Dark-field computed tomography reaches the human scale.

X-ray computed tomography (CT) is one of the most commonly used three-dimensional medical imaging modalities today. It has been refined over several decades, with the most recent innovations including dual-energy and spectral photon-counting technologies. Nevertheless, it has been discovered that wave-optical contrast mechanisms-beyond the presently used X-ray attenuation-offer the potential of complementary information, particularly on otherwise unresolved tissue microstructure. One such approach is dark-field imaging, which has recently been introduced and already demonstrated significantly improved radiological benefit in small-animal models, especially for lung diseases. Until now, however, dark-field CT could not yet be translated to the human scale and has been restricted to benchtop and small-animal systems, with scan durations of several minutes or more. This is mainly because the adaption and upscaling to the mechanical complexity, speed, and size of a human CT scanner so far remained an unsolved challenge. Here, we now report the successful integration of a Talbot-Lau interferometer into a clinical CT gantry and present dark-field CT results of a human-sized anthropomorphic body phantom, reconstructed from a single rotation scan performed in 1 s. Moreover, we present our key hardware and software solutions to the previously unsolved roadblocks, which so far have kept dark-field CT from being translated from the optical bench into a rapidly rotating CT gantry, with all its associated challenges like vibrations, continuous rotation, and large field of view. This development enables clinical dark-field CT studies with human patients in the near future.

Small-angle neutron scattering applied to low-dose neutron-irradiated Fe-Cr alloys and ferritic martensitic steel Eurofer97.

Ferritic/martensitic (F/M) Fe-Cr-based steels are candidates for applications in nuclear fission and fusion. Previous experimental results for neutron-irradiated binary Fe-Cr alloys and high-dose neutron-irradiated F/M steels contributed greatly to the understanding of the irradiation behaviour of these groups of materials. However, some details still need to be addressed. Such gaps are related to the effect of secondary alloying and impurity elements, such as Ni and Si, as well as the dose dependence at lower neutron doses [e.g. in the range 0.1-1 displacements per atom (dpa)]. This input is essential, for example, for multiscale modelling of irradiation effects or the evaluation of nuclear fission or fusion components at the first stages of operation. Using small-angle neutron scattering, three issues are addressed: (1) the effect of Cr undersaturation (5% Cr) and supersaturation (14% Cr) on the formation of irradiation-induced solute atom clusters/precipitates in low-dose neutron-irradiated Fe-Cr alloys in the presence of intentionally added levels of Ni, Si and P; (2) the effect of irradiation temperature (290°C versus 450°C); and (3) the effect of neutron dose in the range 0.06-0.6 dpa on the irradiation response of the reduced-activation F/M 9%Cr steel Eurofer97. The irradiation-enhanced formation of Cr-rich α'-phase particles was found to be the dominant effect for supersaturated Fe-14Cr-NiSiP at both irradiation temperatures. In contrast, α' formation is impossible in Fe-5Cr-NiSiP, for which the pronounced irradiation effects observed at 0.1 dpa are mainly attributed to added Ni, Si and P. Finally, Eurofer97 exhibits an exceptionally weak irradiation effect at low neutron doses, the reasons for which are also considered.

Low-dose cardio-respiratory phase-correlated cone-beam micro-CT of small animals.

PURPOSE: Micro-CT imaging of animal hearts typically requires a double gating procedure because scans during a breath-hold are not possible due to the long scan times and the high respiratory rates, Simultaneous respiratory and cardiac gating can either be done prospectively or retrospectively. True five-dimensional information can be either retrieved with retrospective gating or with prospective gating if several prospective gates are acquired. In any case, the amount of information available to reconstruct one volume for a given respiratory and cardiac phase is orders of magnitud lower than the total amount of information acquired. For example, the reconstruction of a volume from a 10% wide respiratory and a 20% wide cardiac window uses only 2% of the data acquired. Achieving a similar image quality as a nongated scan would therefore require to increase the amount of data and thereby the dose to the animal by up to a factor of 50. METHODS: To achieve the goal of low-dose phase-correlated (LDPC) imaging, the authors propose to use a highly efficient combination of slightly modified existing algorithms. In particular, the authors developed a variant of the McKinnon-Bates image reconstruction algorithm and combined it with bilateral filtering in up to five dimensions to significantly reduce image noise without impairing spatial or temporal resolution. RESULTS: The preliminary results indicate that the proposed LDPC reconstruction method typically reduces image noise by a factor of up to 6 (e.g., from 170 to 30 HU), while the dose values lie in a range from 60 to 500 mGy. Compared to other publications that apply 250-1800 mGy for the same task [C. T. Badea et al., "4D micro-CT of the mouse heart," Mol. Imaging 4(2), 110-116 (2005); M. Drangova et al., "Fast retrospectively gated quantitative four-dimensional (4D) cardiac micro computed tomography imaging of free-breathing mice," Invest. Radiol. 42(2), 85-94 (2007); S. H. Bartling et al., "Retrospective motion gating in small animal CT of mice and rats," Invest. Radiol. 42(10), 704-714 (2007)], the authors' LDPC approach therefore achieves a more than tenfold dose usage improvement. CONCLUSIONS: The LDPC reconstruction method improves phase-correlated imaging from highly undersampled data. Artifacts caused by sparse angular sampling are removed and the image noise is decreased, while spatial and temporal resolution are preserved. Thus, the administered dose per animal can be decreased allowing for long-term studies with reduced metabolic inference.

An investigation of 4D cone-beam CT algorithms for slowly rotating scanners.

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.

Autoadaptive phase-correlated (AAPC) reconstruction for 4D CBCT.

PURPOSE: Kilovoltage cone-beam computed tomography (CBCT) is widely used in image-guided radiation therapy for exact patient positioning prior to the treatment. However, producing time series of volumetric images (4D CBCT) of moving anatomical structures remains challenging. The presented work introduces a novel method, combining high temporal resolution inside anatomical regions with strong motion and image quality improvement in regions with little motion. METHODS: In the proposed method, the projections are divided into regions that are subject to motion and regions at rest. The latter ones will be shared among phase bins, leading thus to an overall reduction in artifacts and noise. An algorithm based on the concept of optical flow was developed to analyze motion-induced changes between projections. The technique was optimized to distinguish patient motion and motion deriving from gantry rotation. The effectiveness of the method is shown in numerical simulations and patient data. RESULTS: The images reconstructed from the presented method yield an almost the same temporal resolution in the moving volume segments as a conventional phase-correlated reconstruction, while reducing the noise in the motionless regions down to the level of a standard reconstruction without phase correlation. The proposed simple motion segmentation scheme is yet limited to rotation speeds of less than 3 degrees/s. CONCLUSIONS: The method reduces the noise in the reconstruction and increases the image quality. More data are introduced for each phase-correlated reconstruction, and therefore the applied dose is used more efficiently.

Combination of voxel-based and projection-based methods in terms of convergence for CT reconstruction.

PURPOSE: Recent applications of iterative image reconstruction algorithms to multislice helical CT have shown that iterative reconstruction can significantly improve image quality and reduce artifacts. In this paper, the authors introduce a combination of two different algorithms with different convergence properties: ordered subsets separable paraboloidal surrogates (OS-SPS) and iterative coordinate descent (ICD). The first one updates image voxels simultaneously, slightly changing attenuation values iteration by iteration. The second algorithm updates image voxel by voxel, each time performing full forward and backward projections of the voxel. It has been shown that ICD converges better at high-frequency areas and requires more iterations to reconstruct low-frequency components of the image. In contrast to ICD, SPS requires multiple iterations to reconstruct high-frequency areas. In this paper, the authors introduce an algorithm which leverages the benefits of both ICD and SPS. METHODS: The idea is to update the entire image with SPS, determine high-frequency components, and focus ICD computations on it using nonhomogeneous ICD update. RESULTS: The authors have successfully implemented OS-SPS, ICD, their hybrid approach, and few variations of ICD based on spatially nonuniform updates. CONCLUSIONS: The authors have examined the convergence of different algorithms and found that proposed algorithm converges better than OS-SPS, ICD, as well as various improved variants of ICD.

Vibrational contribution to the thermodynamics of nanosized precipitates: vacancy-copper clusters in bcc-Fe.

The effects of lattice vibration on the thermodynamics of nanosized coherent clusters in bcc-Fe consisting of vacancies and/or copper are investigated within the harmonic approximation. A combination of on-lattice simulated annealing based on Metropolis Monte Carlo simulations and off-lattice relaxation by molecular dynamics is applied to obtain the most stable cluster configurations at T = 0 K. The most recent interatomic potential built within the framework of the embedded-atom method for the Fe-Cu system is used. The total free energy of pure bcc-Fe and fcc-Cu as well as the total formation free energy and the total binding free energy of the vacancy-copper clusters are determined for finite temperatures. Our results are compared with the available data from previous investigations performed using many-body interatomic potentials and first-principles methods. For further applications in rate theory and object kinetic Monte Carlo simulations, the vibrational effects evaluated in the present study are included in the previously developed analytical fitting formulae.

Improved total variation-based CT image reconstruction applied to clinical data.

In computed tomography there are different situations where reconstruction has to be performed with limited raw data. In the past few years it has been shown that algorithms which are based on compressed sensing theory are able to handle incomplete datasets quite well. As a cost function these algorithms use the ℓ(1)-norm of the image after it has been transformed by a sparsifying transformation. This yields to an inequality-constrained convex optimization problem. Due to the large size of the optimization problem some heuristic optimization algorithms have been proposed in the past few years. The most popular way is optimizing the raw data and sparsity cost functions separately in an alternating manner. In this paper we will follow this strategy and present a new method to adapt these optimization steps. Compared to existing methods which perform similarly, the proposed method needs no a priori knowledge about the raw data consistency. It is ensured that the algorithm converges to the lowest possible value of the raw data cost function, while holding the sparsity constraint at a low value. This is achieved by transferring the step-size determination of both optimization procedures into the raw data domain, where they are adapted to each other. To evaluate the algorithm, we process measured clinical datasets. To cover a wide field of possible applications, we focus on the problems of angular undersampling, data lost due to metal implants, limited view angle tomography and interior tomography. In all cases the presented method reaches convergence within less than 25 iteration steps, while using a constant set of algorithm control parameters. The image artifacts caused by incomplete raw data are mostly removed without introducing new effects like staircasing. All scenarios are compared to an existing implementation of the ASD-POCS algorithm, which realizes the step-size adaption in a different way. Additional prior information as proposed by the PICCS algorithm can be incorporated easily into the optimization process.

Water calibration for CT scanners with tube voltage modulation.

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.