In clinical X-ray imaging, the quantitative information in a CT scan has recently been extended by the possibility of using dual-energy information. Dual-energy CT has found its way into clinical imaging during the last few years and has been proven to add additional diagnostic information in different pathologies. It is based on a dual measurement at different photon energies, such that the energy dependence of the linear attenuation coefficient can be used for improved material discrimination. Here, we demonstrate how the dual information accessed with grating-based phase-contrast CT can be used to provide the same quantitative information. Different from dual energy, the phase-contrast measurement directly yields the electron-density and the total attenuation coefficient in a single measurement. With algebraic basis transformation this can be used for quantitative material decomposition, allowing the visualization of quantitative material maps. Further, a simple interaction parametrization has been used for the generation of effective atomic number maps and virtual monochromatic images. The approach has been demonstrated with an experimental angiography simulation with a chicken heart. The results have been compared with iodine staining, which is a current approach for ex-vivo soft-tissue contrast enhancement. The measurements have been performed at a compact laser-undulator synchrotron X-ray source with a tunable quasi-monochromatic X-ray energy. The simultaneous image acquisition guarantees an inherent registration of the two original data-sets. In total, the method provides a range of novel quantitative image representations which can be helpful for specific material discrimination tasks in medical imaging in the future.
Recently, we have investigated a new algorithm for combining grating-based differential phase contrast radiography and spectral radiography. The algorithm extracts two basis material images and a dark-field image by simultaneously using the spectral and the phase contrast information. Numerical simulations have shown that the combination of these two imaging methods benefits from the strengths of the individual methods while the weaknesses are mitigated. Quantitatively accurate basis material images are obtained and the additional phase shift information leads to highly reduced basis material image noise levels compared to conventional spectral material decomposition. In this work, we extend our approach to spectral phase contrast CT by developing a one-step statistical iterative reconstruction algorithm. In numerical simulations, we demonstrate the potential for further dose reductions as well as the possibility of eliminating the time-consuming phase stepping procedure which is incompatible with a continuously rotating gantry.
While conventional x-ray tube sources reliably provide high-power x-ray beams for everyday clinical practice, the broad spectra that are inherent to these sources compromise the diagnostic image quality. For a monochromatic x-ray source on the other hand, the x-ray energy can be adjusted to optimal conditions with respect to contrast and dose. However, large-scale synchrotron sources impose high spatial and financial demands, making them unsuitable for clinical practice. During the last decades, research has brought up compact synchrotron sources based on inverse Compton scattering, which deliver a highly brilliant, quasi-monochromatic, tunable x-ray beam, yet fitting into a standard laboratory. One application that could benefit from the invention of these sources in clinical practice is coronary angiography. Being an important and frequently applied diagnostic tool, a high number of complications in angiography, such as renal failure, allergic reaction, or hyperthyroidism, are caused by the large amount of iodine-based contrast agent that is required for achieving sufficient image contrast. Here we demonstrate monochromatic angiography of a porcine heart acquired at the MuCLS, the first compact synchrotron source. By means of a simulation, the CNR in a coronary angiography image achieved with the quasi-mono-energetic MuCLS spectrum is analyzed and compared to a conventional x-ray-tube spectrum. The results imply that the improved CNR achieved with a quasi-monochromatic spectrum can allow for a significant reduction of iodine contrast material.
In spectral computed tomography (spectral CT), the additional information about the energy dependence of
attenuation coefficients can be exploited to generate material selective images. These images have found applications
in various areas such as artifact reduction, quantitative imaging or clinical diagnosis. However, significant
noise amplification on material decomposed images remains a fundamental problem of spectral CT. Most spectral
CT algorithms separate the process of material decomposition and image reconstruction. Separating these
steps is suboptimal because the full statistical information contained in the spectral tomographic measurements
cannot be exploited. Statistical iterative reconstruction (SIR) techniques provide an alternative, mathematically
elegant approach to obtaining material selective images with improved tradeoffs between noise and resolution.
Furthermore, image reconstruction and material decomposition can be performed jointly. This is accomplished
by a forward model which directly connects the (expected) spectral projection measurements and the material
selective images. To obtain this forward model, detailed knowledge of the different photon energy spectra and
the detector response was assumed in previous work. However, accurately determining the spectrum is often
difficult in practice. In this work, a new algorithm for statistical iterative material decomposition is presented.
It uses a semi-empirical forward model which relies on simple calibration measurements. Furthermore, an efficient optimization algorithm based on separable surrogate functions is employed. This partially negates one
of the major shortcomings of SIR, namely high computational cost and long reconstruction times. Numerical
simulations and real experiments show strongly improved image quality and reduced statistical bias compared
to projection-based material decomposition.
We present a semi-empirical forward-model for spectral photon-counting CT which is fully compatible with state-of-the-art maximum-likelihood estimators (MLE) for basis material line integrals. The model relies on a minimum calibration effort to make the method applicable in routine clinical set-ups with the need for periodic re-calibration. In this work we present an experimental verifcation of our proposed method. The proposed method uses an adapted Beer-Lambert model, describing the energy dependent attenuation of a polychromatic x-ray spectrum using additional exponential terms. In an experimental dual-energy photon-counting CT setup based on a CdTe detector, the model demonstrates an accurate prediction of the registered counts for an attenuated polychromatic spectrum. Thereby deviations between model and measurement data lie within the Poisson statistical limit of the performed acquisitions, providing an effectively unbiased forward-model. The experimental data also shows that the model is capable of handling possible spectral distortions introduced by the photon-counting detector and CdTe sensor. The simplicity and high accuracy of the proposed model provides a viable forward-model for MLE-based spectral decomposition methods without the need of costly and time-consuming characterization of the system response.
Compared to conventional computed tomography (CT), dual energy CT allows for improved material decomposition by conducting measurements at two distinct energy spectra. Since radiation exposure is a major concern in clinical CT, there is a need for tools to reduce the noise level in images while preserving diagnostic information. One way to achieve this goal is the application of image-based denoising algorithms after an analytical reconstruction has been performed. We have developed a modified dictionary denoising algorithm for dual energy CT aimed at exploiting the high spatial correlation between between images obtained from different energy spectra. Both the low-and high energy image are partitioned into small patches which are subsequently normalized. Combined patches with improved signal-to-noise ratio are formed by a weighted addition of corresponding normalized patches from both images. Assuming that corresponding low-and high energy image patches are related by a linear transformation, the signal in both patches is added coherently while noise is neglected. Conventional dictionary denoising is then performed on the combined patches. Compared to conventional dictionary denoising and bilateral filtering, our algorithm achieved superior performance in terms of qualitative and quantitative image quality measures. We demonstrate, in simulation studies, that this approach can produce 2d-histograms of the high- and low-energy reconstruction which are characterized by significantly improved material features and separation. Moreover, in comparison to other approaches that attempt denoising without simultaneously using both energy signals, superior similarity to the ground truth can be found with our proposed algorithm.