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Accurate and fast simulation of X-ray projection data from mesh models has many applications in academia and industry, ranging from 3D X-ray computed tomography (XCT) reconstruction algorithms to radiograph-based object inspection and quality control. While software tools for the simulation of X-ray projection data from mesh models are available, they lack either performance, public availability, flexibility to implement non-standard scanning geometries, or easy integration with existing 3D XCT software. In this paper, we propose CAD-ASTRA, a highly versatile toolbox for fast simulation of X-ray projection data from mesh models. While fully functional as standalone software, it is also compatible with the ASTRA toolbox, an open-source toolbox for flexible tomographic reconstruction. CAD-ASTRA provides three specialized GPU projectors based on state-of-the-art algorithms for 3D rendering, implemented using the NVIDIA CUDA Toolkit and the OptiX engine. First, it enables X-ray phase contrast simulations by modeling refraction through ray tracing. Second, it allows the back-propagation of projective errors to mesh vertices, enabling immediate application in mesh reconstruction, deep learning, and other optimization routines. Finally, CAD-ASTRA allows simulation of polychromatic X-ray projections from heterogeneous objects with a source of finite focal spot size. Use cases on a CAD-based inspection task, a phase contrast experiment, a combined mesh-volumetric data projection, and a mesh reconstruction demonstrate the wide applicability of CAD-ASTRA.

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Edge illumination (EI) is an X-ray imaging technique that, in addition to conventional absorption contrast, provides refraction and scatter contrast. It relies on an absorption mask in front of the sample that splits the X-ray beam into beamlets, which hits a second absorption mask positioned in front of the detector. The sample mask is then shifted in multiple steps with respect to the detector mask, thereby measuring an illumination curve per detector element. The width, position, and area of this curve estimated with and without the sample in the beam is then compared, which ultimately provides absorption, refraction, and scatter contrast for each detector pixel. From the obtained contrast sinograms, three contrast tomograms can be computed. In summary, conventional EI relies on a two-stage process comprised of a computational and time intensive contrast retrieval process, followed by tomographic reconstruction. In this work, a novel joint reconstruction method is proposed, which utilizes a combined forward model to reconstruct the three contrasts simultaneously, without the need for an intermediate contrast retrieval step. Compared to the state-of-the-art, this approach reduces reconstruction times, as the retrieval step is skipped and allows a much more flexible acquisition scheme, as there is no need to sample a full illumination curve at each projection angle. The proposed method is shown to improve reconstruction quality on subsampled datasets, enabling the reconstruction of three contrasts from single-shot datasets.

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X-ray dark field signals, measurable in many x-ray phase contrast imaging (XPCI) setups, stem from unresolvable microstructures in the scanned sample. This makes them ideally suited for the detection of certain pathologies, which correlate with changes in the microstructure of a sample. Simulations of x-ray dark field signals can aid in the design and optimization of XPCI setups, and the development of new reconstruction techniques. Current simulation tools, however, require explicit modelling of the sample microstructures according to their size and spatial distribution. This process is cumbersome, does not translate well between different samples, and considerably slows down simulations. In this work, a condensed history approach to modelling x-ray dark field effects is presented, under the assumption of an isotropic distribution of microstructures, and applied to edge illumination phase contrast simulations. It substantially simplifies the sample model, can be easily ported between samples, and is two orders of magnitude faster than conventional dark field simulations, while showing equivalent results.Clinical relevance- Dark field signal provides information on the microstructure distribution within the investigated sample, which can be applied in areas such as histology and lung x-ray imaging. Efficient simulation tools for this dark field signal aid in optimizing scanning setups, acquisition schemes and reconstruction techniques.

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Today, new single photon emission computed tomography (SPECT) reconstruction techniques rely on accurate Monte Carlo (MC) simulations to optimize reconstructed images. However, existing MC scintillation camera models which usually include an accurate description of the collimator and crystal, lack correct implementation of the gamma camera's back compartments. In the case of dual isotope simultaneous acquisition (DISA), where backscattered photons from the highest energy isotope are detected in the imaging energy window of the second isotope, this approximation may induce simulation errors. Here, we investigate the influence of backscatter compartment modelling on the simulation accuracy of high-energy isotopes. Three models of a scintillation camera were simulated: a simple model (SM), composed only of a collimator and a NaI(Tl) crystal; an intermediate model (IM), adding a simplified description of the backscatter compartments to the previous model and a complete model (CM), accurately simulating the materials and geometries of the camera. The camera models were evaluated with point sources ((67)Ga, (99m)Tc, (111)In, (123)I, (131)I and (18)F) in air without a collimator, in air with a collimator and in water with a collimator. In the latter case, sensitivities and point-spread functions (PSFs) simulated in the photopeak window with the IM and CM are close to the measured values (error below 10.5%). In the backscatter energy window, however, the IM and CM overestimate the FWHM of the detected PSF by 52% and 23%, respectively, while the SM underestimates it by 34%. The backscatter peak fluence is also overestimated by 20% and 10% with the IM and CM, respectively, whereas it is underestimated by 60% with the SM. The results show that an accurate description of the backscatter compartments is required for SPECT simulations of high-energy isotopes (above 300 keV) when the backscatter energy window is of interest.

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Monte Carlo simulations are widely used to study the behavior and detection of gamma photons in medical imaging devices. Such simulations are computationally expensive. This is why geometrical importance sampling, a variance reduction technique, was recently incorporated into the GEANT4 Monte Carlo code. In order to use this technique for single photon emission computed tomography (SPECT) imaging, it needed to be made compatible with pulse height tallies. These tallies correspond to the number of detected pulses in distinct energy bins, covering an energy spectrum relevant to SPECT. Since each pulse is the combination of different detector hits, the tally bin is not known until the end of an event. In an analog simulation (without variance reduction) this poses no problems as each detected hit can be stored and the pulse can be calculated at the end of each event. Geometrical importance sampling combined with Russian Roulette however introduces branches into the particle history, which results in a much more complicated pulse calculation. This work describes how pulse height tallies are adjusted to geometrical importance sampling and Russian Roulette within GATE, a medical imaging and simulation application based on GEANT4. The validation of this technique is done through SPECT simulations comparing the analog result with the new method.