Artifacts can seriously degrade the quality of computed tomographic (CT) images, sometimes to the point of making them diagnostically unusable. To optimize image quality, it is necessary to understand why artifacts occur and how they can be prevented or suppressed. CT arti-facts originate from a range of sources. Physics-based artifacts result from the physical processes involved in the acquisition of CT data. Patient based artifacts are caused by such factors as patient movement or the presence of metallic materials in or on the patient. Scanner-based artifacts result from imperfections in scanner function. Helical and multisection technique artifacts are produced by the image reconstruction process. Design features incorporated into modern CT scanners minimize some types of artifacts, and some can be partially corrected by the scanner software. However, in many instances, careful patient positioning and optimum selection of scanning parameters are the most important factors in avoiding CT artifacts.
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Grating-based X-ray dark-field imaging is a novel technique for obtaining image contrast for object structures at size scales below setup resolution. Such an approach appears particularly beneficial for medical imaging and nondestructive testing. It has already been shown that the dark-field signal depends on the direction of observation. However, up to now, algorithms for fully recovering the orientation dependence in a tomographic volume are still unexplored. In this publication, we propose a reconstruction method for grating-based X-ray dark-field tomogra-phy, which models the orientation-dependent signal as an additional observable from a standard tomographic scan. In detail , we extend the tomographic volume to a tensorial set of voxel data, containing the local orientation and contributions to dark-field scattering. In our experiments, we present the first results of several test specimens exhibiting a heterogeneous composition in microstructure, which demonstrates the diagnostic potential of the method. X-ray phase contrast | grating interferometer | microstructure orientation | anisotropic scattering X-ray dark-field images, which are obtained using a Talbot−Lau grating interferometer, reveal differences in the real part of the refractive index of a material at micrometer scale, commonly subsumed as ultra-small-angle scattering (1-3). This can be observed in specimens with a high level of porosity or granularity. As such, dark-field imaging yields the potential for novel diagnostic methods in medical imaging as well as approaches to nondestructive testing. A high sensitivity especially to structures composed of weakly absorbing materials has been shown. Ultra-small-angle scattering can be either isotropic or anisotropic, generated by structures of the order of magnitude of the grating period of the interferometer. For the exploitation of isotropic scattering, several groups reported experiments in dark-field radiography and computed tomogra-phy in a wide spread of promising applications such as detection of micrometer-sized calcifications in breast tumor lesions or the investigation of lung and joints (3-5). Until now, however, the exploitation of anisotropic scattering has remained largely un-explored. Anisotropic scattering is produced by ordered structures , such as layers, or fibers with radii of few micrometers. Recently, Bayer et al. (6) presented the observation of periodic dark-field projections caused by the orientations in microstructures and illustrated the potential of exploiting full information of specimens. Hence, special attention has to be paid to the orientation of these structures relative to the grating bars, making the exploitation of anisotropic scattering information challenging. Prior work reported partial retrieval of structured information regarding isotropic and anisotropic properties in materials (6-9). Revol et al. (7) separated isotropic and anisotropic components of individual orientations by known orientati
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目的:最近,进行了几次尝试将深度学习转移到医学图像重建。越来越多的出版物遵循将CT重建作为已知算子嵌入到神经网络中的概念。然而,所提出的大多数方法缺乏完全集成到深度学习环境中的有效CT重构框架。因此,许多方法被迫使用变通方法来解决数学上可解决的问题。方法:PYRO-NN是一个通用的框架,将已知的操作员引入普遍的深度学习框架Tensorflow。目前的状态包括最先进的并行,扇形和锥形投影仪以及通过CUDA加速作为Tensorflowlayers加速的反投影仪。最重要的是,该框架提供了一个高级Python API,可以利用来自真实CT系统的数据进行FBP和迭代重建实验。结果:该框架为集成CT重建算法的设计到终端神经网络管道提供了所有必要的算法和工具。高级Python API允许简单地使用Tensorflow中提出的层。为了证明这些层的功能,框架结合了三个基线实验,显示了锥形束短扫描FDD重建,CT重建滤波器学习设置和TVregularized迭代重建。所有算法和工具都参考科学出版物,并与现有的非深度学习重构框架进行比较。该框架以开源软件\ url {https://github.com/csyben/PYRO-NN}的形式提供。结论:PYRO-NN具有普遍的深度学习框架Tensorflow,允许在医学图像重建环境中建立端到端的可训练神经网络。 Webelieve认为该框架将是迈向可重复研究的一步
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在计算机断层扫描(CT)中,金属植入物增加了测量数据与分析CT重建算法所做的线性衰减假设之间的不一致性。不一致性在重建图像中产生暗和亮带和条纹,统称为金属伪影。这些伪影使放射科医师难以做出正确的诊断决策。我们描述了一种用于图像引导脊柱手术的数据驱动金属伪影减少(MAR)算法,该算法应用于可以获得患者的先前CT扫描的情景。我们用两个临床数据集测试了所提出的方法,这两个数据集都是在神经外科手术中获得的。使用所提出的方法,我们不仅能够去除由植入的螺钉引起的暗和亮条纹,而且我们还恢复了由这些伪影隐藏的解剖结构。这导致外科医生改善能力以确认植入的椎弓根放置的正确性。
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Diagnostic and operational tasks in dental radiology often require three-dimensional information that is difficult or impossible to see in a projection image. A CT-scan provides the dentist with comprehensive three-dimensional data. However, often CT-scan is impractical and, instead, only a few projection radiographs with sparsely distributed projection directions are available. Statistical (Bayesian) inversion is well-suited approach for reconstruction from such incomplete data. In statistical inversion, a priori information is used to compensate for the incomplete information of the data. The inverse problem is recast in the form of statistical inference from the posterior probability distribution that is based on statistical models of the projection data and the a priori information of the tissue. In this paper, a statistical model for three-dimensional imaging of dentomaxillofacial structures is proposed. Optimization and MCMC algorithms are implemented for the computation of posterior statistics. Results are given with in vitro projection data that were taken with a commercial intraoral x-ray sensor. Examples include limited-angle tomography and full-angle tomography with sparse projection data. Reconstructions with traditional tomographic reconstruction methods are given as reference for the assessment of the estimates that are based on the statistical model.
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Dark-field imaging is a scattering-based X-ray imaging method that can be performed with laboratory X-ray tubes. The possibility to obtain information about unresolvable structures has already seen a lot of interest for both medical and material science applications. Unlike conventional X-ray attenuation, orientation dependent changes of the dark-field signal can be used to reveal microscopic structural orientation. To date, reconstruction of the three-dimensional dark-field signal requires dedicated, highly complex algorithms and specialized acquisition hardware. This severely hinders the possible application of orientation-dependent dark-field tomography. In this paper, we show that it is possible to perform this kind of dark-field tomography with common Talbot-Lau interferometer setups by reducing the reconstruction to several smaller independent problems. This allows for the reconstruction to be performed with commercially available software and our findings will therefore help pave the way for a straightforward implementation of orientation-dependent dark-field tomography. Grating-based X-ray phase-contrast imaging (GBI) is an interferometry technique developed a decade ago. In addition to the conventional attenuation image, two additional contrast modalities are acquired simultaneously 1-4 : the differential phase and dark-field contrast. While the widely used X-ray attenuation imaging purely relies on the reduction of the intensity of X-rays when they pass through an object, the differential phase-contrast image is based on the refraction of X-rays. Phase contrast imaging can be several orders of magnitude more sensitive to changes within an object than attenuation-based imaging 1. The additional dark-field contrast is interpreted as scattering of X-rays by structures of sizes below the spatial resolution of the imaging system 3, 5-10. Because of its ability to combine scattering information with spatial resolution in a single image, dark-field imaging is particularly useful for the investigation of microscopic changes inside large objects. Just like conventional X-ray imaging, GBI is not limited to radiography only, but volumetric information an be obtained using computed tomography (CT) for both the phase-contrast 4, 11-16 and the dark-field signals 17-22. A conventional grating-interferometer used for GBI consists of parallel grating lines oriented in a certain direction. The grating-interferometer is therefore sensitive only to phase gradients and scattering information perpendicular to the grating lines. The anisotropic sensitivity of a grating-interferometer can be used to characterize the orientations of microscopic scattering structures both in 2-D 23-25 , and 3-D 20, 26-29. For the three-dimensional case, a complete reconstruction of the anisotropic scattering distribution has only been achieved by the use of complex reconstruction techniques so far 26, 27, 29. As a three-dimensional scattering distribution is reconstructed in eac
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Energy resolved detectors are gaining traction as a tool to achieve better material contrast. K-edge imaging and tomography is an example of a method with high potential that has evolved on the capabilities of photon counting energy dispersive detectors. Border security is also beginning to see instruments taking advantage of energy resolved detectors. The progress of the field is halted by the limitations of the detectors. The limitations include nonlinear response for both x-ray intensity and x-ray spectrum. In this work we investigate how the physical interactions in the energy dispersive detectors affect the quality of the reconstruction and how corrections restore the quality. We have modeled detector responses for the primary detrimental effects occurring in the detector; escape peaks, charge sharing/loss and pileup. The effect of the change in the measured spectra is evaluated based on the artefacts occurring in the reconstructed images. We also evaluate the effect of a correction algorithm for reducing these artefacts on experimental data acquired with a setup using Multix ME-100 V-2 line detector modules. The artefacts were seen to introduce 20% deviation in the reconstructed attenuation coefficient for the uncorrected detector. We performed tomography experiments on samples with various materials interesting for security applications and found the structural similarity index to increase > 5% below 60 keV. Our work shows that effective corrections schemes are necessary for the accurate material classification in security application promised by the advent of high flux detectors for spectral tomography.
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Short glass and carbon fiber reinforced polymer composites are used in many industrial fields such as in automotive and consumers industry. Their physical and mechanical properties are often superior to those of unfilled polymer components. One aspect being of utmost importance for these properties is the fiber orientation distribution. Here, we present X-ray vector radiography as a method to characterize fiber orientation in short fiber reinforced polymer components. The method is based on X-ray grating interferometry and takes advantage of X-ray scattering caused by the sample's microstructure. Therefore, micro-structural properties can be probed nondestructively without the need for high spatial resolution. Compared to standard X-ray imaging techniques, currently applied for fiber orientation studies, the presented method does not restrict the size of the sample under investigation and allows for much shorter measurement times. In contrast to existing methods, X-ray vector radiography allows the characterization of carbon fiber reinforced polymers despite the weak attenuation contrast between the fibers and the polymer matrix. As this method is also extendable into three dimensions it is a very attractive tool for complex component geometries and carries potential to be applied to materials other than short fiber reinforced polymers.
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In practical applications of tomographic imaging, there are often challenges for image reconstruction due to under-sampling and insufficient data. In computed tomography (CT), for example, image reconstruction from few views would enable rapid scanning with a reduced x-ray dose delivered to the patient. Limited-angle problems are also of practical significance in CT. In this work, we develop and investigate an iterative image reconstruction algorithm based on the minimization of the image total variation (TV) that applies to divergent-beam CT. Numerical demonstrations of our TV algorithm are performed with various insufficient data problems in fan-beam CT. The TV algorithm can be generalized to cone-beam CT as well as other tomographic imaging modalities.
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多光谱计算机断层扫描是一种新兴的技术,用于物体材料的破坏性识别和物理性质的研究。这种技术的应用可以在各种科学和工业环境中找到,例如在机场进行行李扫描。由于采集噪声,层析成像重建因素和扫描设置应用约束,材料区别及其识别具有挑战性,即使对于光谱x射线信息也是如此。我们提出MUSIC - andopen访问2D和3D的多光谱CT数据集 - 以促进材料识别领域的进一步研究。我们通过其组成材料的光谱响应,证明了该数据集对物体分割的图像分析挑战的价值。在这种情况下,我们比较了两个数据集上的快速自适应均值漂移(FAMS)和无约束图切割的分割精度。我们进一步讨论了重建人工制品和分割控制对可实现结果的影响。数据集,相关软件包和进一步的文档以开放获取的方式提供给成像社区,以促进对该主题的进一步数据驱动的研究
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We present a survey of techniques for the reduction of streaking artefacts caused by metallic objects in X-ray Computed Tomography (CT) images. A comprehensive review of the existing state-of-the-art Metal Artefact Reduction (MAR) techniques, drawn predominantly from the medical CT literature, is supported by an experimental comparison of twelve MAR techniques. The experimentation is grounded in an evaluation based on a standard scientific comparison protocol for MAR methods, using a software generated medical phantom image as well as a clinical CT scan. The experimentation is extended by considering novel applications of CT imagery consisting of metal objects in non-tissue surroundings acquired from the aviation security screening domain. We address the shortage of thorough performance analyses in the existing MAR literature by conducting a qualitative as well as quantitative comparative evaluation of the selected techniques. We find that the difficulty in generating accurate priors to be the predominant factor limiting the effectiveness of the state-of-the-art medical MAR techniques when applied to non-medical CT imagery. This study thus extends previous works by: comparing several state-of-the-art MAR techniques; considering both medical and non-medical applications and performing a thorough performance analysis, considering both image quality as well as computational demands.
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We present a theoretical overview and a performance evaluation of a novel z-sampling technique for multidetector row CT MDCT, relying on a periodic motion of the focal spot in the longitudinal direction z-flying focal spot to double the number of simultaneously acquired slices. The z-flying focal spot technique has been implemented in a recently introduced MDCT scanner. Using 32 0.6 mm collimation, this scanner acquires 64 overlapping 0.6 mm slices per rotation in its spiral helical mode of operation, with the goal of improved longitudinal resolution and reduction of spiral artifacts. The longitudinal sampling distance at isocenter is 0.3 mm. We discuss in detail the impact of the z-flying focal spot technique on image reconstruction. We present measurements of spiral slice sensitivity profiles SSPs and of longitudinal resolution, both in the isocenter and off-center. We evaluate the pitch dependence of the image noise measured in a centered 20 cm water phantom. To investigate spiral image quality we present images of an anthropomorphic thorax phantom and patient scans. The full width at half maximum FWHM of the spiral SSPs shows only minor variations as a function of the pitch, measured values differ by less than 0.15 mm from the nominal values 0.6, 0.75, 1, 1.5, and 2 mm. The measured FWHM of the smallest slice ranges between 0.66 and 0.68 mm at isocenter, except for pitch 0.55 0.72 mm. In a centered z-resolution phantom, bar patterns up to 15 lp/ cm can be visualized independent of the pitch, corresponding to 0.33 mm longitudinal resolution. 100 mm off-center, bar patterns up to 14 lp/ cm are visible, corresponding to an object size of 0.36 mm that can be resolved in the z direction. Image noise for constant effective mAs is almost independent of the pitch. Measured values show a variation of less than 7% as a function of the pitch, which demonstrates correct utilization of the applied radiation dose at any pitch. The product of image noise and square root of the slice width FWHM of the respective SSP is the same constant for all slices except 0.6 mm. For the thinnest slice, relative image noise is increased by 17%. Spiral windmill-type artifacts are effectively suppressed with the z-flying focal spot technique, which has the potential to maintain a low artifact level up to pitch 1.5, in this way increasing the maximum volume coverage speed that can be clinically used.
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Rapid non-destructive evaluation (NDE) of additively manufactured (AM) parts is critical to advancing our understanding of the impact of various process parameters and qualifying the final quality of the built parts. X-ray computed tomography (XCT) is an important non-destructive technique that can image parts in 3D at the micro-meter resolution. XCT involves taking X-ray images of the part from different orientations and computationally processing the images to obtain a 3D volumetric image. However, obtaining high-resolution 3D reconstructions is typically time consuming, as it requires a large number of images to be taken. This presents a fundamental roadblock to the adoption of the technology for rapid inspection of a large number of parts that may be produced on a given day. We have been developing novel algorithms [1-3], termed model-based iterative reconstruction (MBIR) techniques, based on Bayesian estimation techniques to obtain high quality 3D reconstructions for CT from significantly fewer and noisy measurements. We showed that compared to the standard methods such as the filtered back projection (FBP), MBIR can dramatically reduce the measurement time (by 32x) [2] and in turn accelerate the scan time of a single part. However, using MBIR for real-time high-quality reconstruction is challenging as, due to its iterative nature, it requires a significant amount of computation. In this paper, we present a deep learning algorithm to rapidly obtain high quality CT reconstructions for AM parts. In particular, we propose to use CAD models of the parts that are to be manufactured, introduce typical defects and simulate XCT measurements. These simulated measurements were processed using FBP (computationally simple but result in noisy images) and the MBIR technique. We then train a 2.5D deep convolutional neural network [4], deemed 2.5D Deep Learning MBIR (2.5D DL-MBIR), on these pairs of noisy and high-quality 3D volumes to learn a fast, non-linear mapping function. The 2.5D DL-MBIR reconstructs a 3D volume in a 2.5D scheme where each slice is reconstructed from multiple inputs slices of the FBP input. Given this trained system, we can take a small set of measurements on an actual part, process it using a combination of FBP followed by 2.5D DL-MBIR. Both steps can be rapidly performed using GPUs, resulting in a real-time algorithm that achieves the high-quality of MBIR as fast as standard techniques. Intuitively, since CAD models are typically available for parts to be manufactured, this provides a strong constraint "prior" which can be leveraged to improve the reconstruction. We voxelized a CAD model of an AM part into a volume of 512 slices of 256´256, and added random defects, mainly voids and holes of different sizes, to the volume. This is shown in Fig. 1a. We simulated a parallel beam XCT measurement with Poisson statistic, where we used 180 views, each correspond to one degree rotation. An example projection of the AM part is shown in Fig
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计算机断层扫描(CT)重建是从安全性到医疗保健等各种应用的基本组成部分。经典技术需要从对象的完整180 $ ^ \ circ $视图中测量投影,称为正弦图。当视角小于180°时,这在有限的角度范围内是不切实际的,这可能由于不同因素而发生,包括扫描时间的限制,扫描仪旋转的有限灵活性等。因此得到的正弦图导致现有技术产生高度神器重建的重建。在本文中,我们建议通过隐式正弦图完成来解决这个问题,这个问题包含一个包含普通签入式扫描扫描的具有挑战性的现实世界数据集。我们提出了一个由一维和二维卷积神经网络组成的系统,该系统在有限角度的正弦图上运行,直接产生重建的最佳估计。接下来,我们在这个重构上使用x射线变换来获得一个“完整的”正弦图,好像它来自一个完整的180 $ ^ \ circ $测量。我们将其提供给标准分析和迭代重构技术以获得最终重建。我们展示了经过深思熟虑的实验,这种组合策略优于许多竞争基线。我们还提出了对建筑的信任度,使从业者能够衡量我们网络的预测可靠性。我们表明,这一指标是PSNR衡量的一个强有力的质量指标,同时在测试时不需要基本事实。最后,通过分割实验,我们证明了我们的重建有效地保留了对象的三维结构。
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In x-ray computed tomography (CT), materials having different elemental compositions can be represented by identical pixel values on a CT image (ie, CT numbers), depending on the mass density of the material. Thus, the differentiation and classification of different tissue types and contrast agents can be extremely challenging. In dual-energy CT, an additional attenuation measurement is obtained with a second x-ray spectrum (ie, a second "energy"), allowing the differentiation of multiple materials. Alternatively, this allows quantification of the mass density of two or three materials in a mixture with known elemental composition. Recent advances in the use of energy-resolving, photon-counting detectors for CT imaging suggest the ability to acquire data in multiple energy bins, which is expected to further improve the signal-to-noise ratio for material-specific imaging. In this review, the underlying motivation and physical principles of dual-or multi-energy CT are reviewed and each of the current technical approaches is described. In addition, current and evolving clinical applications are introduced. q RSNA, 2015
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由于缺少标准参考数据集,在工业计算机断层扫描(CT)扫描中比较用于分割玻璃纤维的不同算法是困难的。在这项工作中,我们引入了一组短纤维增强聚合物(SFRP)的注释扫描以及综合创建的CTvolume数据以及评估指标。我们建议将度量和数据集作为研究不同算法性能的参考。真实的扫描是通过尼康MCT225 X射线CT系统获得的。模拟扫描是通过使用内部计算模型和第三方商业软件创建的。对于这两种类型的数据,已经准备了相应的groundtruth注释,包括realscans的手工注释和合成扫描的STL模型。此外,已经实现了一种用于光纤分割的基于Hessian的Frangi血管滤波器,并且开源以作为比较的参考。
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通过减少测量来加速磁共振成像(MRI)有可能降低医疗成本,最大限度地减少对患者的压力,并使MI在目前非常昂贵的应用中成为可能。我们介绍了fastMRI数据集,这是一个大规模的MR MR测量和临床MR图像集合,可用于训练评估MR图像重建的机器学习方法。通过引入标准化评估标准和可自由访问的数据集,我们的目标是帮助社区快速推进MR图像重建的最新技术。我们还为没有医学成像背景的机器学习研究人员提供了独立的MRI入门介绍。
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当测量过程具有潜在危害,需要快速或不经济时,需要进行稀疏测量的层析成像重建。在这种情况下,来自相同或类似对象的先前纵向扫描的先验信息有助于重建当前对象,同时显着减少“更新”测量。然而,所有基于先验的方法的显着限制是先前重建与测试对象一起演化的新的本地化信息的可能优势。在本文中,我们通过以下方式改进现有技术:(1)检测可能发生新变化的潜在区域;以及(2)通过计算区域权重来有效地重建新旧结构,以减轻先验的局部影响。我们已经证实了我们的方法在合成和实际体积数据方面的功效。结果表明,使用加权先验可以显着提高重建数据的整体质量,同时最大限度地降低对包含新信息的区域的影响。
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Computed tomography (CT) with x-rays or,/-rays has the potential of being a powerful tool in assessing the degree of damage in reinforced concrete structures. The traditional CT method consists of taking measurements from all directions around the structure. However in reinforced concrete structures the measurement device cannot be positioned around all the structure, and the measurement data are thus available only from a limited range of angles. In this paper a new algorithm is presented for reducing the effects of the limited angle problem. Computer simulations of limited angle tomography of reinforced concrete columns show that the proposed algorithm is robust and can significantly increase the power of tomography in civil engineering applications.
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