-ray dark-field imaging to structural changes in the lung tissue was demonstrated. The energy-dependence of the X-ray dark-field signal of lung tissue is a function of its microstructure and not yet known. Furthermore, conventional X-ray dark-field imaging is not capable of differentiating different types of pathological changes, such as fibrosis and emphysema. Here we demonstrate the potential diagnostic power of grating-based X-ray dark-field in combination with spectral imaging in human chest radiography for the direct differentiation of lung diseases. We investigated the energy-dependent linear diffusion coefficient of simulated lung tissue with different diseases in wave-propagation simulations and validated the results with analytical calculations. Additionally, we modeled spectral X-ray dark-field chest radiography scans to exploit these differences in energy-dependency. The results demonstrate the potential to directly differentiate structural changes in the human lung. Consequently, grating-based spectral X-ray dark-field imaging potentially contributes to the differential diagnosis of structural lung diseases at a clinically relevant dose level.

Dual-Energy X-Ray Dark-Field Material Decomposition.

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.

Grating-based spectral X-ray dark-field imaging for correlation with structural size measures.

X-ray dark-field (XDF) imaging accesses information on the small-angle scattering properties of the sample. With grating interferometry, the measured scattering signal is related to the sample's autocorrelation function, which was previously demonstrated for simple samples, such as mono-dispersed microspheres for which the autocorrelation function is mathematically given. However, in potential clinical applications of XDF imaging, complex microstructures, such as lung parenchyma are under investigation. Their bahaviour in XDF imaging is not yet known and no mathematical description of the autocorrelation function is derived so far. In this work we demonstrate the previously established correlation of the XDF data of complex sample structures with their autocorrelation function to be impractical. Furthermore, we propose an applicable correlation between XDF and the sample's structural parameter on the basis of mean chord length, a medically-approved measure for alveolar structure, known to be affected by structural lung diseases. Our findings reveal a correlation between energy-dependent XDF imaging and the sample's mean chord length. By that, a connection between a medical measure for alveoli and XDF is achieved, which is particularly important regarding potential future XDF lung imaging applications for the assessment of alveoli size in diagnostic lung imaging.

Photon-counting spectral basis component material decomposition for musculoskeletal radiographs.

As a very fast and non-invasive examination, conventional X-ray radiography is well established as the first line diagnostic imaging method of the human bone system. While major bone injuries such as fractures and dislocations are usually easily detectable on conventional X-ray images, more subtle injuries such as microfractures are often missed, leading to mistreatment and potential long-term consequences. The technology of Photon-Counting Dual-Energy Radiography (PCDER) yields the possibility to decompose conventional X-ray images into basis material images such as bone- and soft-tissue-equivalence images. The obtained basis material images offer significant advantages in terms of image contrast and image details over the raw attenuation image which shows an overlap of bone and soft tissue. Whereas the advantages of bone- and soft-tissue-equivalence images have been broadly discussed referring to bone subtraction images in the detection of pulmonary diseases, this method has not been considered for the analysis of musculoskeletal images until present. In this study we show that basis component equivalence images have high potential to improve the diagnostic accuracy of the detection of minor bone lesions during clinical trauma imaging. A reader study performed by three experienced radiologists compares the image quality of basis material images to a standard radiograph image of a non-fractured cadaveric hand.

Single spectrum three-material decomposition with grating-based x-ray phase-contrast CT.

Grating-based x-ray phase-contrast imaging provides three simultaneous image channels originating from a single image acquisition. While the phase signal provides direct access to the electron density in tomography, there is additional information on sub-resolutional structural information which is called dark-field signal in analogy to optical microscopy. The additional availability of the conventional attenuation image qualifies the method for implementation into existing diagnostic routines. The simultaneous access to the attenuation coefficient and the electron density allows for quantitative two-material discrimination as demonstrated lately for measurements at a quasi-monochromatic compact synchrotron source. Here, we investigate the transfer of the method to conventional polychromatic x-ray sources and the additional inclusion of the dark-field signal for three-material decomposition. We evaluate the future potential of grating-based x-ray phase-contrast CT for quantitative three-material discrimination for the specific case of early stroke diagnosis at conventional polychromatic x-ray sources. Compared to conventional CT, the method has the potential to discriminate coagulated blood directly from contrast agent extravasation within a single CT acquisition. Additionally, the dark-field information allows for the clear identification of hydroxyapatite clusters due to their micro-structure despite a similar attenuation as the applied contrast agent. This information on materials with sub-resolutional microstructures is considered to comprise advantages relevant for various pathologies.

A theoretical framework for comparing noise characteristics of spectral, differential phase-contrast and spectral differential phase-contrast x-ray imaging.

Spectral and grating-based differential phase-contrast (DPC) 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. DPC 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 DPC 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 DPC 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 DPC imaging, there are no long-range noise correlations for spectral DPC imaging. This means that excessive low frequency noise can be avoided. We confirm the analytical predictions by numerical simulations.

Spectral Differential Phase Contrast X-Ray Radiography.

We investigate the combination of two emerging X-ray imaging technologies, namely spectral imaging and differential phase contrast imaging. By acquiring spatially and temporally registered images with several different X-ray spectra, spectral imaging can exploit differences in the energy-dependent attenuation to generate material selective images. Differential phase contrast imaging uses an entirely different contrast generation mechanism: The phase shift that an X-ray wave exhibits when traversing an object. As both methods can determine the (projected) electron density, we propose a novel material decomposition algorithm that uses the spectral and the phase contrast information simultaneously. Numerical experiments show that the combination of these two imaging techniques benefits from the strengths of the individual methods while the weaknesses are mitigated: Quantitatively accurate basis material images are obtained and the noise level is strongly reduced, compared to conventional spectral X-ray imaging.

Dynamic Quantitative Iodine Myocardial Perfusion Imaging with Dual-Layer CT using a Porcine Model.

Ischemic heart disease is the globally leading cause of death. When using coronary CT angiography, the functional hemodynamics within the myocardium remain uncertain. In this study myocardial CT perfusion imaging using iodine contrast agent demonstrated to strongly improve the assessment of myocardial disorders. However, a retrieval of such dynamics using Hounsfield units from conventional CT poses concerns with respect to beam-hardening effects and low contrast-to-noise ratio (CNR). Dual-energy CT offers novel approaches to overcome aforementioned limitations. Quantitative peak enhancement, perfusion, time to peak and iodine volume measurements inside the myocardium were determined resulting in 0.92 mg/ml, 0.085 mg/ml/s 17.12 s and 29.89 mg/ml*s, respectively. We report on the first extensive quantitative and iodine-based analysis of myocardial dynamics in a healthy porcine model using a dual-layer spectral CT. We further elucidate on the potential of reducing the radiation dose from 135 to 18 mGy and the contrast agent volume from 60 to 30 mL by presenting a two-shot acquisition approach and measuring iodine concentrations in the myocardium in-vivo down to 1 mg/ml, respectively. We believe that dynamic quantitative iodine perfusion imaging may be a highly sensitive tool for the precise functional assessment and monitoring of early myocardial ischemia.

Contrast-enhanced spectral mammography with a compact synchrotron source.

For early breast cancer detection, mammography is nowadays the commonly used standard imaging approach, offering a valuable clinical tool for visualization of suspicious findings like microcalcifications and tumors within the breast. However, due to the superposition of anatomical structures, the sensitivity of mammography screening is limited. Within the last couple of years, the implementation of contrast-enhanced spectral mammography (CESM) based on K-edge subtraction (KES) imaging helped to improve the identification and classification of uncertain findings. In this study, we introduce another approach for CESM based on a two-material decomposition, with which we expect fundamental improvements compared to the clinical procedure. We demonstrate the potential of our proposed method using the quasi-monochromatic radiation of a compact synchrotron source-the Munich Compact Light Source (MuCLS)-and a modified mammographic accreditation phantom. For direct comparison with the clinical CESM approach, we also performed a standard dual-energy KES at the MuCLS, which outperformed the clinical CESM images in terms of contrast-to-noise ratio (CNR) and spatial resolution. However, the dual-energy-based two-material decomposition approach achieved even higher CNR values. Our experimental results with quasi-monochromatic radiation show a significant improvement of the image quality at lower mean glandular dose (MGD) than the clinical CESM. At the same time, our study indicates the great potential for the material-decomposition instead of clinically used KES to improve the quantitative outcome of CESM.

Quantitative dual-energy micro-CT with a photon-counting detector for material science and non-destructive testing.

The recent progress in photon-counting detector technology using high-Z semiconductor sensors provides new possibilities for spectral x-ray imaging. The benefits of the approach to extract spectral information directly from measurements in the projection domain are very advantageous for material science studies with x-rays as polychromatic artifacts like beam-hardening are handled properly. Since related methods require accurate knowledge of all energy-dependent system parameters, we utilize an adapted semi-empirical model, which relies on a simple calibration procedure. The method enables a projection-based decomposition of photon-counting raw-data into basis material projections. The objective of this paper is to investigate the method's performance applied to x-ray micro-CT with special focus on applications in material science and non-destructive testing. Projection-based dual-energy micro-CT is shown to be of good quantitative accuracy regarding material properties such as electron densities and effective atomic numbers. Furthermore, we show that the proposed approach strongly reduces beam-hardening artifacts and improves image contrast at constant measurement time.

Publisher Correction: Direct quantitative material decomposition employing grating-based X-ray phase-contrast CT.

A correction to this article has been published and is linked from the HTML and PDF versions of this paper. The error has not been fixed in the paper.

DXA-equivalent quantification of bone mineral density using dual-layer spectral CT scout scans.

OBJECTIVES: To develop and evaluate a method for areal bone mineral density (aBMD) measurement based on dual-layer spectral CT scout scans. METHODS: A post-processing algorithm using a pair of 2D virtual mono-energetic scout images (VMSIs) was established in order to semi-automatically compute the aBMD at the spine similarly to DXA, using manual soft tissue segmentation, semi-automatic segmentation for the vertebrae, and automatic segmentation for the background. The method was assessed based on repetitive measurements of the standardized European Spine Phantom (ESP) using the standard scout scan tube current (30 mA) and other tube currents (10 to 200 mA), as well as using fat-equivalent extension rings simulating different patient habitus, and was compared to dual-energy X-ray absorptiometry (DXA). Moreover, the feasibility of the method was assessed in vivo in female patients. RESULTS: Derived from standard scout scans, aBMD values measured with the proposed method significantly correlated with DXA measurements (r = 0.9925, p < 0.001), and mean accuracy (DXA, 4.12%; scout, 1.60%) and precision (DXA, 2.64%; scout, 2.03%) were comparable between the two methods. Moreover, aBMD values assessed at different tube currents did not differ significantly (p ≥ 0.20 for all), suggesting that the presented method could be applied to scout scans with different settings. Finally, data derived from sample patients were concordant with BMD values from a reference age-matched population. CONCLUSIONS: Based on dual-layer spectral scout scans, aBMD measurements were fast and reliable and significantly correlated with the according DXA measurements in phantoms. Considering the number of CT acquisitions performed worldwide, this method could allow truly opportunistic osteoporosis screening. KEY POINTS: • 2D scout scans (localizer radiographs) from a dual-layer spectral CT scanner, which are mandatory parts of a CT examination, can be used to automatically determine areal bone mineral density (aBMD) at the spine. • The presented method allowed fast (< 25 s/patient), semi-automatic, and reliable DXA-equivalent aBMD measurements for state-of-the-art DXA phantoms at different tube settings and for various patient habitus, as well as for sample patients. • Considering the number of CT scout scan acquisitions performed worldwide on a daily basis, the presented technique could enable truly opportunistic osteoporosis screening with DXA-equivalent metrics, without involving higher radiation exposure since it only processes existing data that is acquired during each CT scan.

Direct quantitative material decomposition employing grating-based X-ray phase-contrast CT.

Dual-energy CT has opened up a new level of quantitative X-ray imaging for many diagnostic applications. The energy dependence of the X-ray attenuation is the key to quantitative material decomposition of the volume under investigation. This material decomposition allows the calculation of virtual native images in contrast enhanced angiography, virtual monoenergetic images for beam-hardening artifact reduction and quantitative material maps, among others. These visualizations have been proven beneficial for various diagnostic questions. Here, we demonstrate a new method of 'virtual dual-energy CT' employing grating-based phase-contrast for quantitative material decomposition. Analogue to the measurement at two different energies, the applied phase-contrast measurement approach yields dual information in form of a phase-shift and an attenuation image. Based on these two image channels, all known dual-energy applications can be demonstrated with our technique. While still in a preclinical state, the method features the important advantages of direct access to the electron density via the phase image, simultaneous availability of the conventional attenuation image at the full energy spectrum and therefore inherently registered image channels. The transfer of this signal extraction approach to phase-contrast data multiplies the diagnostic information gained within a single CT acquisition. The method is demonstrated with a phantom consisting of exemplary solid and fluid materials as well as a chicken heart with an iodine filled tube simulating a vessel. For this first demonstration all measurements have been conducted at a compact laser-undulator synchrotron X-ray source with a tunable X-ray energy and a narrow spectral bandwidth, to validate the quantitativeness of the processing approach.

Spectral Angiography Material Decomposition Using an Empirical Forward Model and a Dictionary-Based Regularization.

By resolving the energy of the incident X-ray photons, spectral X-ray imaging with photon counting detectors offers additional material-specific information compared to conventional X-ray imaging. This additional information can be used to improve clinical diagnosis for various applications. However, spectral imaging still faces several challenges. Amplified noise and a reduced signal-to-noise ratio on the decomposed basis material images remain a major problem, especially for low-dose applications. Furthermore, it is challenging to construct an accurate model of the spectral measurement acquisition process. In this paper, we present a novel algorithm for projection-based material decomposition. It uses an empirical polynomial model that is tuned by calibration measurements. We combine this method with a statistical model of the measured photon counts and a dictionary-based joint regularization approach. We focused on spectral coronary angiography as a potential clinical application of projection-based material decomposition with photon counting detectors. Numerical and real experiments show that spectral angiography with realistic dose levels and gadolinium contrast agent concentrations are feasible using the proposed decomposition algorithm and currently available photon-counting detector technology.

Dual-energy CT: a phantom comparison of different platforms for abdominal imaging.

OBJECTIVES: Evaluation of imaging performance across dual-energy CT (DECT) platforms, including dual-layer CT (DLCT), rapid-kVp-switching CT (KVSCT) and dual-source CT (DSCT). METHODS: A semi-anthropomorphic abdomen phantom was imaged on these DECT systems. Scans were repeated three times for CTDIvol levels of 10 mGy, 20 mGy, 30 mGy and different fat-simulating extension rings. Over the available range of virtual-monoenergetic images (VMI), noise as well as quantitative accuracy of hounsfield units (HU) and iodine concentrations were evaluated. RESULTS: For all VMI levels, HU values could be determined with high accuracy compared to theoretical values. For KVSCT and DSCT, a noise increase was observed towards lower VMI levels. A patient-size dependent increase in the uncertainty of quantitative iodine concentrations is observed for all platforms. For a medium patient size the iodine concentration root-mean-square deviation at 20 mGy is 0.17 mg/ml (DLCT), 0.30 mg/ml (KVSCT) and 0.77mg/ml (DSCT). CONCLUSION: Noticeable performance differences are observed between investigated DECT systems. Iodine concentrations and VMI HUs are accurately determined across all DECT systems. KVSCT and DLCT deliver slightly more accurate iodine concentration values than DSCT for investigated scenarios. In DLCT, low-noise and high-image contrast at low VMI levels may help to increase diagnostic information in abdominal CT. KEY POINTS: • Current dual-energy CT platforms provide accurate, reliable quantitative information. • Dual-energy CT cross-platform evaluation revealed noticeable performance differences between different systems. • Dual-layer CT offers constant noise levels over the complete energy range.

Assessment of quantification accuracy and image quality of a full-body dual-layer spectral CT system.

The performance of a recently introduced spectral computed tomography system based on a dual-layer detector has been investigated. A semi-anthropomorphic abdomen phantom for CT performance evaluation was imaged on the dual-layer spectral CT at different radiation exposure levels (CTDIvol of 10 mGy, 20 mGy and 30 mGy). The phantom was equipped with specific low-contrast and tissue-equivalent inserts including water-, adipose-, muscle-, liver-, bone-like materials and a variation in iodine concentrations. Additionally, the phantom size was varied using different extension rings to simulate different patient sizes. Contrast-to-noise (CNR) ratio over the range of available virtual mono-energetic images (VMI) and the quantitative accuracy of VMI Hounsfield Units (HU), effective-Z maps and iodine concentrations have been evaluated. Central and peripheral locations in the field-of-view have been examined. For all evaluated imaging tasks the results are within the calculated theoretical range of the tissue-equivalent inserts. Especially at low energies, the CNR in VMIs could be boosted by up to 330% with respect to conventional images using iDose/spectral reconstructions at level 0. The mean bias found in effective-Z maps and iodine concentrations averaged over all exposure levels and phantom sizes was 1.9% (eff. Z) and 3.4% (iodine). Only small variations were observed with increasing phantom size (+3%) while the bias was nearly independent of the exposure level (±0.2%). Therefore, dual-layer detector based CT offers high quantitative accuracy of spectral images over the complete field-of-view without any compromise in radiation dose or diagnostic image quality.

Joint Statistical Iterative Material Image Reconstruction for Spectral Computed Tomography Using a Semi-Empirical Forward Model.

By acquiring tomographic measurements with several distinct photon energy spectra, spectral computed tomography (spectral CT) is able to provide additional material-specific information compared with conventional CT. This information enables the generation of material selective images, which have found various applications in medical imaging. However, material decomposition typically leads to noise amplification and a degradation of the signal-to-noise ratio. This is still a fundamental problem of spectral CT, especially for low-dose medical applications. Inspired by the success for low-dose conventional CT, several statistical iterative reconstruction algorithms for spectral CT have been developed. These algorithms typically rely on detailed knowledge about the spectrum and the detector response. Obtaining this knowledge is often difficult in practice, especially if photon counting detectors are used to acquire the energy specific information. In this paper, a new algorithm for joint statistical iterative material image reconstruction is presented. It relies on a semi-empirical forward model which is tuned by calibration measurements. This strategy allows to model spatially varying properties of the imaging system without requiring detailed prior knowledge of the system parameters. We employ an efficient optimization algorithm based on separable surrogate functions to accelerate convergence and reduce the reconstruction time. Numerical as well as real experiments show that our new algorithm leads to reduced statistical bias and improved image quality compared with projection-based material decomposition followed by analytical or iterative image reconstruction.