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Talbot-Lau x-ray imaging (TLXI) is an innovative and promising imaging technique providing information about the x-ray attenuation, scattering, and refraction features of objects. However, the method is susceptible to vibrations and system component imprecisions, which are inevitable in clinical and industrial practice. Those influences provoke grating displacements and hence errors in the acquired raw data, which cause moiré artifacts in the reconstructed images. We developed an enhanced reconstruction algorithm capable of compensating these errors by adjusting the grating positions and thus suppressing the occurrence of moiré artifacts. The algorithm has been developed with regard to a future application in medical practice. The capability of the algorithm is demonstrated on a medical data set of a human hand (post-mortem) acquired under clinical conditions using a pre-clinical TXLI prototype. It is shown that the algorithm reliably suppresses moiré artifacts, preserves image contrast, does not blur anatomical structures or prevent quantitative imaging, and is executable on low-dose data sets. In addition, the algorithm runs autonomously without the need of interaction or rework of the final results. In conclusion, the proposed reconstruction algorithm facilitates the use of TLXI in clinical practice and allows the exploitation of the method's full diagnostic potential in future medical applications.

Purpose: Scattered radiation is one of the major problems facing image quality in flat detector cone-beam computed tomography (CBCT). Previously, a new scatter estimation and correction method using primary beam modulation has been proposed. The original image processing technique used a frequency-domain-based analysis, which proved to be sensitive to the accuracy of the modulator pattern both spatially and in amplitude as well as to the frequency of the modulation pattern. In addition, it cannot account for penumbra effects that occur, for example, due to the finite focal spot size and the scatter estimate can be degraded by high-frequency components of the primary image., Methods: In this paper, the authors present a new way to estimate the scatter using primary modulation. It is less sensitive to modulator nonidealities and most importantly can handle arbitrary modulator shapes and changes in modulator attenuation. The main idea is that the scatter estimation can be expressed as an optimization problem, which yields a separation of the scatter and the primary image. The method is evaluated using simulated and experimental CBCT data. The scattering properties of the modulator itself are analyzed using a Monte Carlo simulation., Results: All reconstructions show strong improvements of image quality. To quantify the results, all images are compared to reference images (ideal simulations and collimated scans)., Conclusions: The proposed modulator-based scatter reduction algorithm may open the field of flat detector-based imaging to become a quantitative modality. This may have significant impact on C-arm imaging and on image-guided radiation therapy.

Purpose: To evaluate several algorithms for 4D cone-beam computed tomography (4D CBCT) with slow rotating devices. 4D CBCT is used to perform phase-correlated (PC) reconstructions of moving objects, such as breathing patients, for example. Such motion phase-dependent reconstructions are especially useful for updating treatment plans in radiation therapy. The treatment plan can be registered more precisely to the motion of the tumor and, in consequence, the irradiation margins for the treatment, the so-called planning target volume, can be reduced significantly, Methods: In the study, several algorithms were evaluated for kilovoltage cone-beam CT units attached to linear particle accelerators. The reconstruction algorithms were the conventional PC reconstruction, the McKinnon-Bates (MKB) algorithm, the prior image constrained compressed sensing (PICCS) approach, a total variation minimization (ASD-POCS) algorithm, and the auto-adaptive phase correlation (AAPC) algorithm. For each algorithm, the same motion-affected raw data were used, i.e., one simulated and one measured data set. The reconstruction results from the authors' implementation of these algorithms were evaluated regarding their noise and artifact levels, their residual motion blur, and their computational complexity and convergence., Results: In general, it turned out that the residual motion blur was lowest in those algorithms which exclusively use data from a single motion phase. Algorithms using the image from the full data set as initialization or as a reference for the reconstruction were not capable of fully removing the motion blurring. The iterative algorithms, especially approaches based on total variation minimization, showed lower noise and artifact levels but were computationally complex. The conventional methods based on a single filtered backprojection were computationally inexpensive but suffered from higher noise and streak artifacts which limit the usability. In contrast, these methods showed to be less demanding and more predictable in their outcome than the total variation minimization based approaches., Conclusions: The reconstruction algorithms including at least one iterative step can reduce the 4 CBCT specific artifacts. Nevertheless, the algorithms that use the full data set, at least for initialization, such as MKB and PICCS in the authors' implementation, are only a trade-off and may not fully achieve the optimal temporal resolution. A predictable image quality as seen in conventional reconstruction methods, i.e., without total variation minimization, is a desirable property for reconstruction algorithms. Fast, iterative approaches such as the MKB can therefore be seen as a suitable tradeoff.

X-ray CT measures the attenuation of polychromatic x-rays through an object of interest. The CT data acquired are the negative logarithm of the relative x-ray intensity after absorption. These data must undergo water precorrection to linearize the measured data and convert them into line integrals through the patient that can be reconstructed to yield the final CT image. The function to linearize the measured projection data depends on the tube voltage U. In most circumstances, CT scans are carried out with a constant tube voltage. For those cases there are dozens of different techniques to carry out water precorrection. In our case the tube voltage is rather modulated as a function of the object. We propose an empirical cupping correction (ECCU) algorithm to correct for CT cupping artifacts that are induced by nonlinearities in the projection data. The method is rawdata based, empirical and requires neither knowledge of the x-ray spectrum nor of the attenuation coefficients. It aims at linearizing the attenuation data using a precorrection function of polynomial form in the polychromatic attenuation data q and in the tube voltage U. The coefficients of the polynomial are determined once using a calibration scan of a homogeneous phantom. The coefficients are computed in the image domain by fitting a series of basis images to a template image. The template image is obtained directly from the uncorrected phantom image and no assumptions on the phantom size or of its positioning are made. Rawdata are precorrected by passing them through the once-determined polynomial. Numerical examples are shown to demonstrate the quality of the precorrection. ECCU is achieved to remove the cupping artifacts and to obtain well-calibrated CT values. A combination of ECCU with analytical techniques yielding a hybrid cupping correction method is possible and allows for channel-dependent correction functions.